1. Integrated Vehicle Health Management in Network Centric Operations International Helicopter Safety Symposium, Montreal September, 2005
Piet Ephraim

2. 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

3. 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

4. NCO Implications NCO implies: NCO requires:
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

5. 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

6. 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

7. Background and Current Development

8. HUMS - 20 Aircraft types, 2 million flight hours
Bell-Agusta BA609
Agusta-Bell AB139
Japan SH-60K
UK MoD
Chinook Lynx
Sea King
Apache
US Army
UH-60L &
MH-47E

9. Example HUMS System On-board system At aircraft maintenance
Optical Blade Tracker
Rotor Sensors
Area Mic
Control Position Sensors
Pitch Roll Heading Sensors
Hanger Bearing Accelerometers
CG Accelerometer
Engine Accelerometers
RT &B Accelerometers
RT & B Accelerometers
Rotor Azimuth
Altitude, Airspeed & Air Temperature Sensors
On-board system
At aircraft
maintenance
Depot Level
Fleetwide support
In-depth analysis &
Diagnostics
Ground System Software

10. HUMS: Proven Benefits 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
Aircraft Usage
Monitoring – £600k
Engine Health
Monitoring – £200k
Transmission Health
Monitoring – £1.0M
Rotor Track & Balance
– £1.5M

11. Comprehensive Aircraft Health Systems
Doors and door actuators STRUCTURAL HEALTH ACTUATOR HEALTH
Engine Components EDMS/IDMS OIL CONDITION VIBRATION USAGE IGNITOR HEALTH ROTOR HEALTH LOD
Hi-Lift systems STRUCTURAL HEALTH
Fuel & hydraulic tubes/hoses SMART VALVES CORROSION LEAKAGE OBSTRUCTION DETECTION
Fuel Systems FUEL QUALITY LEAKAGE PUMP HEALTH
Environmental Control
SUBSYSTEM HEALTH
Power Generation
GENERATOR HEALTH
Weapon Control & Release
Integrated Avionics, Flight Management, Data, Displays
SUBSYSTEM HEALTH LEAST DAMAGE NAV
Power Distribution
ARC FAULT DETECT
Current
Growth
Cable Harnesses & Connectors
ARC FAULT PROTECTION WIRE FAULT DETECT
Airframe components
STRUCTURAL HEALTH
Utilities Management
SUBSYTEM HEALTH
Fly-by-wire flight control actuators
ACTUATOR HEALTH

12. 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

13. 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
The Smiths Common Core System (CCS) is the central nervous system of the aircraft

14. 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

16. 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

17. 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)

18. ProDAPS component configuration for PHM
Ground-based
Reasoning
Diagnostics
Prognostics
On-board components
applicable to in- dev. a/c
Embedded
Reasoning
Diagnostics
Input to
Autonomous
Controls
Decision
Support
Recommended
actions
Ground-based components
applicable to:
Legacy a/c
In-development a/c
Future a/c
Fleet
Autonomous
control
Data
Mining
New knowledge
Anomaly models
On-board components
applicable to future a/c

19. 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

20. Demonstration of ProDAPS data mining tool on helicopter MRGB bevel pinion fault
1. Initial cluster model based on ‘healthy’ data
80% of all data (first 80% of flights for each
gearbox)
18500
19000
19500
20000
20500 2 4 6 8 10
No. of Clusters
Score
Gearbox A - 80% of all Data 1 3
Flight
Gearbox B - 80% of all Data 37 73
109
145
181
217
253
289
325
361
397
433
469
Cluster
Gearbox C - 80% of all Data
MRGB Bevel Pinion
2. Trend of faulty gearbox relative to initial ‘anomaly’ cluster
3. Adaptive modelling to characterise ‘trending’ data
All data used
21000
22000
23000
24000
25000 2 4 6 8 10
No. of Clusters
Score
Gearbox A - All data used 1 3 5 17 33 49 65 81 97
113
129
145
161
177
193
209
flight
Cluster
Gearbox B - All data used 36 71
106
141
176
211
246
281
316
351
386
421
456
491
Flight
Gearbox C - All data used 13 25 37 61 73 85
109
121
133
157
Movement relative to Cluster 4 - Learnt on 80%
-100
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)

21. Future Integrated Information Systems Architecture

22. Concept of On-board IVHM Operation
Vehicle Sensor Information
State Detection Data
Act
Adaptive Flight
Control System
Control
Algorithms
Surface
Assess
IVHM
Health
Assessment
High Level
Reasoning Engine
Vehicle
Capabilities
On-board Real-Time Replanning
Flight Management System
Mission
Planning
Flight Planning
Plan
Health Data
(Vehicle Subsystems
Health Data)

23. Networked on-board and off-board IVHM System
Operation
Data Mining,
Data Fusion
&
Analysis
Components
Diagnostics
and
Prognostics
Data Warehouse
Decision
Support
Reasoning
On-board
Anomaly
Detection
Real Time Data
Acquisition
Decision Component
Mission
Information

24. 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