CH-46 Aft Main Transmission Fault Diagnosis—Final Report

Technical Document 2966 June 1997

Air Vehicle Diagnostic System: CH-46 Aft Main Transmission Fault Diagnosis—Final Report

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K. G. Church

R. R. Kolesar

M. E. Phillips

R. C. Garrido

Approved for public release; distribution is unlimited.

19970912 024

.fine QUALITY wsvEvmm' Naval Command, Control and Ocean Surveillance Center RDT&E Division, San Diego, CA 92152-5001

DTIC QUALITY INSPECTED 9

Technical Document 2966 June 1997

Air Vehicle Diagnostic System: CH-46 An Main Transmission Fault Diagnosis—Final Report

K. G. Church

R. R. Koiesar

M. E. Phillips

R. C. Garrido

V Naval Command, Control and Ocean Surveillance Center RDT&E Division, San Diego, CA 92152-5001

NAVAL COMMAND, CONTROL AND OCEAN SURVEILLANCE CENTER

RDT&E DIVISION San Diego, California 92152-5001

H. A. WILLIAMS, CAPT, USN R. c. KOLB Commanding Officer Executive Director

ADMINISTRATIVE INFORMATION

The work detailed in this report was performed for the Chief of Naval Operations by the Naval Command, Control and Ocean Surveillance Center RDT&E Division, Advanced Technology Development Branch, Code D374.

Released by Under authority of R. R. Kolesar, Head D. W. Murphy, Head Advanced Technology Advanced Systems Development Branch Division

SB

CONTENTS 1 INTRODUCTION 1

2 DATA 3

2.1 CH-46 Helicopter 3 2.2 TEST STAND DATA 3 2.3 INFLIGHT DATA COLLECTION 4 2.4 FLIGHT TRIALS DATA 4

3 SYSTEM DESCRIPTION 7

3.1 NEURAL NETWORK PROCESSOR (N3P) SYSTEM DESIGN 7 3.1.1 System Summary 7 3.1.2 Software Processing Flow 7

3.2 NEURAL NETWORK PROCESSOR HARDWARE 9 3.2.1 Digital Storage Devices 9 3.2.2 Neural Network Processor Cards 9

3.3 NEURAL NETWORK PROCESSOR/HUMS INTERFACE 10 3.3.1 General Operation 10 3.3.2 Regime Processor 11 3.3.3 Regime Processor to N3P Message Content 12 3.3.4 N3Pto Regime Processor Message Content 12 3.3.5 Regime Processor to MDAU 12

4 NEURAL NETWORK PROCESSOR LABORATORY BENCH TESTS 17

4.1 INTRODUCTION 17 4.1.1 Probability of False Alarm 17 4.1.2 Correction Condition Identification (CCI) 17 4.1.3 Correct Classification 18

4.2 DIGITAL PERFORMANCE TEST 18 4.2.1 Test Phase I - Baseline Digital Performance Test 18 4.2.2 Test Phase II - Digitally Degraded Sensor Environment Performance Test 19 4.2.3 Test Phase III - Digital DAFC Transition False Alarm Performance Test 22

4.3 DIGITAL PERFORMANCE TEST RESULTS 22

5 DETECTION OF A FAULT IN BU NO. 156432 29

5.1 USE OF IN-FLIGHT DATA 29 5.2 RADIAL BASIS FUNCTION NEURAL NETWORKS 29 5.3 AUGMENTING THE TRAINING OF THE RBF NETWORK USING IN-FLIGHT DATA..33 5.4 INDICATION OF AN ANOMALOUS CONDITION DURING CROSS VALIDATION 34 5.5 INVESTIGATION OF ANOMALY 35 5.6 NOTIFICATION OF NAVAL AVIATION SYSTEMS COMMAND (NAVAIR)

REGARDING THE ANOMALY 36 5.7 RESULTS OF THE ENGINEERING INSPECTION (El) 37 5.8 AUTO-ASSOCIATOR NEURAL NETWORK 38

in

6 NEURAL NETWORK PROCESSOR (N3P) FLIGHT TRIALS 43

6.1 INTRODUCTION 43 6.2 PROCESSING THE RESULTS OF THE TRIAL 45 6.3 PROCESSED FLIGHT TRIALS DATA 49 6.4 FLIGHT TRIALS CONCLUSIONS [".'.'.'.'.'.'.'.50

7.0 PROGRAM CONCLUSIONS 51

7.1 RECOMMENDATIONS 51

APPENDIX A A-1

Figures

1 N3P System in the VME chassis 7

2 AVDS block diagram 8

3 Regime Processor serial link signal flow diagram 11

4 Regime Processor to N3P message content 13

5 N3P to Regime Processor message content 14

6 Regime Processor to MDAU message content 15

7 Typical architecture of a RBF neural network. This network has a single hidden layer of RBFs fully interconnected to an output layer of linear units. Each output of an RBF node is multiplied by an interconnection weight that is determined by training. The weighted outputs of the RBF nodes are then summed at the corresponding linear unit. After summation, the output of the linear node is determined using a sigmoid transform to bound the output 30

8 Modified RBF in two-dimensional feature space formed using the modified logistic function

l.exp«. 1, where cp represents the bias added to the receptive field, CT

represents the width of the field, and \i represents the center of the field. Note that the function gives rise to a symmetric, localized, receptive field 31

9 Sigmoid transfer function that takes the sum of inputs to a linear output neuron and bounds the output response to [0,1]. Note that the input can vary from -00 to 00 in two dimensions, but the output will always be "squashed" to between zero and one. Hence the nickname, squashing function 26

IV

10 Typical Whisker plots showing the distribution of RBF Output Node Response: (a) typical no-defect data; (b) data from Bu No. 156432 at 27% torque; (c) data from Bu No. 156432 at 30% torque. The bars indicate the standard deviation of the distributions while the boxes contain 75% of the data with the mean of the distribution indicated by the horizontal bars within the box. For the typical no-defect response, only the first class of no-defect node is highly excited, with all other nodes showing a low, approximately constant, level of excitation. For the data from Bu No. 156432, all node responses are relatively low level, with the node having the highest response toggling as a function of torque 34

11 Trend in the SSE as a function of training epoch. The SSEs have been normalized by the maximum sum-squared error encountered during the training run. The SSE for the training data set is plotted as a solid line. The SSE for the three A/C in the cross-validation data set that did not have an anomalous response are shown as the lightly shaded lines while Bu No. 156432 is shown as the darkly shaded line 37

12 Damage to spiral bevel ring gear (a) general and (b) close-up. Damage occurs around the entire gear and across a significant portion of the mesh. Galling has worn through the black metal oxide coating and is close to cold metal working 39

13 Damage to spiral bevel input pinion gear (a) general and (b) close-up. The damage is symmetric to that of the mating spiral bevel ring gear. Again, the galling has worn through the black metal oxide coating and is close to cold metal working 39

14 Damage to starboard helical input pinion gear. Gouge or chip is localized to a single tooth near the edge of the mesh 39

15 Architecture of a typical AA network. This network has three layers that are fully interconnected. The hidden or middle layer is of a reduced number of nodes when compared to the input or output layer in order to encode the essence of the training class of data. The input and output layer have the same number of nodes. After training, the output of a feature vector is compared to the input and the RMS error is calculated between the two. The error is then compared to a threshold. If the error is greater than the threshold, the input is considered novel 40

16 Blind testing RMS prediction error vs. feature vector run for the trained AA network. The feature vectors from A/C Bu No. 156432 are in the upper portion of the graph while the feature vectors from the three other A/C in the cross-validation test set are in the graph's lower portion. The prediction error threshold is set at approximately 0.75 41

17 Front view of the VMEbus installation in AC 692. the VMEbus chassis containing the N3P is located in the lower portion of the equipment rack. The OQAR that logged the N3P status message is the rectangular black box shown in the upper left of the equipment rack 44

18 Side view of the VMEbus installation 45

19 Sample histogram from Flight N3SHA-6 showing distribution report indicators broken down into fields of good tranmission, anomalous- faulty transmission, no report at this time, and network confidence failures. During this flight, multiple sensor/wiring failures related to loose connectors and an accelerometer backing off its mounting bolt caused network confidence failures 45

20 Sample histogram from Flight N3SHA-6 showing confidence indicated in the report indicators (figure 19). In this case, the majority of the no- fault answers provided by the RBF networks had marginal confidence while all answers provided by the Auto-Associator and some of the answers by the RBF networks had a relatively low confidence 48

Tables 1 Transmission/mix box test stand data summary 4

2 Test stand torques 5

3 Test Stand torques and corresponding flight conditions 5

4 N3P power budget 10

5 Acceptable torque levels for valid Data Acquisition Flight Conditions 11

6 Phase I digital baseline performance test 20

7 Phase II digitally degraded sensor environment test 21

8 Phase III digital DAFC transition false alarm test 24

9 Baseline digital performance test results by data record 25

10 Digitally degraded sensor environment performance test by data record 26

11 Digital DAFC transition false alarm performance test by data frame 27

12 Report indicator breakdown 46

13 DAFC breakdown 47

14 Report indicators broken down by flight. Flight N3SHA6-8 was particulary plagued with multiple sensor failures including bad microdot connections, accelerometers backing off mounts, problematic patch panel connections, intermittent signal saturation, etc.49

VI

1. INTRODUCTION The goal of the helicopter drive system condition-monitoring component of the Air Vehicle Diagnos-

tic System (AVDS) program is to develop technology mat will facilitate transition to "condition-based maintenance" (CBM) for these components. The specific system requirements as stated in the AVDS Execution Plan are as follows:

• Detect and classify gearbox faults in real time in-flight.

• Cope with variations between gearboxes.

• Perform diagnostics in a variety of flight regimes.

• Reduce false-alarm rates.

• Reduce maintenance costs and facilitate transition to CBM.

This report describes the performance of a system developed by NRaD that meets these requirements.

2 DATA

2.1 CH-46 HELICOPTER

The focus of this project, because of its age and cost of operation, was on the CH—46 helicopter. The power train consists of two turbines that drive the forward and aft transmissions and subsequent rotors. Engine outputs are combined in a mix box. The power train is a constant- speed, variable- torque system. The effective torque varies as a function of the load on the rotor blades. Operation- ally, the aft-main transmission and mix box assembly have been the most troublesome. Because of these difficulties, this assembly has also been the subject of significant false removals, that is, prema- ture aircraft removal and rebuild (NADEP Cherry Point estimates 30% of rebuilds are on aft-main transmission/mix box assemblies that are defect-free).

Instrumentation consisted of eight accelerometers and an optical tachometer. The accelerometers are uni-axial and attached to modified case nuts symmetrically positioned about the center-line of the assembly. The optical tachometer was substituted for the rotor position indicator. The tachometer produces 256 pulses per shaft revolution. The rotor position indicator shaft rotates at a nominal 114.4 Hz that dictates a data sampling rate of at least 58.5 kHz.

2.2 TEST STAND DATA The data set consists of vibration data recorded from the aft transmission and mix box installed on a laboratory test stand. The data collection utilized a single transmission and mix box. Data were collected for two defect-free reassemblies of the mechanical system. Additionally, eight different faulted components were installed one at a time. Only one faulted component was present in the assembly for any given test. Data were recorded at nine torque levels on an International Recording Instruments Group B (IRIG-B) 28-channel tape recorder. The data were then transcribed into the digital domain using high-order, linear-phase anti-aliasing filters and a multi-channel, simultaneous sampling/digitization system. The following faulted components were installed and vibration data collected:

Planetary Gear (Corrosion Spalling)*

Spiral Bevel Input Pinion Bearing (Corrosion)*

Spiral Bevel Input Pinion Gear (Tooth Spalling)*

Helical Input Pinion Gear (Chipping)*

Clutch Bearing (Brinelling)*

Helical Idler Gear (Crack Propagation)

Collector Gear (Crack Propagation)

Quill Shaft (Crack Propagation)

* Component fault naturally occurring. Two components at different defect levels utilized. Components provided by NADEP Cherry Point.

Table 1 is a comprehensive list of transmission/mix box data that are known to have at least one 20- second, repeatably locatable (via time-code), contiguous block during which all nine channels are good (eight accelerometer and one tachometer). The data blocks have been examined for quality assurance using both automated and manual techniques. In order to locate such data blocks it was necessary to scroll through the entire analog recording cycle that corresponded to a valid torque level and for which ground-truth was known, using overlapping data blocks. In cases where no data were indicated, an error-free block of 20 seconds could not be found in the original analog data record.

Table 1. Transmission/mix box test stand data summary.

