1. 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

2. © 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

3. © 2005 by Smiths Aerospace: Proprietary Data 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. © 2005 by Smiths Aerospace: Proprietary Data 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

5. © 2005 by Smiths Aerospace: Proprietary Data 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. © 2005 by Smiths Aerospace: Proprietary Data 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. Smiths Aerospace www.smiths-aerospace.com © 2005 by Smiths Aerospace: Proprietary Data Background and Current Development

8. © 2005 by Smiths Aerospace: Proprietary Data HUMS - 20 Aircraft types, 2 million flight hours Bell-Agusta BA609Agusta-Bell AB139Japan SH-60K UK MoD Chinook Lynx Sea King Apache US Army UH-60L & MH-47E

9. © 2005 by Smiths Aerospace: Proprietary Data 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 Acceleromete rs CG Acceleromet er Engine Acceleromet ers Rotor Sensors RT &B Acceleromet ers Rotor Azimuth Altitude, Airspeed & Air Temperatur e Sensors Optical Blade Tracker On-board system

10. © 2005 by Smiths Aerospace: Proprietary Data 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

11. © 2005 by Smiths Aerospace: Proprietary Data 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 SUBSYSTEM HEALTH 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. © 2005 by Smiths Aerospace: Proprietary Data 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. © 2005 by Smiths Aerospace: Proprietary Data 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

14. © 2005 by Smiths Aerospace: Proprietary Data 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

15. © 2005 by Smiths Aerospace: Proprietary Data

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. © 2005 by Smiths Aerospace: Proprietary 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)

18. © 2005 by Smiths Aerospace: Proprietary Data ProDAPS component configuration for PHM Fleet Ground-based Reasoning Diagnostics Prognostics Embedded Reasoning Diagnostics Input to Autonomous Controls Decision Support Recommended actions Autonomous control Data Mining New knowledge Anomaly models Ground-based components applicable to: Legacy a/c In-development a/c Future a/c On-board components applicable to future a/c On-board components applicable to in- dev. a/c

19. © 2005 by Smiths Aerospace: Proprietary Data 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. © 2005 by Smiths Aerospace: Proprietary Data Demonstration of ProDAPS data mining tool on helicopter MRGB bevel pinion fault MRGB Bevel Pinion 1. Initial cluster model based on ‘healthy’ data 80% of all data (first 80% of flights for each gearbox) 18500 19000 19500 20000 20500 0246810 No. of Clusters Score Gearbox A - 80% of all Data 0 1 2 3 4 Flight Gearbox B - 80% of all Data 0 1 2 3 4 1 3773 109145181217253289325361397433469 Flight Cluster Gearbox C - 80% of all Data 0 1 2 3 4 Flight All data used 21000 22000 23000 24000 25000 0246810 No. of Clusters Score Gearbox A - All data used 0 1 2 3 4 5 6 1 17 33 49 65 8197 113 129 145161177193 209 flight Cluster Gearbox B - All data used 0 1 2 3 4 5 6 1 36 71 106141176211246 281316 351 386421 456491 Flight Cluster Gearbox C - All data used 0 1 2 3 4 5 6 1 132537 49 61 73 8597 109 121133 145 157 Flight Cluster 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 0 100 200 300 400 500 600 147 10131619222528313437 Gearbox A Gearbox B Gearbox C 6 per. Mov. Avg. (Gearbox B)

21. Smiths Aerospace www.smiths-aerospace.com © 2005 by Smiths Aerospace: Proprietary Data Future Integrated Information Systems Architecture

22. © 2005 by Smiths Aerospace: Proprietary Data Concept of On-board IVHM Operation Vehicle Sensor Information State Detection Data On-board Real-Time Replanning Flight 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)

23. © 2005 by Smiths Aerospace: Proprietary 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

24. © 2005 by Smiths Aerospace: Proprietary Data 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