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Piet Ephraim

Integrated Vehicle Health Management in Network Centric Operations International Helicopter Safety Symposium, Montreal September, 2005. Piet Ephraim. Outline. Network Centric Operation & its implications Vehicle Health Management objectives and challenges Background and Current developments

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Piet Ephraim

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  1. Integrated Vehicle Health Management in Network Centric OperationsInternational Helicopter Safety Symposium, MontrealSeptember, 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: • 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. EngineAccelerometers RT &B Accelerometers Rotor Sensors RT & BAccelerometers Hanger Bearing Accelerometers Rotor Azimuth Optical Blade Tracker Rotor Sensors Optical Blade Tracker CG Accelerometer Altitude, Airspeed & Air Temperature Sensors Control Position Sensors Area Mic Pitch Roll Heading Sensors Example HUMS System 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. Engine ComponentsEDMS/IDMSOIL CONDITIONVIBRATION USAGE IGNITOR HEALTHROTOR HEALTHLOD Doors and door actuatorsSTRUCTURAL HEALTH ACTUATOR HEALTH Hi-Lift systemsSTRUCTURAL HEALTH Fuel SystemsFUEL QUALITYLEAKAGEPUMP HEALTH Fuel & hydraulic tubes/hoses SMART VALVES CORROSIONLEAKAGEOBSTRUCTION DETECTION Environmental Control SUBSYSTEM HEALTH Power Generation GENERATOR HEALTH Weapon Control & Release SUBSYSTEM HEALTH Integrated Avionics, Flight Management, Data, Displays SUBSYSTEM HEALTHLEAST DAMAGE NAV Cable Harnesses & Connectors ARC FAULT PROTECTIONWIRE FAULT DETECT Power Distribution ARC FAULT DETECT Utilities Management SUBSYTEM HEALTH Current Growth Airframe components STRUCTURAL HEALTH Fly-by-wire flightcontrol actuators ACTUATOR HEALTH Comprehensive Aircraft Health Systems

  12. 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 On-board common core computing

  13. Common computing resource Common data network Common core system remote data concentrators Enhanced airborne flight recorder Common data network Common core system remote data concentrators The Smiths Common Core System (CCS)is the central nervous system of the aircraft Smiths on-board networked systems on Next-generation airliners: The Boeing 787 Dreamliner

  14. Remote Download Remote Access Windows Groundstation Data Warehouse Smiths On-line Support Site Smiths Fault Database 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

  15. 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

  16. 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)

  17. ProDAPS component configuration for PHM Ground-based Reasoning Diagnostics Prognostics On-board components applicable to in- dev. a/c Diagnostics Embedded Reasoning 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

  18. 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. 7. Presentation Layer 6. Decision Reasoning 5. Prognostics 4. Health Assessment 3. Condition Monitor 2. Data Manipulation 1. Data Acquisition ProDAPS

  19. All data used Gearbox A - All data used 25000 6 5 24000 4 Score Cluster 23000 3 2 22000 1 21000 0 1 17 33 81 97 65 49 0 2 4 6 8 10 113 129 145 161 177 193 209 No. of Clusters flight Gearbox B - All data used Gearbox C - All data used 6 6 5 5 4 4 Cluster Cluster 3 3 2 2 1 1 0 0 1 1 36 71 73 85 97 13 25 37 49 61 109 106 141 176 211 246 281 316 351 386 421 456 491 121 133 145 157 Flight Flight Gearbox A - 80% of all Data 80% of all data (first 80% of flights for each gearbox) 4 Movement relative to Cluster 4 - Learnt on 80% 20500 3 20000 600 2 19500 Score 19000 1 500 18500 0 0 2 4 6 8 10 No. of Clusters Flight 400 Gearbox A Gearbox B 300 Gearbox B - 80% of all Data Gearbox C - 80% of all Data Gearbox C 4 4 200 3 3 6 per. Mov. Avg. 2 (Gearbox B) Cluster 100 2 1 1 0 0 1 37 73 1 4 7 109 145 181 217 253 289 325 361 397 433 469 10 13 16 19 22 25 28 31 34 37 0 Flight -100 Flight Demonstration of ProDAPS data mining tool on helicopter MRGB bevel pinion fault 1. Initial cluster model based on ‘healthy’ data MRGB Bevel Pinion 2. Trend of faulty gearbox relative to initial ‘anomaly’ cluster 3. Adaptive modelling to characterise ‘trending’ data

  20. Future Integrated Information Systems Architecture

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

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

  23. 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

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