27%T 30%T 40%T 45%T 50%T 55%T 60%T 70%T 75%T 80%T 100%T LEVEL

No Defect, First Test X N/A

Planetary Pinion Bearing Corrosion X 2

S.B. Input Pinion Bearing Corrosion X X X X X X X X X 1.2

S.B. Input Pinion Gear Scuffing X X X X X X X X X 1.2

M.B. Input Pinion Gear Chipping X X X X 2

M.B. Idler Gear Crack X X X X N/A

Collector Gear Crack X X X X X X X X N/A

Quill Shaft Crack X X X X X X X X X N/A

S.B. Assembly Galling X X X X X X X X N/A

No Defect, Second Test X X X X X X X X X N/A

X — 20 seconds of good vibration data

2.3 IN-FLIGHT DATA COLLECTION Vibration data were collected from nine deployed Marine Corps CH-46E aircraft. The eight accelerometers, locations, modified case nut mounts, and tachometer were the same as those used in the test stand data collection. A 14-channel IRIG-B tape recorder was used to acquire the data To observe the effects of naturally occurring wear on the transmission/mix box, two data collections were made on each aircraft at widely separated intervals (approximately 6 to 10 months apart). Since the aircraft were deployed, the assumption was that the data represented systems in good working condition with no defects.

To expand the utility of the test stand data to in-flight conditions, it was necessary to acquire the in- flight data in flight conditions that approximated those of the test stand. For these data collections, pi- lots flew the aircraft at a constant torque and tried to maintain straight and level flight. The achieved flight conditions, corresponding to the nine test stand torques shown in table 2. Table 3 lists in-flight transmission/mix box data that are known to have at least one 20-second, contiguous block during which all nine data channels are functioning.

2.4 FLIGHT TRIALS DATA

Flight trials of the NRaD-developed, real-time, in-flight data collection and diagnostic system were conducted on a single CH-46E aircraft at NAWC Patuxent River, MD. The accelerometers, locations, modified case nut mounts, and tachometer were the same as previously used. For the diagnostic system

Table 2. Test stand torques.

Torque Level (%) Flight Condition

27 Below light on wheel

30 750 ft/min descent

40 80 knots air speed (KNAS)

45 60 KNAS

50 light on wheels

60 100 KNAS

70 Hover, in-ground effect

75 Hover, out-of-ground effect

80 120 KNAS

100 140 KNAS VNE

Table 3. Phase II Transmission and i nix box in-flight data (all no fault). 27%T 30%T 40%T 45%T 50%T 55%T 60%T 70%T 75%T 80%T 100%T

BuNo 156457 A B X X X X X X X X X X X

BuNo 156420 A B X X X X X X X X

BuNo 157673 A B X X X X X X X X X X

BuNo 153395 A B X X X X X X X X X X

BuNo 153960 A B X X X X X X X X

BuNo 156432 A X B X X X X X X X X X

BuNo 154829 A X X X X B X X X X X X X X X X X

BuNo 154815 A X X X X X X X X X X X B

X — 20 seconds of good vibration data

to operate autonomously, an automatic flight regime recognition capability was required. This permit- ted the pilot to fly the aircraft without the constraints imposed during the in-flight data collections. The system would automatically acquire data when the aircraft was in a valid Data Acquisition Flight Condition (DAFC), previously defined in table 2. Teledyne Controls had the task of developing this capability and integrating it into the in-flight system. Teledyne also manufactured the Health and Usage Monitoring System (HUMS) that was evaluated during these flight trials.

The flight trials consisted of 18 dedicated flights and one non-dedicated flight. The data collection system operated during 17 of the 19 flights and collected a total of 22.37 hours of transmission/mix box vibration data.

3 SYSTEM DESCRIPTION

3.1 NEURAL NETWORK PROCESSOR (N3P) SYSTEM DESIGN

3.1.1 System Summary

The N3P system processes, in near real time, transmission/mix box data from CH-46 helicopters and evaluates the mechanical condition of the mechanical system. The in-flight N3P system consists of a VME chassis with the capability to digitize 10 analog data channels at a nominal sample rate of 117 kHz, process the data, and determine the transmission condition approximately every 3.1 seconds. The system also contains a data recording capability that saves all data, results, and communications to 8-mm digital tapes for post-flight analysis.

PLLRESET

Tachometer

Accd 1-8

TDY1 TDY2

Phase Lock Loop lOOFfeCsc.

Anti-Alias Filters

AlDOxHd

AID 1-10

1SBP N3P/KBGÊ SERTALOCMVti

Figure 1. N3P System in the VME chassis.

3.1.2 Software Processing Flow

The N3P system has 10 processors that perform the various system functions. Analog data are received by the analog-to-digital (A/D) subsystem digitized synchronously with the transmission tachometer through a NRaD-designed Phase-Lock-Loop (PLL); see figure 1. The resultant digital time-series data are retrieved by the Data Distribution (DD) subsystem. The data are sent to the Quality Assurance (QA) and Feature Extraction (FE) subsystem. The Feature Vectors (outputs from the Feature Extraction) are sent to both a set of Radial Basis Function Neural Networks and a set of Auto-Associator Neural Networks, provided the time-series data pass the QA tests. Once the Neural Network outputs are generated, they are fused together. The fused RBF and AA results are then rated for acceptability. If acceptable they are averaged and an answer code is generated. The answer code is then assembled into the N3P output message, providing that valid Data Acquisition Flight Condi- tion (DFAC) exists (see section 3.3). Figure 2 is the system block diagram.

VMEBUS

A/D Control

/

A/D SUBSYSTEM

A \ /

A

DATA DISTRIBUTION

7K

A A

QUALITY ASSURANCE

FEATURE EXTRACTION

A A M NEURAL

NETWORKS

VSBBUS

M

/

\

A

Figure 2. AVDS block diagram.

MEMORY

A V

COMMUNICATIONS

PHASE LOCK LOOP CONTROL

DATA STORAGE

N3P/REGIME SERIAL COMM

PLL RESET

A V

n

EXECUTIVE CONTROLLER

3.1.2.1 Executive Controller (HOST). The N3P process starts with the Executive Controller (HOST) execution. The Executive Controller does the bulk of the decision making on how the N3P processing is performed. The HOST also provides all the system services that the N3P requires to load and execute all of the program threads. After all the programs are loaded and executing, the HOST signals the DD subsystem to begin data distribution and the A/D subsystem to begin data acquisition. Once this is done, the HOST waits for results to arrive. The HOST only communicates over the VME subsystem bus to the other N3P processes (with the exception of the I/O Support Sub- system that can receive a reset over a serial interface and control information over the Ethernet).

When signaled that the QA/FE data products are available, the HOST checks the QA communication packet for errors. If no fatal errors are present, the HOST transfers the Feature Vector to the NN Subsystem for processing. The system design achieves fault tolerance through the "banks" of neural networks. If a single channel fault occurs, the HOST determines the transmission condition from the network within the bank that contains valid results. Otherwise, all network outputs are fused to determine the transmission state. The QA/FE results are always sent to the Memory Subsystem and bundled with the data frame sent by the DD.

When signaled that the NN processing is completed, the HOST retrieves the results from all the separate neural networks and fuses them into a single result word. The NN results are transferred to the Memory Subsystem. An answer code is also written to the Memory Status Section for transmission via the Communications process of the I/O Support subsystem.

3.1.2.2 Data Distribution (DD). After initialization, the DD Subsystem monitors the A/D Subsys- tem for the data ready signal. Data ready triggers the process to begin assembling data and de- multiplexing it from the raw stream format onto 64K sample blocks. Once the data frame (64K samples x 10 channels) is completed, it is sent to the QA/FE Subsystem and the Memory Subsystem. The QA/FE Subsystem is notified that a new data frame is available and the DD starts assembly of the next data frame. Dual buffers (A/B) are employed throughout to increase system throughput.

3.1.2.3 Quality Assurance/Feature Extraction (QA/FE). The QA/FE Subsystem waits for the data ready signal from the DD then processes the data frame to determine that the data is of sufficient quality for NN processing (no dropouts, saturation, etc.) After quality checks, the feature set is

generated and the HOST is signaled that processing is complete and data products are available. The QA/FE Subsystem process then waits for another data frame ready and the processing repeats.

3.1.2.4 Neural Networks (NN). The NN Subsystem, after receiving the signal that a new Feature Vector has arrived, processes the features through both a Radial Basis Function neural network system and an Auto-Association neural network system. Each network system consists of a "bank" of eight neural networks that provide for fault tolerance. By eliminating a single channel from each of the individual networks, any one channel can be faulty and there is still a single network running that can determine the transmission state. After processing is complete, the HOST is signaled and the NN process waits for another Feature Vector to arrive.

3.1.2.5 I/O Support. The I/O Support Subsystem consists of three basic processes: the Communica- tions process, the Phase Lock Loop Control process, and the Data Storage process, which provide the critical functions of external interface support and timing.

The Communication process receives and sends messages over a RS-232 interface. It also calculates and maintains the Data Acquisition Flight Condition (DFAC) Status and queues for N3P An- swer processing. These processes supply the HOST generated answer under "valid" DFAC conditions and NO-ANSWER, otherwise.

The Phase Lock Loop (PLL) Control process responds to analog data acquisition and QA errors and can reset the external PLL hardware over a digital line. It also has the responsibility of maintaining the socket (Ethernet) interface with the HOST used for I/O Support Subsystem control.

The Data Storage process waits for the HOST to indicate that a complete data packet (data frame, QA/FE results and NN results) is ready and then records the data packet to a bank of three Exabyte 8505 8-mm digital data recorders.

3.2 NEURAL NETWORK PROCESSOR HARDWARE

The VME enclosure contains circuit cards that perform the following functions: 1) interface and signal conditioning for eight accelerometers and an optical tachometer; 2) flight regime recognition and RS-232 communications between HUMS and the N3P; and 3) the Neural Network Processor (N3P). Software, developed under this program, is implemented in the N3P hardware.

The in-flight VME enclosure is a ruggedized and electromagnetic interference (EMI)-shielded ELMA chassis. It has a 14-slot backplane and a six-slot VSB overlay. Table 4 shows a circuit card power budget.

3.2.1 DIGITAL STORAGE DEVICES

The chassis has a Kingston Technology 1GB hard disk. Three 8-mm Exabyte tape drives provide archival data storage of eight accelerometer and optical tachometer signals and eight inter- mediate system processing results.

3.2.2 NEURAL NETWORK PROCESSOR CARDS

The following 6U size VME circuit cards constitute the N3P system:

(1) Force 2CE Single Board Computer

(1) Motorola MVME 166 with MVME 7 12-006 Transition module

(2) SKY bolt 8146-V

(1) Micro Memory 256MB memory

(1) ICS-110A Digitizer

Table 4. N3P power budget.

Device +5 Volts +12Volts -12 Volts

1GB hard disk (EST). 2.55 2.50

Three EXABYTE 8505XL tape drives

5.40 2.25

Slot 1-FORCE 2CE 4.00 1.60 0.20

Slot 2-MVME 166 6.50 1.00 0.10

Slot 3-MVME712-006 0.28 0.10 0.15

Slot4-MVME712-007 0.28 0.10 0.15

Slot7-SKYbolt8146-V 8.40

Slot 8-SKYbolt8146-V 6.40

Slot 10-256Mbyte Memory 7.75

Slot11-ICS-110AA/D 3.00 0.20 0.20

Slot12-Spare

Sum of currents 44.56 7.75 0.80

Watts 222.80 93.00 9.60

Total Watts 325.40

3.3 NEURAL NETWORK PROCESSOR/HUMS INTERFACE

3.3.1 General Operation

The HUMS MDAU communicates with the N3P via a Matrix I/O board in the VME chassis. The board provides three serial ports and processes aircraft flight data to determine valid Data Acquisition Flight Conditions (DAFC) for the N3P. A valid DAFC is achieved when the aircraft is in steady-state flight and is at one of the nine torque levels utilized in the transmission/mix box data collection phase of the project (discussed in section 2). Acceptable torque levels are shown in table 5. It is important to note that the only flight data available to the N3P are whether the aircraft is in a valid DAFC or not. The aircraft could be on the ground (27% torque) or at 140 knots (100% torque) and still be in a valid DAFC.

10

Table 5. Acceptable torque levels for valid Data Acquisiton Flight Conditions (DAFC).

Torque Level (%) Flight Condition Comments

27 Below light on wheel Normally transient

30 750 ft/min descent

40 80 knots air speed (KNAS) Losing altitude

45 60KNAS Maintaining altitude

50 Light on wheels

60 100 KNAS

70 Hover, in-ground effect

75 Hover, out-of-ground effect

80 120 KNAS

100 140 KNAS VNE Velocity not to exceed

3.3.2 Regime Processor

The Regime Processor communicates with the DMAU and the N3P via a serial asynchronous full- duplex communication channel (figure 3). Each of the three processors (MDAU, Regime Processor, and N3P) transmit unsolicited messages at a uniform rate. The receiving processor in each case determines the start of each message by recognizing the inter-message quiet time before each message. No handshaking messages or control signals are employed.

