Smiths Aerospace Integrated Vehicle Health Management in Network

Smiths Aerospace Integrated Vehicle Health Management in Network Centric Operations International Helicopter Safety Symposium, Montreal September, 2005 Piet Ephraim www. smiths-aerospace. com © 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 © 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 ‘C 4 ISRS 2’ • Command, Control, Computing, Communications • Intelligence • Surveillance • Reconnaissance • Support and Sustainment © 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 © 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 © 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 © 2005 by Smiths Aerospace: Proprietary Data

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

HUMS - 20 Aircraft types, 2 million flight hours Bell-Agusta BA 609 Agusta-Bell AB 139 US Army UH-60 L & MH-47 E Japan SH-60 K UK Mo. D Chinook Lynx Sea King Apache © 2005 by Smiths Aerospace: Proprietary Data

Example HUMS System Rotor Sensors RT & B Acceleromet ers On-board system Engine Acceleromet ers RT &B Acceleromete rs Hanger Bearing Rotor Acceleromete rs Azimuth Optical Blade Tracker Altitude, Airspeed & Air Temperatur e Sensors Area Mic Control Position Sensors CG Acceleromet er Pitch Roll Heading Sensors Optical Blade Tracker Rotor Sensors At aircraft maintenance Ground System Software Depot Level Fleetwide support In-depth analysis & Diagnostics © 2005 by Smiths Aerospace: Proprietary Data

HUMS: Proven Benefits Increased safety • Reduced fatal accident statistics Significant annual savings: Aircraft Usage Monitoring – £ 600 k • Rotor track & Balance • Transmission Health • Aircraft Usage Engine Health Monitoring – £ 200 k Transmission Health Monitoring – £ 1. 0 M Rotor Track & Balance – £ 1. 5 M • Engine Health Notable diagnostic successes: • Minimised screening process • Prevention of fleet grounding © 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 Systems FUEL QUALITY LEAKAGE PUMP HEALTH Fuel & hydraulic tubes/hoses SMART VALVES CORROSION LEAKAGE OBSTRUCTION DETECTION 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 Utilities Management SUBSYTEM HEALTH Fly-by-wire flight control actuators ACTUATOR HEALTH Airframe components STRUCTURAL HEALTH © 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 © 2005 by Smiths Aerospace: Proprietary Data

Smiths on-board networked systems on Next-generation airliners: The Boeing 787 Dreamliner Common data network Common computing resource Common core system remote data concentrators Enhanced airborne flight recorder Common core system remote data concentrators Common data network The Smiths Common Core System (CCS) is the central nervous system of the aircraft © 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 Remote Download Remote Access Windows Groundstation Data Warehouse Smiths Fault Database Smiths On-line Support Site © 2005 by Smiths Aerospace: Proprietary Data

© 2005 by Smiths Aerospace: Proprietary Data

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 © 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 Pro. DAPS • 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) © 2005 by Smiths Aerospace: Proprietary Data

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

Pro. DAPS Positioned within the OSA-CBM evolving Open System Architecture standard • Pro. DAPS provides high level intelligent functions and capabilities to push Health Monitoring to true IVHM/PHM. Current capability gap, and key target area for Pro. DAPS intelligent systems tools, e. g. • Data fusion • Data mining (for empirical models) 6. Decision Reasoning 5. Prognostics 4. Health Assessment Automated reasoning • 7. Presentation Layer Existing Smiths HUM systems provide considerable functionality in these areas. 3. Condition Monitor 2. Data Manipulation 1. Data Acquisition © 2005 by Smiths Aerospace: Proprietary Data

Demonstration of Pro. DAPS data mining tool on helicopter MRGB bevel pinion fault 1. Initial cluster model based on ‘healthy’ data Gearbox A - 80% of all Data 80% of all data (first 80% of flights for each gearbox) 4 20500 3 Score 20000 2 19500 19000 1 18500 0 0 2 4 6 8 10 No. of Clusters Flight Gearbox B - 80% of all Data Gearbox C - 80% of all Data 3 3 2 2 1 469 433 397 361 325 289 253 217 181 145 1 73 1 0 109 MRGB Bevel Pinion 37 Cluster 4 4 0 Flight 2. Trend of faulty gearbox relative to initial ‘anomaly’ cluster Flight 3. Adaptive modelling to characterise ‘trending’ data All data used 193 209 177 145 161 10 129 8 97 6 113 4 81 2 Gearbox B 157 145 133 121 97 109 85 73 61 49 37 25 13 6 5 4 3 2 1 0 1 491 456 421 386 351 Cluster Gearbox C - All data used Cluster Flight 316 281 246 211 176 141 71 36 37 34 31 28 25 22 19 13 16 7 10 4 1 0 6 5 4 3 2 1 0 1 6 per. Mov. Avg. (Gearbox B) 100 flight Gearbox B - All data used Gearbox C 200 -100 No. of Clusters 106 300 0 Gearbox A 65 21000 400 33 23000 22000 500 6 5 4 3 2 1 0 49 24000 Cluster Score 600 17 Gearbox A - All data used 25000 1 Movement relative to Cluster 4 - Learnt on 80% Flight © 2005 by Smiths Aerospace: Proprietary Data

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

Concept of On-board IVHM Operation Vehicle Sensor Information State Detection Data Assess Act Adaptive Flight Control System IVHM Control Algorithms High Level Reasoning Engine Surface Control Plan Health Assessment Vehicle Capabilities Health Data (Vehicle Subsystems Health Data) On-board Real-Time Replanning Flight Management System Mission Planning Flight Planning © 2005 by Smiths Aerospace: Proprietary Data

Networked on-board and off-board IVHM System Real Time Data Acquisition Anomaly Detection Data Fusion Diagnostics and Prognostics Mission Information Reasoning and Decision Component On-board Operation Decision Support Components Reasoning Components Data Mining, Data Fusion & Analysis Components Data Warehouse Off-board Operation © 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 © 2005 by Smiths Aerospace: Proprietary Data