Piet Ephraim

Smiths Aerospace

www.smiths-aerospace.com © 2005 by Smiths Aerospace: Proprietary Data

Integrated Vehicle Health Management in Network Centric Operations

International Helicopter Safety Symposium, Montreal

September, 2005

Piet Ephraim

© 2005 by Smiths Aerospace: Proprietary Data

Outline

Network Centric Operation & its implications

Vehicle Health Management objectives and challenges

Background and Current developments

• Comprehensive health management

• On-board common computing platforms & networks

• Ground system networks

• New tools and architectures

Integrated Vehicle Health Management in the Net centric environment

Conclusions


Network Centric Operation (NCO)

NCO is a philosophy that aims to provide dispersed operations with:

• Greater speed, more precision, Fewer forces

• Information & Decision Superiority

• Shared Situational Awareness

• Interoperability

NCO includes ‘C4ISRS2’

• Command, Control, Computing, Communications

• Intelligence

• Surveillance

• Reconnaissance

• Support and Sustainment


NCO Implications

NCO implies:

• Greater reliance on maximised vehicle availability and reduced logistics footprint – benefits afforded by Health Management

NCO requires:

• Information from data

• Timely delivery of accurate, coherent and comprehensive intelligence, operational and logistics information

• Integration of sensors, decision makers, operational and support systems through networked and integrated open systems

• Adaptability and extensibility

• Increased levels of autonomy

Health Management is an integral part of Net Centric Operations


Vehicle Health Management Objectives

Increased mission readiness, effectiveness and sortie rate

Reduced downtime (advise maintenance prior to return)

Improved safety

Reduced redundancy requirements

Reduced sustainment burden & logistics footprint

Address need for autonomous & integrated on-board health management (e.g. for UAVs)

To provide the right information to the right people at the right time so that decisions can be made and actions taken


Vehicle Health Management Challenges

Flexible, open Architectures

Improved Diagnostics & Prognostics - Decision Support tools

Optimised roles of, & interaction between, on-board and off-board functions

Integration and Interoperability (sharing of monitored information)

Distribution of Data / Functionality - on-board & off-board

Autonomous (self-supporting) vehicle capability

Provide a demonstrated payback

Smiths Aerospace


Background and Current Development


HUMS - 20 Aircraft types, 2 million flight hours

Bell-Agusta BA609 Agusta-Bell AB139 Japan SH-60K

UK MoDChinook LynxSea KingApache

US ArmyUH-60L &MH-47E


At aircraft maintenance

Depot Level Fleetwide support In-depth analysis &Diagnostics

Example HUMS System

Ground System Software

Optical Blade

Tracker

Rotor Sensors

Area Mic

Control Position Sensors

Pitch Roll Heading Sensors

Hanger Bearing

Accelerometers

CG Acceleromet

er

EngineAcceleromet

ers

Rotor Sensors

RT &B Acceleromete

rsRT & BAcceleromet

ers

Rotor Azimuth

Altitude, Airspeed &

Air Temperature Sensors

Optical Blade

Tracker

On-board system


HUMS: Proven Benefits

Transmission Health Monitoring – £1.0M

Engine Health Monitoring – £200k

Aircraft Usage Monitoring – £600k

Rotor Track & Balance – £1.5M

HUMS: Proven Benefits

Increased safety

• Reduced fatal accident statistics

Significant annual savings:

• Rotor track & Balance

• Transmission Health

• Aircraft Usage

• Engine Health

Notable diagnostic successes:

• Minimised screening process

• Prevention of fleet grounding


Comprehensive Aircraft Health Systems

Doors and door actuators STRUCTURAL HEALTH

ACTUATOR HEALTH

Engine Components EDMS/IDMS

OIL CONDITIONVIBRATION USAGE IGNITOR HEALTHROTOR HEALTH

LOD

Hi-Lift systems STRUCTURAL HEALTH

Fuel & hydraulic tubes/hoses SMART VALVES CORROSION

LEAKAGEOBSTRUCTION DETECTION

Fuel Systems FUEL QUALITY

LEAKAGEPUMP HEALTH

Environmental Control

SUBSYSTEM HEALTH

Power Generation

GENERATOR HEALTH

Weapon Control & Release

SUBSYSTEM HEALTHIntegrated Avionics,

Flight Management, Data, Displays

SUBSYSTEM HEALTHLEAST DAMAGE NAV

Power Distribution

ARC FAULT DETECT

CurrentGrowth

Cable Harnesses & Connectors

ARC FAULT PROTECTIONWIRE FAULT DETECT

Airframe components

STRUCTURAL HEALTH

Utilities Management

SUBSYTEM HEALTH

Fly-by-wire flightcontrol actuators

ACTUATOR HEALTH


On-board common core computing

Common Computing Platform

• Single computing resource runs multiple applications

• Vehicle Management System for X-47 J-UCAS

• Flight Management

• Flight Control

• Fuel, Power, Engine Management

• C-130 AMP, KC-767 Tanker,MMA, X-45 J-UCAS

• Boeing 787 Dreamliner


Smiths on-board networked systems on Next-generation airliners: The Boeing 787 Dreamliner