VME CHASSIS

IftJKbp* IMsgfSac

12Mbps 1ltagfS«c

REGIME PROCESSOR

c

ItJKbpa 4MaglSac

1UKbpt

JZI FSO RTN

J8 P8

Figure 3. Regime Processor serial link signal flow diagram.

11

The regime processor analyzes flight data associated with each 0.25-second interval and determines if the aircraft is in a valid DAFC for that interval. Determination of valid DAFCs is in accordance with a predefined set of flight parameters.

3.3.3 Regime Processor to N3P Message Content

Each 0.25 seconds the Regime Processor sends a message (figure 4) to the N3P. This message contains the current Regime Processor status and the most recently determined DAFC. The date and time values are derived from the MDAU internal time-of-day clock. The values sent to the N3P are adjusted for transit time, so that the N3P stores a consistent time with minimal processing. The values in each message field are formatted in hexadecimal.

3.3.4 N3P to Regime Processor Message Content

The N3P sends an event message to the regime processor once each second. This message (figure 5) contains the current status and the present condition of the transmission/mix box assembly. The values in each message field are hexadecimal.

3.3.5 Regime Processor to MDAU

The content of the four most-recent N3P event messages and the current regime are sent to the MDAU once each second for recording on the Optical Quick Access Recorder (OQAR). Figure 6 shows the message format.

12

BYTE# CONTENT

1 Status

2 Spare

3 DAFC

4 Aircraft Type

5 Aircraft Tail ID

6 Message Year

7 Message Month

8 Message Day of Month

9 Message Hour

10 Message Minutes

11 Message Seconds

12 Checksum

Figure 4. Regime Processor to N3P message content.

13

BYTE# CONTENT

1 Status

2 Spare

3-4 Fault

5 Message Hour

6 Message Minute

7 Message Second

8 Message Year

9 Message Month

10 Message Day of Month

11 Event Hour

12 Event Minute

13 Event Second

14 Checksum

11 Event Hour

12 Event Minute

13 Event Second

14 Checksum

Figure 5. N3P to Regime Processor message content.

14

BYTE 1 BYTE 2 BYTE 3 BYTE 4 BYTE 5 BYTE 6 1 2 3 4 J5 6 ' 8 1 ; 3 X 5 (. 7 | s 1 2 3 <j 5 5 7 8 12 3 4 5 6 7 8 1 2J 3 1 5 £ 7 S 1 2 (3 4 : 6 7 8

S1ATUS *AULT M.HOJjks M M1IS M.SEC M.YEAK

BY1 rE 7 BYTE 8 BYTE 9 BYTE 10 1 2 3 4 5 6 7 8 1 3 X 5 e 7 8 1 2 3 ^ 5 5 7 8 1 2 4 5 6 7 8 LV I.MUN M.JUUM E.HOUR E.M1N E.SEC DA*C 1

BYT E 11 BYTE 12 BYTE 13 BYTE 14 BYTE 15 1 2 3J 4 5 6 " 8 1 ^ 3 X 5 ( 1 8 1 2 3 * 5 5 7 8 1 2 4 5 6 " 8 1 3 1 5 (. 1 S

DAFC2 DAFC3 DAFC4 FRAME COUNT 00 CKSUM

Legend: STATUS STATUS 08 bits E. HOUR EVENT HOUR 05 bits FAULT FAULT 16 bits ■ E. MIN EVENT MINUTE 06 bits

M.HOURS MESSAGE HOURS 05 bits E. SEC EVENT SECOND 06 bits

M. MIN MESSAGE MINUTE 06 bits DAFC 1 Fourth Most Recent Calculated DAFC 06 bits

M SEC MESSAGE SECOND 06 bits DAFC2 Second Most Recent Calculated DAFC 06 bits M.YEAR MESSAGE YEAR 07 bits DAFC 3 Third Most Recent Calculated DAFC 06 bits

M. MON MESSAGE MONTH 04 bits DAFC 4 Most Resent Calculated DAFC 06 bits

M. DOM MESSAGE DAY OF MONTH 05 bits 00 Padding for byte alignment 02 bits CKSUM CHECKSUM 08 bits

Figure 6. Regime Processor to MDAU message content.

15

4 NEURAL NETWORK PROCESSOR LABORATORY BENCH TESTS

4.1 INTRODUCTION

The objectives of these tests was to quantify the system performance and determine if the requirements stated in the AVDS execution plan are being met (discussed in section 1). To quantify the system performance, the following metrics are defined:

1. Probability of False Alarm (PFA) - the number of times an alarm condition was issued when the underlying ground-truth indicated that a no defect condition existed.

2. Correct Condition Identification (CCI) - correct alarm condition (alarm/no alarm) based on the ground-truth of the data.

3. Correct Classification (CC) - probability of correct classification given that an alarm condition existed according to the ground-truth of the data.

4.1.1 Probability of False Alarm

The PFA metric quantifies the system's potential for false alarm. A high PFA indicates that the system has a tendency to false alarm. A low PFA indicates that insofar as the limitations of the testing procedures allow, the system appears to be robust and not subject to significant concerns regarding false-alarm rates.

4.1.2 Correction Condition Identification (CCI)

The CCI metric quantifies the system's performance in correctly issuing an alarm or no alarm condition.

4.1.2.1 CCIi. A high CCIi indicates that the system performs well overall in correctly identifying the condition (alarm/no alarm) of the transmission. A low CCIi indicates that overall the system did not perform well in correctly identifying the condition of the transmission. Poor performance of the CCIi metric can be attributed to four factors: false positives (false alarm), false negatives, no reports (including those caused by poor data quality), and system failures.

4.1.2.2 CCI2. A high CCI2 indicates that when a condition could be determined for the transmission, the system performed well in correctly identifying this condition. A low CCI2 indicates that specifically when condition reports were issued, the system did not perform well.

4.1.2.3 CCIi and CCI2 Together. A low CCIi with a high CCI2 indicates that the system performs well when a condition could be identified, but the system is plagued by no reports and/or system failures. Most likely, the no reports would be due to low neural network confidence, DAFC transition instabilities, and poor data quality.

A low CCIi with a low CCI2 indicates that the system performed poorly in identifying the condition of the transmission mix box. This also indicates that the system made a substantial number of false positive or false negative determinations. If the PFA is high, the system made a significant number of false positive determinations. Otherwise, the system made a significant number of false negative determinations.

17

4.1.3 Correct Classification

The CC metric quantifies the system's performance in correctly classifying the transmission's condition to one of a set of predetermined states (currently eight defect states and one no-defect state).

4.1.3.1 CC!. A high CCi indicates that the system performs well overall in correctly classifying the health of the transmission to a predetermined state. A low CCi indicates that overall the system did not perform well in correctly classifying the status of the transmission. Poor performance of the CCi metric can be attributed to five factors: misclassification, novel conditions that do not conform to one of the predetermined states, poor data quality, no reports, and system failures.

4.1.3.2 CC2. A high CC2 indicates that when a condition could be determined for the transmission, the system performed well in further correctly classifying this condition to one of a somewhat narrow set of defects or no defect. A low CC2 indicates that specifically when condition reports were issued, the system did not perform well in further resolving the status of the transmission.

4.1.3.3 CCi and CC2 Together. A low CCi with a high CC2 indicates that the system classifies well when a condition could be identified, but the system is plagued by poor data quality, novel conditions, DAFC transition instabilities, or system failures.

A low CCi with a low CC2 indicates that the system performed poorly in classifying the status of the transmission. This also indicates that the system made a substantial number of misclassifications. If the PFA is high, the system made a significant number of false alarm misclassifications. If the PFA is midrange to low, the system made a significant number of misclassifications across the set of classes.

4.2 DIGITAL PERFORMANCE TEST

Digital performance tests were carried out using test stand data only (section 2.2) stored on optical read-write media. The data records are each approximately 20 seconds long and consist of continuous digitally sampled data in, by definition, a valid Data Acquisition Flight Condition (DAFC). The N3P system requires 0.588 seconds of data to make a call. Therefore, 34 calls should be made over a 20-second period1. These data have previously passed both automated and manual quality assurance checks and is not known to contain any sensor failures or artifacts.

4.2.1 Test Phase I - Baseline Digital Performance Test

4.2.1.1 Test Format. System performance for this phase of testing is qualified using the ground-truth of the data. The data records to be used for this phase of testing and the sequence of testing are listed in table 6. Note that the data records corresponding to no-fault data represent approximately 30% of the total data with the remaining 70% of data comprised of fault data Also note that each subset of data is represented by a complete set of DAFCs (data availability permitting).

Specifically, 34 calls would be made if the system had been up and running long enough to stabilize, the DAFC had been constant long enough to have been judged a stable DAFC and enough prior data had been accumulated to fill the integrator, etc.

18

4.2.1.2 Test Methodology. The purpose of Phase I Testing was to quantify the baseline digital performance of the N3P System. Phase I Testing will be based upon the correct issuance of an alarm or no alarm condition. An alarm condition is further broken down to reflect correct classification statistics. In addition, false alarm results are also be collected. For the purposes of Phase I Testing, each data frame, regardless of origin, has an equal weight in determining the overall statistics of the test. Each 20-second data record is played into the N3P System and the results will be tabulated in order to quantify the performance of the system.

4.2.1.3 Test Criteria. At the highest level, each data frame falls into one of two classes: (1) the class for which an alarm condition should be generated (defect) and (2) the class for which no alarm condition should be generated (no defect). Each data frame processed by the N3P System is graded to determine the performance of the system in determining the correct condition (alarm/no alarm) based on the ground-truth of the data. The output of this portion of Phase I Testing is the Correct Condition Identification (CO) statistic. The CCI value is determined by dividing the number of data frames for which a correct answer was issued by the total number of data frames. This metric is calculated using two different sets of data. The first, CCIi, measures all data frames and all N3P outputs. The second, CCI2, is limited to those data frames for which the N3P system reported a condition. Conse- quently, a no condition report is considered an error by the CCIi metric but it is ignored by the CCI2 metric. The difference between the two CCI values will quantify the effect of startup and transient on the system's stability. Each data record will be "cold-started2" into the N3P system to enhance repeatability.

In addition, the probability of correct classified (CC) and the probability of false alarm (PFA) are determined. The CC metric is the probability of correct classification given an existing alarm condition according to the ground-truth of the data. As before, the CC metric will be separated into two separate statistics. The first, CCi, measures all data frames and all N3P outputs while the second, CC2, measures only those frames for which the N3P system reported a condition. Again, the difference between the two CCs will quantify the effect of startup and transients on the system's stability.

The probability of false alarm is calculated using all of the no-defect data for which a condition report was issued. It is determined by dividing the number of data frames for which an alarm condition was issued when the underlying ground-truth indicated that no defect condition existed by the total number of data frames for which the N3P system issued a response. Note that this measure excludes those data frames for which no report was issued by the N3P System.

4.2.2 Test Phase II - Digitally Degraded Sensor Environment Performance Test

4.2.2.1 Data Format. System performance for this phase of testing is quantified using the ground truth of the data. In order to simulate a degraded sensor environment, a single channel of each record is digitally zeroed in software. This simulates the loss of signal associated with a channel. The data records to be used for this phase of testing, the sequence of testing, and the data channel to be zeroed for each record are listed in table 7. Note that the data records comprise the same basic subset of data used during Phase I Testing.

2i.e., the N3P System software is re-initialized between each data record.

