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Turbine Engine DiagnosisOn the Reconstruction of Performance Parameters
from Engine and FADEC Measurements
RSL Electronics LTD.
November 2005
IAF
AH-64Hermes 450 UAVCH-53UH-60
Snecma
CFM56
The Challenge
Diagnosis of a twin-spool turbofan, based on pressure and temperature measurements at different stations, FADEC variables (VBV, VSV, WFM, etc.), environmental conditions.
Technique: reconstruction of station performance measures (efficiency measures, flow measures) at said stations.
The Information
Black box (OEM-supplied) thermodynamic steady-state simulator
Limited test-cell measurements of engine, used for parameter range estimation.
The Simulator
performance measures
environment conditions
Fan Eff.
Fan Flow…
LPT FlowAmb. Tmp.…
control input N1
T+P S12…
VBV…
station measurementscontroller output
The Mission
Producing a simulator reconstructor (The Perdictor)
The Simulator
performance measures
environment conditions
Fan Flow…
LPT FlowAmb. Tmp.…
control input N1
T+P S12…
VBV…
Fan Eff. Fan Eff.Fan Flow
…
LPT Flow
performance measures
The Predictor
The Goal
The Predictor as a diagnostic tool for the real engine
environment conditions
…
Amb. Tmp.…
control input N1
T+P S12…
VBV…
The Predictor
Fan Eff.Fan Flow
LPT Flow
performance measures
…
Is this a well-posed problem?
1. Existence ?
2. Uniqueness ?
3. Continuity ?
(Hadamard, 1902, 1923;
Tikhonov & Arsenin, 1977;
Morozov, 1993;
Kirsch, 1996;
Haykin, 1999)Generally: NO!In practice:
•Prior knowledge about simulation type/problem nature
•Restraints on parameter combinations/distributions
•Parameters limited to localized working conditions
For Practical Purposes: WE’LL TRY!
There exist more well-posed
(Kandariya Visvanatha Mahadeva Temple, Khajoraho, India, 10th-11th century AD)
But it works!
examples..
Predictor Implementation
The Predictor (non-linear regression) was realized on feed-forward neural networks.
Different predictor/NN for each performance parameter.
Fan Eff. Predictor
Fan Flow Predictor
LPT Eff. Predictor
Estimated Fan Eff.
Estimated Fan Flow
Estimated LPT Eff.
Contol Input
Stations P+T
FADEC Params
Env. Conditions
Data Sets
Two sets for training and testing of The Predictor.
Sets to span spaces to be encountered: Performance parameters (engine integrity
states) in all stations/cocktails thereof
Engine operating states
Environmental conditions
Working with a simulator, manufacturing synthetic data with no limits on size, distribution (independence) and quality.
Data Sets
Data sets were produced using extensive execution of the simulator under controlled input conditions, as to span the working conditions to be encountered.
Parameter Space
GeneratorThe
Simulator
Engine Control + Env. Params
Performance Params
P+T, FADEC Params
In
Out
Data Sets
Test
set
Training set
Predictor Training/Testing
Test
set
Training set
A Predictor (e.g. LPT
Eff.)
Data Sets
Train Env+P+T+FADEC Params
Train Performance Params
Predicted Performance Params
Test Env+P+T+FADEC Params
Test Performance Params +
-
Σ Test Error
Train Loop
Test Loop
Data Sets Distribution Strategy
Environment and engine control (e.g. N1) inputs were chosen to be uniformly distributed in ranges obtained from test-cell data.
0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.60
200
400
600
800
1000
1200ZTAMBA
e.g. ambient temperature histogram
Data Sets Distribution Strategy
Performance parameters (efficiencies and flows) were chosen from two distributions, representing either “normal behavior” or “faulty behavior”
“normal behavior” consisted of most values near their nominal expected value (scaler = 1.0), with exponential distribution with parameter μ= 0.001 to the “left” of 1, accounting for less than ideal performance.
“faulty behavior” was chosen from a uniform distribution in the scaler range 0.95 – 1.0.
Modeled parameter was uniform at interval 0.95-1.0 according to target reconstruction range.
Data Sets Distribution Strategy
e.g. Fan isentropic efficiency HISTOGRAM
0.95 0.955 0.96 0.965 0.97 0.975 0.98 0.985 0.99 0.995 10
0.5
1
1.5
2
2.5
3
3.5
4x 10
4 ZSE13D
scaler values
Fig. 2.
ZSE13D histogram, from Ptest
There’s a “tail” here, when
parameter was faulty
Normal cases 0.95 0.955 0.96 0.965 0.97 0.975 0.98 0.985 0.99 0.995 1
200
400
600
800
1000
1200
1400
1600
1800
ZSE13D
See fault distribution strategy presented below
(Pf = 0.5)
Fault Distribution Strategy
Test set fault distribution assumed fault occurrence independence, resulting in sets with decreasing frequency of occurrence of multiple simultaneous faults.
The number of simultaneous faulted parameters (not including the modeled parameter) was chosen at each data point, such that if a probability for a single faulted parameter is Pf, then the probability for n faults was Pf
n. Pessimistic values used for Pf were 0.1 and 0.5.
Fault Distribution Strategy
Fault distribution HISTOGRAM for Pf = 0.5
0 5 10 15 20 250
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5x 10
4
Number of simultaneous faults
His
togra
m c
ount Average fault
number is 1 (for Pf
= 0.1 average is ~0.11)
Results
e.g. Fan flow scaler (Pf = 0.5)
-6 -4 -2 0 2 4 6
x 10-3
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Ttest
- Tnet
his
togra
m c
ount
0.5 P_FAULT SW12RA STD DIFF: 0.00036467 [0.036467% res.]
90th Percentile Error: 0.050%
Graph shows reconstructed parameter value VS original value
Results
e.g. Booster isentropic eff. scaler (Pf = 0.5)
-4 -3 -2 -1 0 1 2 3
x 10-3
0
5000
10000
15000
Ttest
- Tnet
his
togra
m c
ount
0.5 P_FAULT SE23DA STD DIFF: 0.00019 [0.019% res.]
90th Percentile Error: 0.025%
Graph shows reconstructed parameter value VS original value
Results
e.g. LPT flow scaler (Pf = 0.5)
-0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.050
2000
4000
6000
8000
10000
12000
Ttest
- Tnet
his
togra
m c
ount
0.5 P_FAULT SW49RA STD DIFF: 0.0036329 [0.36329% res.]
90th Percentile Error: 0.377%
Graph shows reconstructed parameter value VS original value
Conclusions
The regression attempt has produced results whose 90th percentile deviation averaged0.156%, exceeding requirements for detecting plausible fault performance degradations of 1% –2%.
The less precise regression has been achieved for LPT efficiency and flow parameters
This feasibility study proved the capability of utilizing machine learning techniques to reproduce actual engine status from accessible measurements, at least in the simulator context.
ANY QUESTIONS?