Common core system

remote data concentrators

Common data network

Enhanced airborne flight recorder

Common computing

resource

Common core system

remote data concentrators

Common data network

The Smiths Common Core System (CCS)is the central nervous system of the aircraft


Integrated Web-enabled HUMS Ground Support

Generic capability for aircraft and land vehicles

Meets deployment / non fixed base requirement for IVHM

Full range of IVHM functions & services

Windows Groundstation

Smiths Fault Database

Remote Access

Remote Download

Smiths On-line Support Site

Data Warehouse



Lessons learned

Health & Usage Management has proven benefits in safety and maintenance

New computing and communications provide the processing power and data for comprehensive integrated vehicle health management

Existing health management functions are still heavily reliant on people to provide prognostics, decision support and learning

Further development is required to improve:

• Prognostics

• Autonomous decision making

• Extraction of information from historic data

• Automatic capture of experiential data


New tools for data fusion, data mining and reasoning

Intelligent Management of HUMS data

• CAA sponsored

• Effectiveness of AI techniques as a method of improving fault detection in helicopters

ProDAPS• USAF sponsored

• Development of tools for PHM

• Application of tools to F-15 engine

Internal Development Activity• Development of AI tools and

techniques

• Application to

• Electrostatic engine data

• Flight Operational Quality Assurance (FOQA)


ProDAPS component configuration for PHM

Fleet

Ground-basedReasoning Diagnostics

Prognostics

Embedded Reasoning

Diagnostics

Input toAutonomous

Controls

Decision Support

Recommendedactions

Autonomouscontrol

Data Mining

New knowledge

Anomaly models

Ground-based componentsapplicable to:Legacy a/cIn-development a/cFuture a/c

On-board componentsapplicable to future a/c

On-board componentsapplicable to in- dev. a/c


ProDAPS

Positioned within the OSA-CBM evolving Open System Architecture standard

• ProDAPS provides high level intelligent functions and capabilities to push Health Monitoring to true IVHM/PHM.

Current capability gap, and key target area for ProDAPS intelligent systems tools, e.g.

• Data fusion

• Automated reasoning

• Data mining (for empirical models)

Existing Smiths HUM systems provide considerable functionality in these areas.

4. Health Assessment

7. Presentation Layer

6. Decision Reasoning

5. Prognostics

1. Data Acquisition

3. Condition Monitor

2. Data Manipulation


Demonstration of ProDAPS data mining tool on helicopter MRGB bevel pinion fault

MRGB Bevel Pinion

1. Initial cluster model based on ‘healthy’ data80% of all data (first 80% of flights for each

gearbox)

18500

19000

19500

20000

20500

0 2 4 6 8 10

No. of Clusters

Sco

re

Gearbox A - 80% of all Data

0

1

2

3

4

Flight

Gearbox B - 80% of all Data

0

1

2

3

4

1 37 73 109

145

181

217

253

289

325

361

397

433

469

Flight

Clu

ster

Gearbox C - 80% of all Data

0

1

2

3

4

Flight

All data used

21000

22000

23000

24000

25000

0 2 4 6 8 10

No. of Clusters

Sco

re

Gearbox A - All data used

0123456

1 17 33 49 65 81 97 113

129

145

161

177

193

209

flight

Clu

ster

Gearbox B - All data used

0123456

1 36 71 106

141

176

211

246

281

316

351

386

421

456

491

Flight

Clu

ster

Gearbox C - All data used

0123456

1 13 25 37 49 61 73 85 97 109

121

133

145

157

Flight

Clu

ster

3. Adaptive modelling to characterise ‘trending’ data

2. Trend of faulty gearbox relative to initial ‘anomaly’ cluster

Movement relative to Cluster 4 - Learnt on 80%

-100

0

100

200

300

400

500

600

1 4 7 10 13 16 19 22 25 28 31 34 37

Gearbox A

Gearbox B

Gearbox C

6 per. Mov. Avg.(Gearbox B)

Smiths Aerospace


Future Integrated Information Systems Architecture


Concept of On-board IVHM Operation

Vehicle Sensor InformationState Detection Data

On-board Real-Time ReplanningFlight Management System

Mission Planning Flight Planning

Plan

Assess

IVHM

Health Assessment

High Level Reasoning Engine

Vehicle Capabilities

Act

Adaptive Flight Control System

Control Algorithms

Surface Control

Health Data(Vehicle Subsystems

Health Data)


Networked on-board and off-board IVHM System

Off-board Operation

Data Mining,Data Fusion

&Analysis

Components

Data Fusion

Diagnostics and

Prognostics

Data Warehouse

Decision Support

Components

Reasoning Components

On-board Operation

Anomaly Detection

Real Time Data Acquisition

Reasoning and

Decision Component

Mission Information


Conclusions

Network Centric Operation requires vehicle health information in order to achieve mission readiness goals whilst reducing logistic support.

New architectures and network centric technologies will provide a powerful framework for the exploitation, integration and distribution of vehicle health information.

The use of AI techniques has shown considerable potential for information extraction to meet the challenges of:

• Improved fault detection, diagnostics and prognostics

• Decision support, reasoning, data mining

• Improved payback through Optimal use of deployed assets

Documents

Piet Ephraim