19

Table 6. Phase I digital baseline performance test.

iest# lape# I orque "' 100

Used •F5T- 1 rain

""27" lest ~45" Test "SO" j I ram

: I ram

"TO-

"TOTr

iest

rest

~so~ "TesT

"22" "~22~

TT "W

lest ". I ram

~2T ""22"

~S5" "5Ü"

lest lest

"22" "70" " lest

~22" "50" * Test

"23" TesT

"T0" ~23~ ""23"

rest ~?5" ~~5G~

"T5" "70"

lest

lest

"TT ~T5" "T5"

~zr ""24" ~2T" "srr

"35" "70" "70" "70" "7Ü" ""37"

~75~ TOO"

" rest lest

~str i I rain

Test ""SO"-

"TOO" . lest Test

"TO" "50"

11 ram

"TOO" "~5u~

; I ram i I rain

Test 100 TesT

~40~ TesT "50" TesT "70" lest ~30~

"TOO" lest lest

Detect Level TDefect

~T5~ "X5" "TT"

"TT5" "7T5" "TT" ~3T" 1T "T"~

"7T~ "TO" ~T0" "T0~ "TC"

; Planet Corrosion jbB bearing Corrosion

"(SB Beanng corrosion I SB Beanng Corrosion iSB Beanng corrosion |SB Beanng corrosion iSB Beanng Corrosion tab Beanng corrosion !SB beanng Corrosion JOB üpalling ISB SpailTng"

iüBöpalling "SB Spailing

üB Spallmg i>B üpalling

öB üpalling \üti Spatling" üb üpalling

Port Input Pinion Chip jport input Pinion ctiip [Port input Pinion Chip"

iPoainput Pinion ctiip j Port Idler bear Crack

j port idler üear Crack I Port Idler Gear Crack

a j Pon idler üear crack ~S ;MB collector Crack

I None

None "üuill Crack IUUIII crack juuni cracTT juuiii crack iNone

"NRäü Number

MB1.099.10ir :03.A1.140.027 jg37A1.i26.04~5" "Ö3.A1.122.027"

-"03TA1.138.0Sff "03.A1.146.040

■03.A1.149.1Ö0" TO.b1.156.050

"04.A1.426.027" ;ü4.A1.422.0477

IU4.A1.421.045

'04.A1.41S.050 Ü4.A1.410.070

"TÜ4.A1.424.050" :04.A2.432.040 IÜ4.A2.431.Ö45 Ü4.A1.433.050'

i06.B1.294.070 Tub: B1.295.075 "UJ5.B1.291.100 JÜ6.B1.300.Ö80

70Ö.A1.404.08Ö ""0STA1.405.100 :ue.A1.440.070

~"ut>.A1. 445.080 :0/.A2.495.100 i0O.A2.678.0S0

701TÄ1T579.10Ö "0S.A1.280.040 •037T28"21!"5Ö"

";ü8.A1.284.070" ;08.A1.286.080

""3ö~ATT27Tfö"0"

20

Table 7. Phase II digitally degraded sensor environment test.

lest* lape# I Torque Used . For ; Levei i Detect j to Remove NRaD Numoer 2 5 ; ioo Train 3 ! Planet Corrosion 5 02. B 1.099.100 3 7 27 Test j 2 jSB Bearing corrosion 3 03.A1.140.027 3 7 i 45 Test i 2 | SB Bearing (Jorrosion 4 Ö3.A1.126.045 3 7 ! 50 Train i 2 | SB Bearing corrosion 7 03.A1.122.027 3 7 j 80 Tram ! 2 jSB Bearing corrosion 4 03.A1.133.080 3 8 j 40 Test ! 2 ,SB Beanng Corrosion 3 03.A1.14S.Ö4Ö 3 8 ! 70 Test I 2 ;SB Beanng Corrosion 4 • 03.B1.161.070 3 3 ! 100 rest 2 ;SB Beanng corrosion 4 03.A1.149.100 3 8 60 Train ! 2 | SB Bearing Corrosion / 03.B1.156.060 4 22 ! 27 Test ! 4.5 SB Spalling 7 04.A 1.426.027 4 22 ! 40 Test ! 4.5 [SB Spalling 4 04.A1.422.040 4 22 I 45 rest i 4.5 I SB Spalling 4 04.A1.421.045 4 22 j 50 Test I 4.5 SB Spalling 4 04.A1.418.050 4 22 j 70 Test i 4.S SB Spalling 7 04.A1.410.070 4 22 | 80 Test ! -4.5 SB Spalling 3 04.A1.424.080 4 23 | 40 Test 4.5 SB Spalling / Ö4.A2.432.Ö4Ö 4 23 ! 45 Test 4.5 SB Spalling 3 04.A2.431.045 4 23 ! 60 Test 4.5 SB Spalling 3 Ö4.A1.433.06Ö 5 15 ! 70 lest 5 Fort Input Pinion Chip 4 .Ö5.B1.294.070 5 15 75 Test 5 Port Input Pinion Chip 1 .05.51.295.075 5 15 100 irain 5 Port Input Pinion Chip 2 05.B1.291.100 0 16 80 rest 5 Port Input Pinion Chip 1 ,ö5.BUÖÖ.ÖäQ 6 21 80 Test 4 Port Idler Gear Crack 4 06.A1.404.080 6 21 100 Test 4 Port Idler Gear Crack 4 :0S.A1.405.100 6 24 70 I rain 9 Port Idler Gear Crack 2 06.A1.440.070 6 24 80 Train S Port Idler Gear Crack 2 :Ö6.A1.445.0SÖ 7 80 100 Train 5 MB Collector Crack 4 07.A2.49S. 100 8 35 80 Test 0 None ] 4 Ö0.A2.67S.ÖSÖ 8 35 100 Test o None | 7 iOO.Al.67S.100 8 70 40 Test 10 | Quill Crack 3 08.A1.280.040 8 70 50 Test 10 | Quill Crack 4 Ö8.A1.282.Ü5Ö 8 70 70 Test ! 10 i Quill Crack 7 08.A1.284.070 8 70 80 lest 10 | Uuill Crack 7 08.A1.286.080 9 37 •töö Test I 0 ! None | 4 ÖÖ.A4.727.100

21

4.2.2.2 Test Methodology. The purpose of Phase II Testing is to quantify the performance and robustness of the N3P System in the presence of a degraded signal environment. Phase II Testing will be based upon the correct issuance of an alarm or no-alarm condition. An alarm condition will be further broken down to reflect correct classification statistics. In addition, false alarm results are also collected. For the purpose of Phase II Testing, each data frame, regardless of origin, will have equal weight in determining the overall statistics of the test. Each 20-second data record is played into the N3P System and the results will be tabulated in order to quantify the performance of the system. As in Phase I, each data record will be cold-started into the N3P System.

4.2.2.3 Test Criteria. The same criteria used in Phase I of the testing is used to determine system performance during Phase II of the testing: CCI, PFA, and CC.

4.2.3 Test Phase III - Digital DAFC Transition False Alarm Performance Test

4.2.3.1 Data Format. System performance for this phase of testing is quantified using the ground- truth of the data. In order to simulate rapid transitions among DAFCs, a single data record of approximately 20 seconds duration is created. The data record will consist of data frames from randomly se- lected DAFCs of a single aircraft (A/C). The data frames are combined into a single data record of 34 frames to play into the N3P System. The data frames to be used for this phase of testing and the sequence of combination of the frames are listed in table 8.

4.2.3.2 Test Methodology. The purpose of Phase DI Testing is to quantify the false alarm rate of the N3P System as a function of transition between DAFCs. Phase HI Testing is based on upon the collection of false alarm statistics. For the purpose of Phase HI Testing, each data frame, regardless of origin, will have equal weight in determining the false alarm rate. The data record is played into the N3P System and the results will be tabulated in order to quantify the performance of the system. The first frame of the data record is cold-started into the N3P System.

4.2.3.3 Test Criteria. The criteria used in Phase m of the testing to determine system performance is PFA

4.3 DIGITAL PERFORMANCE TEST RESULTS

Phase I and II Combined

Criteria CCI CC PFA

Value 99.9% 96.0% 0.0%

Phase I - Baseline Digital Performance Test

Criteria CCIi CCI2 CCi CC2 PFA

Value 100.0% 100.0% 96.2% 96.2% 0.0%

Note: In all cases, the condition of the transmission according to ground-truth was correctly identified. Cases where the Correct Classification was not made occurred during the first and/or second data

22

frame of a record where the Radial Basis Function (RBF) nets had not yet stabilized. There were seven data frames (Helical Idler Gear Crack 80%T) were the AAs incorrectly identified the fault as normal. It is believed that this due to the threshold of the AAs being set to error on the side of no false alarm. The breakdown of calls for each data record used in Phase I Testing is included in table 9.

Phase II - Digitally Degraded Sensor Environment Performance Test

Criteria CCIi CCI2 CCi CC2 PFA

Value 99.9% 99.9% 95.8% 95.8% 0.0%

Note: All cases where the correct condition of the transmission were not identified were false negatives (i.e., a ground-truth defect condition was identified by the anomaly detectors as a good transmission) that occurred at a DAFC of 70% Torque. Cases where the Correct Classification was not made are once again the first few data frames of a record where the RBF nets had not yet stabilized. There were 18 data frames (SB Bearing Corrosion, Helical Idler Gear Crack, and Quill Shaft Crack) were the AAs incorrectly identified the fault as normal. Once again, this believed to be due to the threshold of the AA's being set to err on the side of no false alarm. The breakdown of calls for each data record is included in table 10.

Phase III - Digital DAFC Transition False Alarm Performance Test

Criteria PFA CCI CC

Value 0.0% 100.0% 97.0%

Note: There were no false alarms during this, or any, portion of testing. While the criteria of CCI and CC are not performance criteria for this test phase according to the test plan, these criteria were both satisfied by any sense of the measure. The breakdown of calls for each data record is included in table 11.

23

Table 8. Phase III digital DAFC transition false alarm test.

night # [■ 1614 ■

TBTT "1614 1614

"1614 TSTT"

I 1614 16M T6T4^ T6T4~

"1514 1614

"T6T4-

1614 "1614 "1614 "TSTT"

I 1614 1614 1614 1614 1614

' 1614 1674

"1614 '1614 1614

TSTT" TBTT" 1614

"1614 1614

'1614

Torque ~3o"—

Kegime DseS JTÖ7~ rest

«r SF Test ô "27"

Descent -BFPLOW

lest Test

"5TT "SF" Test SF lest

"TOO" ""S7RE TesT 45^

~5IT Test

SF lest "SÜ"

"27"

ST— BFPLOW

lest lest

~W "BFPLOW

D3OT— Test Test

"7Ö- "SF" 1WT -cnraB"

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~35~ "27-

SF BFPLOW

lest lest

~35"" "5T

lest ~5F"

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" I est

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"sr Test

IT "TJESCENT Test lest

60 ^F^ lest -?T" "To~

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Türr "WE"

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iNone iNone

NKau NumDer 1614.040.14.33 1614.070.15.34

iNone •■1614.03071526" INone "TBT3Cü57?rTST iNone : 1614.0607T5T59" iNone 1614.060.15.5S jNone '1614.100.15.44 Tftone 1614.0"45T57ÖÖ" None :1614.050.15724" None None

.1614.080.15.41 T614.027.14.32""

'None "1614.027.14.51 None 1614.050.15.03 None 1614.070.15,56"

iNone 1614.100.16.39 INone 1614.045.15.21 jNone 1614.027.14.51 I None 1614.045.14.40 None ;i614.055.15.29 None '1614.100.15.47 None .1614.080.15.49 None 1614.050.16.06 None 1614.027.16.31 None "1614.060.15.31 None TMone

M614.Ö45.16.09 ;1614.070.15.56

None 11614.080.16.33 None ""1614.075.15.37 None i1614.030.14.35 None :1614.Ö4Ö.15.19"" None 1614.060.15.59 None

I None

:1614.027.14.51 1614.100.15.47

Note: If an NRaD number appears more than once, sequential data frames will be used from that data file. Otherwise, the first data file will be used.

24

Table 9. Baseline digital performance test results by data record. »Calls PossiDie I TW I i i i

»Calls Made I 1700 i i ; j i 1 i , !

uerect | ! NKau Numbe CCI ! CC i No Keport j halse Alarm Planet corrosion JUÜ.B1.U99.100 34 j 33 i 0 i o ÜB Seanng Corrosion jUJ.A1.14U.U2/ 1 | 34 i 32 I u ! U bö Beanng Corrosion |UJ.A1.1^t3.U4& ! I 34 j 33 i o i u bb beanng Corrosion U3.A1.122.027 I 34 ] 33 i u I Ü bb beanng Corrosion UJ.A1.13Ö.UÖ0 j i 34 33 ! 0 ! o bb beanng Corrosion U3.A1.146.040 | j 34 ! 33 : o ! 0 üü beanng Corrosion U3.B1.161.0/0 j | 34 ] 33 i o I 0 bB Beanng Corrosion Ü3.A1.149.100 I M j 33 0 i 0 bb Beanng Corrosion U3.B1.156.060 I 34 j 33 0 0 bb bpalling U4.A1.426.027 i 34 i 32 : u ! U bb bpalling |U4.A1.422.040 | 34 | 33 j 0 ! 0 bb bpalling j04.A1.421.045 | 34 j 33 ! 0 i 0 bb bpalling IO4.A1.418.060 j 34 j 33 i <J i 0 bb bpalling |04.A1.410.070 i 34 i 33 | 0 i o bb bpalling I04.A1.424.080 | 34 33 j o u bb bpalling 04.A2.432.040 | 34 33 i o i o bb bpalling U4.A2.431.04b i 34 33 l u ! Ü bb bpalling U4.A1.433.060 | 34 33 ! u 0 Port Input Pinion Chip 05.B1.294.0/0 j 34 32 ! u ! 0 Port input Pinion Chip 05.B1.295.075 j j 34 32 i 0 ! 0 Port Input Pinion Chip 05.B1.291.100 34 33 i o j 0 Port Input Pinion Chip 05.B1.300.080 34 32 I o o Port Idler üear Crack 06.A1.404.080 34 33 I Ü ; 0 Port Idler üear Crack 06.A1.405.100 34 33 0 i o Port Idler üear Crack 06.A1.440.070 34 33 0 ! 0 Port Idler Ciear Crack 06.A1.445.080 I 34 33 I Ü . I 0 Mb Collector Crack 07.A2.495.100 I 34 33 0 ! 0 None j 00.A2.678.080 34 33 0 ! u None | 00.A1.679.100 34 32 0 I o Uuill Crack 08.A1.280.040 34 32 0 i 0 Uuiil Crack 08.A1.282.050 34 32 0 i o Uuill Crack 08.A1.284.070 i 34 33 u I 0 uuill crack UÖ.A1.286.U80 I 34 32 Ü i ° None | UU.A4./2/.1UU i 34 32 0 I u

; I i Detect j | r I ; None | 1 614.100.15.47 34 33 0 i Ü None | ; 651.030.12.51 34 33 0 ! 0 None j ; 651.040.13.30 34 33 0 (J None i 3 651.050.14.08 34 33 0 0 None j 2 651.060.14.02 34 33 0 0 None | 2 651.070.13.59 34 33 0 u None 3653.040.10.28 34 33 0 0 None 3653.045.16.04 34 33 0 0 None 3653.050.10.34 34 33 0 0 None 7642.027.15.02 34 33 0 u None /642.055.15.57 34 33 0 0 hAULI 7645.030.14.01 34 31 u Ü hAULI j 7 645.U55.15.53 34 32 o 0 l-AUL I | 7 645.070.15.4/ 34 32 u 0 l-AULl" j 7 645.080.16.09 34 32 o 0 None j 7 646.100.16.13 34 33' o 0

I I I otals 1700 ' 1635 0 0

25

Table 10. Digitally degraded sensor environment performance test by data record. «Calls Possible

#Calls Made T7ÜÜ" 1700

i Proposed i Accelerometer i

ÜB Hearing Corrosion ÜB Beanng Corrosion

Delect Planet Corrosion

to Kemove 5

ÜB Beanng Corrosion

| NKaL) Number |0ZB1.099.100 J03.A1.140.027 103.A1.126.045

üB Beanng Corrosion ÜB Bearing Corrosion ÜB Beanng Corrosion ÜB Beanng Corrosion üB Beanng Corrosion ~ ÜB üpalling üB üpalling üB Spätling ÜB üpalling ÜB üpalling

Ü3ST Ü33H"

122.027 138.080

Ü3.A1.146.040 HTBT Z33ÄT

T6"1.070 "«9.100

"Ö37B1.156.060" 04.A1.426.027 "(ÄfAi .422.040 TKtt TKM

421.045 4T87J5Ö"

ÜB üpalling T04:A1 .410.070 'JÜ4.A1.424.080

üB üpalling ÜB üpalling üB üpalling Port Input Pinion Chip

Ü4.A2.432.040 TKJSZ. Ü43AT

431.045 733.060

Port Input Pinion Chip '06.b51.294.070

Port Input Pinion Chip Port Input Pinion Chip Port Idler Gear Crack port idler Uear Crack Port Idler Gear Crack

"Ü6.B1.295.075 "05'.B1.291.100 05.B1.300.080 Ü6.A1.404.080 Ü6.A1.405.100 T

Port Idler (iear Crack" MB collector Crack

06.A1.440.070

None None Uuill Crack Uuili Crack Uuill Crack Uuill Crack

06.A1.445.080 Ü/.AZ495.100 00.AZ578.080 00.A1.679.100 08.A1.280.040 08.A1.282.050 08.A1.284.070

None |ua.Ai.^86.080 i0O.A4.727.10O

Detect None '71614.100.15.47 None None None None None None

3b"5"1':030.12.51 3651.040.13.30 3651.050.14.08 '3661.Ü60.14.02 3651.070.13.59 "3653.040.10.28

None None None None FAULT FÄÜCT"

3663.045.16.04 3653.050. 7642U27

Ttrw WÜT

/642.055.15.57" /645.030.14.01

"CcT "34"

"C"C No Keport halse Alarm "32"

"34" "32" "34" "33" 134^ 133^ "34" "33" 134 "33" ^4" "33" ^4^ 134

13T "33"

134^ "34"

3V "33"

"34" "33" 134^ "33" 15T "33" "34" 133" 134 3T "34" "33" "34" ^T "34" "32" 134" 32^ "34" 133" ^r "34"

~32~ "33"

34 "33" "33" 134^ "34~

"32" "32"

"34" "33" "33"

"34—j—3T "34" 134^ 134 "34"

33 132^ "32" "32"

"34" "32"

34 "34"

"33" "33"

"34" "33" "34" "33" 134^ XT 13T 1ST 3T "33"

134^ 134^ 134^ ^W 134^

"33" "33" "33" "33" 13T

W

FÄTJLT" 7645.055. 7645.07CT

T533" "34" T574T

FATJCT" "34"

"3T" "32"

76457ü5ö7T6Ug" "None

"34" 76463ÜÖ36T3" 134"

U2~ "32"

Totals "T6'99~TT629"

26

Table 11. Digital DAFC transition false alarm performance test by data frame. »calls HossiDle 34 | | CCI I cc l No Report | halse Al arm »(Jails Made 34 I j 34 j 33 I 0 ! 0

I i I | j Defect I I !

i-ugni ff i orque Kegime Used jhor j Level j Detect j NKaD Number ; ibi4 • 40 fest j i o (None "[1614.040.14.38 j

1014 ; 70 SF lest ! | 0 I None i 1614.070.15.34 j 1614 30 Descent Test 0 INone 1614.030.1b.26 i 1614 i 27 ■ BFPLOW lest ■ 0 [None 1614.027.14.51 i 1614 : 60 SF lest j i 0 iNone |1614.060.15.5Ö i 1614 ; 60 SF Test i o iNone (1614.060.15.69 j 1614 ; 100 VNE lest [ ! 0 iNone 11614.100.15.44 i 1514 45 lest i ! o | None ,1614.045.15.00 | 1014 5U SF lest | | o None j 1614.050.15.24 j 1614 80 SF Test i j 0 None 11614.080.15.41 j 1614 27 : BFPLOW rest | ! 0 None j 1614.027.14.32 j 1614 27 BFPLOW lest j ■ o None "1614.027.14.51 ! 1614 50 LOW lest j ■ 0 None 1614.050.15.03 i 1614 70 SF lest | 0 None 1614.070.15.56 1614 100 CLIMB lest | i o None 1614.100.16.39 1614 45 SF lest I | 0 None 1614.045.15.21 1614 27 BFPLOW lest | | o None 1614.027.14.51 1b14 , 45 I est ; 0 None 1614.045.14.40 1b14 55 SF lest | 0 None 1614.055.15.29 1614 100 VNE lest i 0 None 1614.100.15.47 1614 . 80 SF 11 est | i 0 None 1614.080.15.49 1614 50 SF [Test . I 0 None 1614.050.16.06 1614 . 27 DESCENT j Fest | 0 None , 1614.027.16.31 ! 1614 . 60 SF Test j 0 I None 1614.060.15.31 | 1614 . 45 SF lest i u I None | 1614.045.16.09 i 1614 : 70 SF lest i u I None | 1614.070.15.56 j 1614 80 CLIMB lest j | 0 j None 11614.080.16.33 j 1614 75 SF lest | | 0 | None | 1614.075.15.37 |

— 1614 30 lest | | 0 None > 1614.030.14.35 j 1614 : 40 SF lest j ! 0 j None j 1614.040.15.19 i 1614 60 SF lest j j ü | None | 1614.060.15.59 1614 27 BFPLOW lest j ! 0 | None j 1614.027.14.51 1614 ■■ 100 VNE lest | | 0 | None j 1614.100.15.47 i 1614 ' 30 lest | ] 0 | None | 1614.030.14.54 ]

27

5 DETECTION OF A FAULT IN BU NO. 156432

5.1 USE OF IN-FLIGHT DATA

The transmission/mix box diagnostic neural networks were trained to recognize one of eight test stand conditions (seven different defect conditions and a no-defect condition). These training data were limited to a single mechanical assembly that underwent several rebuilds during the seeded fault trials. Additionally, the data were from an assembly that was installed in a massive test stand and driven by electric motors into a water brake load. To extend the diagnostics to a realistic environment, recorded in-flight vibration data were used. Sixty-four features indicative of the condition of a particular subassembly, in the transmission/mix box, were extracted from each of the eight vibration channels. These features were combined into a single feature of 512 elements that represented the overall condition of the transmission/mix box for a particular Data Acqusition Flight Condition. These exemplars were used to augment the original network training. This was expected to increase the robustness and reduce false alarms due to differences between the test stand and in-flight envi- ronments. The data, from multiple aircraft, could also be used to evaluate the capability to perform diagnostics on a transmission/mix box without first establishing a baseline signature. (The Teledyne HUMS required 25 in-flight hours to establish a baseline signature). Implicit in the use of in-flight data from deployed Marine Corps aircraft is the assumption that the data represent transmission/mix box assemblies in good working order with no defects.

5.2 RADIAL BASIS FUNCTION NEURAL NETWORKS

A neural network differs from classical techniques of mechanical diagnosis in that the network is trained using a representative sample of data out of the "universe" of data or features the network is expected to encounter. This training allows the network to form statistical models of the types of features that the network will be expected to recognize. Essentially, the network builds its own statistical defect models using examples from the feature sets that it is desired it recognize. Assuming that the training feature sets are truly representative of the particular classes of data, the statistical models are inherently robust and have an ability to generalize across the entire class of data.

This statistical model is then used to recognize other examples of the same type of defect. Since the models are robust, the network can generalize to features that are similar but not exactly the same as the training sets. It is desired that this robustness could be exploited so that the network would not have to be baselined using data from each and every transmission/mix box to which they would ever be applied. At this time, the program was using a Radial Basis Function (RBF) network as the major technique of classification since RBF networks have many characteristics that make them desirable to use in this application. Some of the characteristics that the program was trying to exploit are that RBFs work well as classifiers, that a RBF network forms "soft" as opposed to sharply delineated decision surfaces, and that RBF nets can be rapidly trained on a relatively large quantity of features.

RBF neural networks have the common network architecture of simple processing elements or nodes that are heavily interconnected and are typically constructed as a feed- forward network with a single hidden layer. Each node of the hidden layer simultaneously receives the entire input vector and is fully interconnected to an output layer of linear units (see figure 7). Each hidden layer node uses a radial basis function to form a localized receptive field that calculates the "closeness" of the input vector to a particular data cluster in feature space. The closer the input vector is to the center of the cluster the more strongly the neuron responds with an upper bound of one.

29

The RBF used is typically a gaussian basis or a logistic function. This type of function gives rise to a symmetric receptive field in feature vector space (figure 8). The combination of these basis functions in the hidden layer gives rise to "soft" decision surfaces where there is no hard boundary that encloses a decision region. This soft decision surface allows the RBF net's performance to degrade somewhat gracefully as a function of deterioration of data quality. As data quality degrades, the features extracted tend to fall further from the centers of the data clusters, resulting in a smaller output for any given class or combination of classes. This can be contrasted with the hard decision surfaces formed by a typical Multi-Layer Perception (MLP).

Input Vector

* Output

*■ Output

Output

Figure 7. Typical architecture of a RBF neural network. This network has a single hidden layer of RBFs fully interconnected to an output layer of linear units. Each output of an RBF node is multiplied by an interconnection weight that is determined by training. The weighted outputs of the RBF nodes are then summed at the corresponding linear unit. After summation, the output of the linear node is determined using a sigmoid transform to bound the output.

30

Output for feature vector

falling in receptive field

Center of Data Cluster

Figure 8. Modified RBF in two-dimensional feature space formed using the modified logistic function

l + exp lx-n| , where q> represents the bias added to the receptive field, a represents the width of

the field, and \i represents the center of the field. Note that the function gives rise to a symmetric, localized, receptive field.

Each hidden unit of a multi-layer perceptron is used to form a hyperplane as opposed to a localized receptive field. This hyperplane, in combination with hyperplanes formed from other hidden layer nodes, encloses a decision region of feature space. Typically, it takes four or more hyperplanes to en- close a given decision region. The input feature vector is either inside or outside of this bounded decision region as opposed to the continuum of responses provided by a soft decision surface. This strictly delineated decision boundary is a potential source of high false positives for a multi-layer perceptron approach.

To form the RBF network, the features extracted from the data are first clustered in some manner to reduce the complexity of the problem and to speed computations. It is desired that this clustering be efficient without losing the essential character of the features. The best clustering is achieved when the clusters densely populate the data-rich regions of the feature space. This speeds training since most training techniques only update those weights associated with a stimulated cluster during any given training cycle and improves classification accuracy. Our approach, as is common in the neural network community, has been to use the Ä-means3algorithm to cluster the feature training set. The choice of the number of clusters, k, is arbitrary and typically confirmed using cross-valation techniques. Due to the architecture of RBF networks, these types of nets perform very well on features that have well- separated data clusters.

3 J. MacQueen. 1967. "Some Methods for Classification and Analysis of Multivariate Observations," In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume I, Statistics. L. M. Le Cam and J. Neyman, eds. Univeristy of California Press, Berkeley, CA.

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After the features have been clustered, a RBF corresponding to a single middle layer neuron is placed at the centroid of each cluster. By centering the RBF of this neuron on the given data cluster, the receptive field of this neuron has been tuned to respond to a feature vector input near the given data cluster. All of the RBF neurons are then connected in turn to all of the output class neurons with each output class represented by a single neuron in the output layer. This neuron is typically a linear unit that sums its interconnection weighted inputs, and then applies a sigmoid transform to bound the output between zero and one (figure 9). The network is then trained to recognize a particular class of data using a representative set of features corresponding to those data.

Input

Figure 9. Sigmoid transfer function that takes the sum of inputs to a linear output neuron and bounds the output response to [0,1]. Note that the input can vary from -oo to oo in two dimensions, but the output will always be "squashed" to between zero and one. Hence the nickname, squashing function.

Once the network architecture has been determined, the network can be trained and the interconnection weights determined. RBF networks are typically trained on one class of data at a time. During the training, representative features from the class being trained are presented to the network and the interconnection weights for the RBF nodes or data clusters are adjusted until the network does an adequate job recognizing this class of data. The next class of data are then trained with its associated weights adjusted and so forth, until all classes of data have been trained. The training is insensitive to the sequence of classes that is presented to the net. Once the network has been fully trained, it is ready to be cross-validated using hold-out techniques and other methods.

32

5.3 AUGMENTING THE TRAINING OF THE RBF NETWORK USING IN-FLIGHT DATA

In order to train and evaluate the performance of the RBF network using the in-flight data, a training feature set and a cross-validation set of features were required. Due to differences in the method of data collection between the in-flight and test-rig data sets, it was necessary to design a preprocessing filter transform to align the two data sets. This necessitated retraining the entire network since the baseline data had fundamentally changed. The same test-rig training set as used previously was reused along with a subset of the total in-flight data set.

During the training process, it was desired that the network develop a capability to generalize and to correctly classify as demonstrated using the cross-valation techniques. The need to develop these capabilities across a range of torques and airframes drove the design of the training set. Obviously, a representative sample of each defect was required with a corresponding sample of no-defect conditions. Since the no-defect condition is the nominal condition and was assumed to occur for a large number of transmissions, the amount of no-defect training data are over-represented relative to any given fault class but the amount of no-defect training data were roughly the same as the training data for all fault classes. It was also desirable for each training exemplar to be represented across the full range of torques available in order to provide for a robust classification capability. These concerns provided the framework for the partitioning of the in-flight data into a training set and a cross-valation set.

Given the total amount of in-flight data available, feature sets from four of the A/C were chosen to augment the training set and feature sets from the remaining four A/C were chosen for the evaluation set. This decision was based on the amount of data available from each A/C for both the initial and final data collections4, the torques available for all A/C, and the torques available for any given A/C. Note that no data from the A/C chosen for the cross validation feature set had ever been presented to the network during the training process. This type of "blind" cross validation using features from a dis- tinctly new and separate, but similar type of data that the network should be able to correctly classify based on generalization capabilities, is commonly referred to as hold-out testing, if done properly. It is worth emphasizing that to be true hold-out testing, the data must be uniquely distinct from the data forming the training set and original to the network.

The training data set was then used to train the RBF network. First the features were clustered using a modified &-means algorithm. Several choices of the number of centroids were evaluated for the purposes of cross validation and network complexity tradeoff studies. The different networks were then trained on the same data set using supervised learning techniques. Each of the seven defect classes of training data from the test-rig were sequentially presented to each network and the interconnection weights adjusted until the network correctly classified the defect class with an arbitrarily low Sum- Squared Error (SSE). The combined no defect class of training consisting of in-flight and test-rig no- defect data were then presented to the networks in the same manner with corresponding adjustment to the interconnection weights. After training, no further adjustments were made to the networks and they were ready for cross validation.

*Due to instrumentation/sensor malfunctions, the initial data collections from five of the A/C were unusable and all of the data from one A/C proved unusable.

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5.4 INDICATION OF AN ANOMALOUS CONDITION DURING CROSS VALIDATION

During the cross validation of the trained networks, data from three of the A/C were consistently and confidently classified as corresponding to a no-defect condition, as expected. The networks' response to the features extracted from the second data collection on Bu No. 156432, however, proved to be quite unexpected. For the two lowest torques at which data were collected, the networks consistently responded as if they were confused as to the condition of this particular transmission. In particular, the response for the network lying at the knee of the performance versus the complexity curve is shown in figure 10. Additionally, the classification at the higher torques was consistently no defect, but at a degraded confidence level. The response for all networks trained was consistent and similar.

1 ' (a) 1 [ (b) 1 (c) 0.9 ' 0.9 0.9 ■

0.8 [ 0.8 • 0.8

0.7 1 0.7 0.7

0.6 1 0.6 0.6

0.5 •t 0.5 0.5

0.4 r 0.4 t | 0.4

0.3 0.3 b 0.3 it []

* 5 flf 0.2

0.1

0 C

* Iff^S 0.2

0.1 9

0.2

0.1

Ol 5

itput Class

0 c ) 5

Output Class

o1

c 5 Output Class

Figure 10. Typical Whisker plots showing the distribution of RBF Output Node responses: (a) typical no-defect data; (b) data from Bu No. 156432 at 27% torque; (c) data from Bu No. 156432 at 30% torque. The bars indicate the standard deviation of the distributions while the boxes contain 75% of the data with the mean of the distribution indicated by the horizontal bars within the box. For the typical no-defect response, only the first class or no-defect node is highly excited, with all other nodes showing a low, approximately constant, level of excitation. For the data from Bu No. 156432, all node responses are relatively low level, with the node having the highest response toggling as a function of torque.

In general, the RBF output nodes were showing a low-level response to all classes previously trained on, with no confident classification to any particular class of defect or no defect previously trained on. Further, the node that seemed to be most highly excited was not stable as a function of torque. For the two lowest torques, the highest node output toggled among classes four and eight. No significance is attached to the particular classes excited since the network was most likely responding to a type of data clustered outside of its "universe" and the scarcity of data makes analysis problematic.

This type of behavior from RBF classifiers is typically indicative of presentation to the network of a type of data with which they have no previous experience and have not been trained to recognize. If the data have not been corrupted in some manner (i.e., "garbage in—garbage out") and most resembles the characteristics of a class of data clusters on which the network has been trained, the output class repre- senting these clusters will be the one most highly excited. If the data do not resemble any of the characteristics of the data clusters, the response of a typical RBF net is not a priori determinable except in certain pathologic cases. The situation is analogous to training a RBF network to recognize red or green, and then presenting the network with a yellow and asking it to tell you whether it is red or green.

34

In some sense, it is kind of red, and kind of green, but the correct response is that it is neither and is novel to the network.

Given our past experience with these data, this type of behavior was atypical due to the nature of and consistency of the misclassification. Previously, we had found that there was a locally optimal number of data clusters for which the size of the RBF employed tended to be irrelevant (within reasonable limits) to the performance of the network. As the number of clusters increased, performance did not significantly increase and tended to depend on the size of the RBF employed. When the number of clusters reached an architecture and data-dependent threshold, adding more clusters decreased the performance independent of the size of the radial basis function. As the number of data clusters was decreased below the local optimum, performance was highly dependent on the size of the radial basis function used and tended to be degraded compared to the networks trained using the locally optimum number of clusters. In all cases when misclassifications were previously observed, they tended to be distributed among several to all of the data classes and inconsistent within any given class. Our experience with the features extracted from Bu No. 156432 was the first time that we had observed consistent misclassification confined to a single class for all of the network architectures used during the cross-valation procedure.

5.5 INVESTIGATION OF ANOMALY

Once the anomaly had been consistently indicated, the baseline data were once again examined regarding its integrity. The baseline automated quality assurance algorithms were rerun on the raw data with no deficiencies in the data indicated. These algorithms had been developed to check for sensor faults and data deficiencies that had previously been seen in the avionics environment. The algorithms also checked to ensure that the data collection system was functioning properly and did not induce any artifacts into the data

The raw data were then manually examined in minute detail to determine if a new type of sensor fault or data collection, system-induced problem had been encountered during the collection of this particular data set. There was no indication from visual inspection of the raw data that data had been corrupted in any manner. To all intents and purposes, the raw time-series data appeared to be free from defects. The data did have slightly different amplitude levels on two of the eight channels (one elevated and one decreased) when compared to the minimal amount of data available from the initial collection5, but the magnitude of the changes were within normal deviations of all the data examined to date. The conclusion was drawn that the anomaly was not caused by corrupted data.

The training and cross-valation data sets were then repartitioned in order to further investigate the anomaly. Each of the three cross-valation set members that did not behave in an anomalous manner was sequentially switched with a single member of the initial training data set. Three RBF networks having a different number of Ä-means clusters and different cluster widths were then trained using the new training data set. The networks were then evaluated using the new cross-valation data set. This investigation continued until all original members of the training data set had been replaced in rum by one of the members of the original cross-valation set that did not display anomalous behavior. In total,

5 Due to instrumentation difficulties, only one complete data record of approximately 20 seconds duration was available from the first data collection. The anomaly may or may not have been present at this time but the short duration of the data record did not allow enhanced signal averages to be formed and very little analysis or comparison could be done with such a small amount of data.

35

27 different networks were trained6 and evaluated. In all cases, the behavior of the networks still indicated that the data from the low torques of Bu No. 156432 were different from the remainder of the in- flight data and the test-rig no-fault data, and that this difference was statistically significant.

Lastly, a generic feed-forward MLP network with two hidden layers was selectively trained using the baseline training data set and standard back propagation techniques of training. The back propagation algorithm was slightly modified so that during each training epoch, the cross-valation data were also presented to the network and a SSE-computed for the data from each of the A/C. Although the cross-valation data were presented during the training process, no adjustment of the interconnection weights was made for the cross-valation data The errors due to the cross-valation data set were not allowed to back propagate through the net, only the errors due to learning to recognize the baseline training data set were allowed to back propagate and cause weight adjustments. As the network As the network converged, the SSEs for all of the A/C from the cross-valation data set significantly decreased while the MSE calculated for the data from Bu No. 156432 stayed relatively large and roughly constant. This trend in the SSEs is shown in figure 11.

5.6 NOTIFICATION OF NAVAL AVIATION SYSTEMS COMMAND (NAVAIR) REGARDING THE ANOMALY

At this time, the decision was made to notify NAVAIR that an anomaly had occurred during the processing of the in-flight data and that the anomaly was indicative of a transmission that differed from the seven other transmissions available for comparison. Naval Air Warfare Center Aircraft Division (NAWCAD) PAX River Flight Test Engineering Group and NAVAIR 4.4.2 were contacted and in- formed that the data from A/C BuNo 156432 was anomalous when compared to the data from the seven other A/C in our database7. After discussions including NAVAIR, diagnosticians at the Original Equipment Manufacturer (OEM) and NAWCAD Trenton, the decision was made to down the A/C and notify the associated squadron of the downing due to a potential aft transmission failure.

At the time that the A/C was downed, the time on this particular aft transmission was 830.6 hours. The Time Between Overhaul (TBO) for this type of transmission is 900 hours. Typically, a 10% life waiver is requested, and the aft transmission will fly for approximately 990 hours between overhauls. The data collection causing the anomalous response was started at an aft transmission time of 710.7 hours and continued for approximately 20 hours at Marine Corps Air Station El Toro where the A/C had been attached to Marine Medium Helicopter Squadron (HMM) 764. Recent to the downing, the A/C had been deployed ferrying troops ship-to-shore during Operation Kernal Blitz '95.

After downing, the transmission was to be removed and sent to Naval Depot (NADEP) Cherry Point for Engineering Inspection (El) to determine the status of the transmission components. If the A/C had not been downed, it could have been expected to fly for another 159.4 hours since there were no other

6 Note that for each network trained, the centers of the initial data clusters differed. Further, the weights for each training session were randomly initialized prior to convergence. Thus, none of the networks after training were identical.

The Flight Test Engineering Group at Pax River had been responsible for the data collection while NAVAIR 4.2.2 had program oversight regarding the aft transmission neural network effort.

36

1 ■' ! ; ,

ii1/ f

SSE from Anomalous A/C

.SSE from No-Defect A/C

—Training Error

400 600 Training Epoch

1000

Figure 11. Trend in the SSE as a function of training epoch. The SSEs have been normalized by the maximum sum-squared error encountered during the training run. The SSE for the training data set is plotted as a solid line. The SSE for the three A/C in the cross-valation data set that did not have an anomalous response are shown as the lightly shaded lines while BuNo 156432 is shown as the darkly shaded line.

indications of a potential problem with the transmission. Periodic Spectral Oil Analysis Program (SOAP) burns had been passed with no anomalies indicated, and the squadron reported no special maintenance actions for mis A/C. Additionally, no unusual behavior from the transmission had been reported by the crew. To the date of downing, the only indication that there was a potential problem with the transmission had been the anomalous response of the RBF networks.

5.7 RESULTS OF THE ENGINEERING INSPECTION (El)

The El on transmission S/N A9-328 was performed by the NADEP Cherry Point personnel who are responsible for performing the standard 900-hour rebuilds on the CH-46 transmission. When the transmission cases were cracked, separating mix box from the aft transmission, the spiral bevel ring and pinion gear were found to have obvious and considerable damage. Both mating surfaces of the gears had considerable wear to the point of cold metal working on their meshing surfaces. The wear was evenly distributed for all teeth of the gears and a considerable portion of the mesh was worn (please refer to figures 12 and 13). The wear was so extensive that depot personnel called it severe galling. Depot personnel also said that if this was not the worst, then it was the second-worst type of this damage that they had seen.

As the El progressed, the collector gear was found to be in good condition with a minimal and normal amount of wear. Intervening bearings and shafts were also found to be in good condition and at proper preload and alignment. No other components were found to be damaged until the helical input pinions were removed from the mix box case. When the input pinions were removed, the starboard input pinion was found to have a localized chip or gouge and associated wear surrounding the gouge (figure 14). It is believed that this gouge was caused by debris passing through the mesh.

37

To summarize the El, the transmission was anomalous and damaged. Two out of the three areas we had identified as potential problems were found to have defects. The defects were non-trivial and caused rejection of the parts by NADEP Cherry Point. In particular, the spiral bevel gear had a very high degree of wear and was new at the time that this transmission was last rebuilt. Such gears typically last two to three rebuilds.

5.8 AUTO-ASSOCIATOR NEURAL NETWORK

Once the transmission had proved defective, the in-flight defect data were used to validate and dem- onstrate the utility of a new type of neural network. We had recently been working with an Auto- Associator (AA) or encoding network for novelty detection. This type of network is a three-layer, feed- forward network that attempts to map the output onto the input using a reduced number of bidden or middle layer nodes (figure 15). In this type of architecture, the input layer and output layer have the same number of nodes in order to facilitate quantifying the success in mapping the output onto the input. The middle layer has a reduced number of nodes when compared to the input and output layers. The purpose of the hidden layer is to encode the essential character of the training class by use of a reduced set of information about the training class.

(a) (b)

Figure 12. Damage to spiral bevel ring gear (a) general and (b) close-up. Damage occurs around the entire gear and across a significant portion of the mesh. Galling has worn through the black metal oxide coating and is close to cold metal working.

38

Figure 13. Damage to spiral bevel input pinion gear (a) general and (b) close-up. The damage is symmetric to that of the mating spiral bevel ring gear. Again, the galling has worn through the black metal oxide coating and is close to cold metal working.

Figure 14. Damage to starboard helical input pinion gear. Gouge or chip is localized to a single tooth near the edge of the mesh.

39

Input Output Compare Input to Output and Threshold

Figure 15. Architecture of a typical AA network. This network has three layers that are fully interconnected. The hidden or middle layer is of a reduced number of nodes when compared to the input or output layer in order to encode the essence of the training class of data. The input and output layer have the same number of nodes. After training, the output of a feature vector is compared to the input and the RMS error is calculated between the two. The error is then compared to a threshold. If the error is greater than the threshold, the input is considered novel.

This type of network differs significantly from typical pattern classifiers in that only "positive" data corresponding to a single class of data is used to train the network. During training, the network learns to map the output onto the input by encoding the essential character of the training class into the hidden layer. This is done by taking the output vector, reconstructed from the hidden layer, and comparing it to the input vector. A Root-Mean-Squared (RMS) error is then calculated from the difference of the input and output vectors. The interconnection weights are adjusted during the training process to reduce the RMS prediction error.

Once the network has been trained and a sufficiently small RMS prediction error achieved for the elements of the training data set, a threshold is set on the RMS error using cross-valation techniques. Typically, the threshold is set so that all members of the training class of data fall below the threshold while RMS prediction errors calculated from vectors corresponding to other classes of cross-valation data fall above the threshold. The network is then ready to be used in a specific application. When the network is employed, a feature vector never seen before by the network is passed in as input. The middle layer attempts to reconstruct the input using the mapping encoded in the layer during training. The input and output feature vectors are then compared and the RMS prediction error between the two is calculated. A large RMS error implies that the feature vector, for whatever reason, is novel or different than the class of data that the network has been trained on. Note that the network does not classify this vector to a specific class of data, it just denotes the feature vector as different or not a member of

40

different or not a member of the class of data that the network was trained on. Essentially, the network will tell you when something is novel not why it is novel.

In the AVDS gearbox application, the AA network had been trained using the original (including the in-flight data from the four good A/C) RBF training set of no-defect data. The test-rig defect data had then been used to cross validate the network and verify the threshold setting for RMS prediction error. The original cross-valation data set that caused the RBF network to indicate an anomaly was then presented to the AA network. The results of this test are shown in figure 16.

0.09 —

o Ü 0.08 in to S

0.07

0.06 —

L. • BuNo 156432 1 o OTHER IN FLIGHT I

«

• • • •

• • • — • • • • • • •

o

o

O O o ° o

° o O 0 o o w

1 1 1 1 ? 1 10 15

TEST RLE 20 25 30

Figure 16. Blind testing RMS prediction error vs. feature vector run for the trained AA network. The feature vectors from A/C Bu No. 156432 are shown in black while the feature vectors from the three other A/C in the cross-valation test set are shown in white. The prediction error threshold is set at approximately 0.75.

The figure shows that AA net correctly indicated the anomaly and did not false alarm for the three other A/C in the cross-valation test set. The AA net was able to correctly identify an anomalous condition consisting of multiple defects in an A/C when trained using only positive no-defect data. The AA net also demonstrated the capability to correctly assess the condition of multiple A/C with which the network had no previous experience.

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6 NEURAL NETWORK PROCESSOR (N3P) FLIGHT TRIALS

6.1 INTRODUCTION

The AVDS CH-46E Flight Trials demonstrated the potential of the N3P approach to transmission diagnosis. During the course of the trials, there were no false alarms and all classifications of the status of the aft main transmission of A/C 692 were to a healthy transmission. All classifications were made by the N3P without having to baseline the system to the transmission of A/C 692. The potential of the approach to identify those situations where sensor/data flaws bring into question the reliability of the results was also demonstrated. Unfortunately, the minimal quantity of data collected from a single A/C during the trials only shows the potential of the approach and does not provide a critical, defensible, quantified metric on the performance of the system.

For the analysis of the flight trials, it will be of benefit to quickly review the installation and communications of the N3P systems. The N3P hardware was installed in an avionics-hardened VMEbus chassis for placement in the A/C. A Teledyne-supplied signal conditioning card, a Teledyne-supplied phase lock loop and oscillator card, and a flight regime processor/communications card were also installed in the VMEbus chassis (the installation is shown in figures 17 and 18). The N3P would receive analog conditioned signals from the aft main transmission accelerometers and an analog conditioned rotor position shaft tachometer signal via the Tele- dyne-supplied cards. The N3P would also receive Data Acquisition Flight Conditions (DAFC) from the MDAU via the flight regime processor card at a 4-Hz rate for use in data processing. In turn, the N3P would provide a status to the MDAU at a 1 -Hz rate. Note that the health status of the transmission is not necessarily calculated at a 1-Hz rate. In the case where all conditions were present so that the N3P could analyze and classify the condition of the transmission, this calculation took approximately 3 seconds. The intervening status messages that went to the MDAU would be latched to the previous status in the output buffer during the calculation. Thus, the total number of status messages does not necessarily represent on a one- to- one basis the amount of data (in seconds) that was processed by the system.

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Figure 17. Front view of the VMEbus installation in A/C 692. The VMEbus chassis containing the N3P is located in the lower portion of the equipment rack. The OQAR that logged the N3P status message is the rectangular black box shown in the upper left of the equipment rack.

44

Figure 18. Side view of the VMEbus installation.

6.2 PROCESSING THE RESULTS OF THE TRIAL

Once the N3P status message was received by the MDAU via the flight regime processor card, it was then sent to the OQAR for data logging. The status message consisted of four fields; the report indicator, fault location, fault severity, and DAFC. A report indicator of value

45

1 to 3 indicates a healthy transmission (for a breakdown of the possible report indicators, please see table 12). A report indicator of 4 to 8 indicates a faulty or anomalous transmission.

Table 12. Report indicator breakdown.

Report Indicator Value Description

0 No Report At This Time

1 Good (Radial Basis Function)

2 Good (Auto-Associator)

3 Good (Radial Basis Function and Auto- Associator)

4 Anomaly (Auto-Associator)

5 Anomaly (Radial Basis Function and Auto- Associator)

6 Anomaly & Fault

7 Fault (Radial Basis Function)

8 Fault (Radial Basis Function and Auto- Associator)

9 Neural Network Confidence Failure

The fault location and fault severity fields were never used during the trials since no faults or anomalies were indicated during the course of the trials. The DAFCs in which the N3P at- tempted to process data and calculate the status of the transmission are listed in table 13. Note that the DAFC had to have been stable for approximately 15 seconds prior to and approximately 15 seconds after the calculation in order for the health status of the transmission to be output from the N3P to the MDAU.

After the flight, the N3P status messages on the OQAR disc could be browsed and converted for analysis using the Teledyne-supplied FLIDRAS software. The converted files then had to be edited prior to importation into the N3P groundstation for display. During and after the trials, the process of downloading the N3P messages turned out to be a convoluted and time consuming process that increased the time latency of the results and made troubleshooting and analysis a difficult and tiresome process.

In the N3P groundstation, the analysis of the results was limited to a sequence of histogram displays used to analyze the results and an overall status of the aft main transmission and N3P system for the flight. The messages could be broken down to display histograms of the status message sorted by types of report indicator (figure 19), confidence indicated in the answer (figure 20), and subsets of the answer corresponding to no fault and faults/anomaly.

46

Histograms of the DAFCs received from the flight Regime Processor could also be dis- played in the groundstation. The interim message file could also be manually examined in the groundstation to develop trend information and to select subsets of the flight for analysis and display.

Table 13. DAFC breakdown.

DAFC Value Description

0 Flat pitch (27% torque)

1 Light on wheels (50% torque)

2 Hover in ground effect (70% torque)

3 Hover out of ground effect (75% torque)

4 750 feet per minute descent (30% torque)

5 60 knots indicated air speed (45% torque)





10 Spare (not currently used)

13 Spare (not currently used)

14 Unknown on ground

15 Unknown in flight

47

N3P Report Histogram Flight Name = Flt-6 Record Count = 6137

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Figure 19. Sample histogram from Flight N3SHA-6 showing distribution report indicators broken down into fields of good transmission, anomalous-faulty transmission, no report at this time, and network confidence failures. During this flight, multiple sensor/wiring failures related to loose connectors and an accelerometer backing off its mounting bolt caused network confidence failures.

N3P NN Confidence Tabulation Flight Name = Flt-6 Record Count = 6137

144

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48

6.3 PROCESSED FLIGHT TRIALS DATA

During the course of the trials, there were 18 dedicated flights and one scripted piggy back flight (the flight to and from the USS Saipan LHA2). For the first flight, the N3P system had a hardware failure that prevented the system from booting and operating during the flight. The N3P system was not installed aboard the A/C during the second flight due to troubleshooting of the hardware. For the remaining 17 flights, at least some data from each flight are available for analysis (table 14). It is worth emphasizing that these data were derived without baselining the system to the transmission of 692, and that the N3P software operated without flaw during the trials. Two software changes were made during the trials, around Flight N3SHA-13, in an at- tempt to aid in troubleshooting the DAFC calculation and sensor failures, but these changes did not affect the diagnostic or performance characteristics of the N3P. One software change allowed time code to be written to exabyte tapes to help in troubleshooting the DAFC calculations while the other change stored an output file on the hard disk to allow "quick look" troubleshooting of the sensors and wiring.

Table 14. Report indicators broken down by flight. Flight N3SHA6-8 was particularly plagued with multiple sensor failures including bad microdot connections, accelerometers backing off mounts, problematic patch panel connections, intermittent signal saturation, etc.

FLIGHT No Report Indicator Good Indicator Confidence Failure Anomaly/Fault N3SHA -3 5993 129 0 0 N3SHA -4 3864 240 0 0 N3SHA -5 3356 234 0 0 N3SHA -6 5831 144 162 0 N3SHA-7 2060 162 138 0 N3SHA -8 5806 114 60 0 N3SHA-9 5282 570 0 0

N3SHA-10 5947 779 0 0 N3SHA -11 5375 479 0 0 N3SHA-12 4631 334 0 0 N3SHA-13 5190 996 0 0 N3SHA-14 1970 381 0 0 N3SHA-15 6708 381 0 0 N3SHA-16 6157 144 0 0 N3SHA-17 5429 415 0 0 N3SHA-18 2525 807 0 0

N3SHA Saipan 4400 162 0 0

Total 80524 6471 360 0

The majority of no report at this time indicators encountered during the trials were due to development and refinement of the DAFC calculation and limits. Many of the remaining no report indicators and the network confidence failure indicators are due to sensor failures, connector failures, cabling failures, and equipment failures. Under the operating conditions of the trials, the A/C spent a fair amount of time transitioning between DAFCs and in-flight regimes that were not covered by the DAFCs used by the N3P. This normal operation accounts for the remainder of the no report indicators.

49

From the above data, two trends are important to note. First, over the course of the trials the DAFC calculations did tend to improve with a corresponding rise in the number of report indicators that actually classified the health of the transmission. This trend is positive, but not mo- notonic. Secondly, right up to the end of the trials the installation was plagued by sensor and cabling associated failures. There were also several unscripted piggyback flights where the N3P was installed aboard the A/C, but due to DAFC troubleshooting and faulty SCSI drive cabling, there is no N3P data available for analysis from these flights.

6.4 FLIGHT TRIALS CONCLUSIONS

During the course of the trials, the N3P system was up and running for a total of 24.3 hours. The N3P system provided 1.9 hours of status reports containing a condition identification other than no report at a 1-second interval rate. Of these 1.9 hours, 1.8 hours were actual classifications as to the health of the transmission. This 1.8 hours of no fault transmission classifications corresponds to 7.8% of the time that the N3P was up and running. The actual amount of data that were used to classify the transmission as to a no-fault status for this 7.8% of N3P uptime was 0.60 hours or 36 minutes.

There were no false alarms recorded during the course of the trials. The total amount of time that comprised an opportunity for a false alarm was 1.9 hours. The underlying amount of data comprising this opportunity correspond to 0.63 hours or 37.8 minutes. This window of opportunity for false alarm corresponds to 2.6% of the total N3P uptime. It should be obvious that extraction of a meaningful false alarm rate from this minimal amount of data from a statistical sample of one transmission is not possible. Therefore, even though no false alarms were indicated during the trials this is not taken to indicate with any confidence that the N3P false alarm rate is zero. The final conclusion is that the trials were encouraging, but by no means a defini- tive measure of the performance of the N3P system.

50

7 PROGRAM CONCLUSIONS The capability of the N3P system to perform diagnostics without baselining to a particular

build of the given transmission was demonstrated in all three phases of the program. During the Test Stand portion of the program the neural networks were trained using data from one build of the transmission and blind-tested on data from a build separated by nine major maintenance actions. The networks successfully classified the "blind-build" as no fault. When the system was trained using in-flight data, only data from four of the available A/C were used during training. The data from the remaining four A/C were used to blind-test the system. There was no baselining of the system to this set of data. It was out of this set of data that the find in A/C 156432 came. As previously noted, the system had an anomalous response to A/C 156432 and successfully classified the other A/C as no fault. During the flight trials, the system was installed on A/C 692 without baselining. The system was able to correctly diagnose the condition of the A/C without using data from the A/C to "tune" the system to this A/C.

Being able to operate the system without baselining to a given A/C is a significant achieve- ment and a valid goal for the program. Typically, it takes COTS systems on the order of 20 dedicated flight hours to baseline to a given A/C. This baselining must be performed for each system installed on every A/C. The baselining must be repeated if a major maintenance action is performed on the transmission of the A/C. As one can see, the baselining effort is by no means trivial for a significant fleet of A/C. In particular, the Marine Corps alone has on the order of 238 CH-46E A/C which would require approximately 4760 flight hours of dedicated baselining.

The N3P system has also been demonstrated in all three phases of the program to operate across a range of torques. For all phases, there were at least nine torque settings where the N3P was consistently shown to operate and assess the condition of the transmission. This is also a valid goal for the program since in a military environment the A/C are operated in a manner of conditions across a wide range of regimes. A diagnostic system should adapt itself to the manner in which the A/C will be flown since military flight operations can hardly be expected to adapt themselves to the requirements of a diagnostic system.

7.1 RECOMMENDATIONS

In light of the experience gained during the course of the program, the following recommendations are made:

• Accelerometers

The microdot connectors of the Endevco 7259A-10 accelerometers proved to be quite problematic during the inflight phase of the program and the course of the flight trials. There is a major reliability versus sensitivity/bandwidth tradeoff issue that should no longer be a concern with many of the accelerometers available today. Most current accelerometers have a more than sufficient sensitivity and bandwidth for diagnostic purposes that makes the tradeoff issue become one of reliability. Accelerometers should be chosen first on the basis of their reliability and durability in the helicopter avionics environment, with sensitivity and bandwidth secondary issues. It is also recommended that the signal path from the

51

accelerometers to the diagnostic system be made as simple as possible with as few intervening connectors as possible.

• Accelerometer Mounts

The accelerometer mounts should have their vibrational response characterized prior to use. If the response has resonances or characteristics that would tend to mask the vibration signature, an alternate mount should be designed and characterized.

• Accelerometer Locations

The location of the accelerometers should be chosen to maximize the visibility of the components) that the accelerometer is chosen to cover. Interference in the signal from other components and transmission case resonances should be minimized. As an example of signal interference, the AVDS accelerometer #8 signal was dominated by the vibration from the underbody oil scavenge pump. The signals from gear box accessories and other components were effectively masked by the pump signature. To adequately determine locations that maximize the visibility of monitored components, the accelerometer locations should be determined using an instrumented survey.

• Data Quality Assurance

On-line, automated data quality assurance should be available. This data quality assurance should be both passive and active. The types of quality assurance should be chosen to ensure that the data is free from defects and artifacts, but the implementation should be chosen so that ease of use and availability for real-time troubleshooting are maximized.

• Flight Trials Performance Metrics

The performance metrics used to assess performance should be well-defined and chosen in ad- vance of the trials. The metrics should be unambiguous, easy to compute, and adequately charac- terize any performance tradeoffs related to the system. The metrics should also provide an apples-to-apples comparison between different systems that are being evaluated at the same time. This is especially true in the case where false alarm rates are a significant measure of the performance of the system. In particular, many COTS systems have a decision interval on a given component that is on the order of minutes. By contrast, the N3P has a decision interval on the complete assembly on the order of 3 seconds. In order to compensate for the differing decision intervals and component coverages, the false alarm rates calculated for the two types of systems would have to be time base normalized and component coverage normalized.

• Flight Trials Design

The design of the flight trials should be driven by the performance metrics used to quantify performance during the trials. The trials should be designed so that the metrics can be established and properly quantified. Design of the trials should also place the different systems under evaluation on an equal footing during the trials.

52

• Representative Fleet Sample

In order for the results of the trials to be statistically meaningful, the trials should encompass a greater period of time spread across a larger number of A/C. On each A/C, the systems should be evaluated for approximately 10% of the life of the transmission between rebuilds. The evaluation should take place on a representative sample of the A/C in the fleet. Approximately 5-10% of the A/C comprising the fleet should have systems installed and evaluated during the course of the trials.

• Continued Development of Neural Network Diagnostic Techniques

Test Stand, In-flight, and Flight Trials results suggest that the N3P approach appears to offer significant advantages over COTS technology, but that the approach requires further evaluation. Since the results to date have been so promising, continued development of the N3P system should be pursued. The system should be evaluated on a number of fleet A/C for a longer duration so that credible false alarm rate statistics can be established. This evaluation should take place prior to efforts to "productionize" the system.

53

APPENDIX A AIR VEHICLE DIAGNOSTIC SYSTEM SIGNAL PROCESSING AND

CLASSIFICATION ROUTINES. BOARD-LEVEL IMPLEMENTATION.

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REPORT DOCUMENTATION PAGE Form Approved OMBNo. 0704-0188

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information including suggestionsfor reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204 Arlington VA 22202-4302 and to the Office of Management and Budget, Paperwork Reduction Proiect (0704-01881. Washinntnn nr. jrwrw a *' ' 9 ' "** ***' Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503.

1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE

June 1997 4. TITLE AND SUBTITLE

AIR VEHICLE DIAGNOSTIC SYSTEM: CH-46 AFT MAIN TRANS- MISSION FAULT DIAGNOSIS—FINAL REPORT

3. REPORT TYPE AND DATES COVERED

Final 5. FUNDING NUMBERS

6. AUTHOR(S)

K. G. Church, R. R. Kolesar, M. E. Phillips, R. C. Garrido

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

Naval Command, Control and Ocean Surveillance Center (NCCOSC) RDT&E Division (NRaD) San Diego, California 92152-5001

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

Chief of Naval Operations Washington, DC 20350-2000

PE: 0603792N

8. PERFORMING ORGANIZATION REPORT NUMBER

TD2966

10. SPONSORING/MONITORING AGENCY REPORT NUMBER

11. SUPPLEMENTARY NOTES

12a. DISTRIBUTION/AVAILABILITY STATEMENT

Approved for public release; distribution is unlimited.

13. ABSTRACT (Maximum 200 words)

12b. DISTRIBUTION CODE

The goal of the helicopter drive system condition monitoring component of the Air Vehicle Diagnostic System (AVDS) program is to develop technology that will facilitate transition to "condition-based maintenance" (CBM) for these components. The specific system requirements as stated in the AVDS Execution Plan are as follows:

Detect and classify gearbox faults in real time in flight. Cope with variations between gearboxes. Perform diagnostics in a variety of flight regimes. Reduce false-alarm rates. Reduce maintenance costs and facilitate transition to CBM.

This report describes the performance of a system developed by NRaD that meets these requirements.

14. SUBJECT TERMS

Mission area: Diagnostics and Maintenance false-alarm rates helicopter gearbox maintenance diagnostics condition-based maintenance (CBM)

17. SECURITY CLASSIFICATION OF REPORT

UNCLASSIFIED

18. SECURITY CLASSIFICATION OF THIS PAGE

UNCLASSIFIED

19. SECURITY CLASSIFICATION OF ABSTRACT

UNCLASSIFIED

15. NUMBER OF PAGES

130

16. PRICE CODE

20. LIMITATION OF ABSTRACT

SAME AS REPORT

NSN 7540-01-280-5500 Standard form 298 (FRONT)

21a. NAME OF RESPONSIBLE INDIVIDUAL

R. R. Kolesar 21b. TELEPHONE (include Ares Code)

(619)553-9893 email: [email protected]

21c. OFFICE SYMBOL

Code D374

NSN 7540-01-280-5500 Standard form 298 (BACK)

INITIAL DISTRIBUTION

CodeD0012 Patent Counsel (1) CodeD0271 Archive/Stock (2) Code D0274 Library (2) CodeD0271 D. Richter (1) Code D374 R. R. Kolesar (26)

Defense Technical Information Center Fort Belvoir, VA 22060-6218 (4)

NCCOSC Washington Liaison Office Arlington, VA 22245-5200

Center for Naval Analyses Alexandria, VA 22302-0268

Navy Acquisition, Research and Development Information Center (NARDIC)

Arlington, VA 22244-5114

GIDEP Operations Center Corona, CA 91718-8000

Documents

CH-46 Aft Main Transmission Fault Diagnosis—Final Report