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

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Integrated Vehicle Health Management in Network
Centric OperationsInternational Helicopter
Safety Symposium, MontrealSeptember, 2005

• Piet Ephraim

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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

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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

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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
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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
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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

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Background and Current Development
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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
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Example HUMS System
On-board system
At aircraft maintenance
Depot Level Fleetwide support In-depth analysis
Diagnostics
Ground System Software
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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

Transmission Health Monitoring 1.0M
Engine Health Monitoring 200k
Aircraft Usage Monitoring 600k
Rotor Track Balance 1.5M
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Comprehensive Aircraft Health Systems
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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

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

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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

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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)

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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
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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.

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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
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Future Integrated Information Systems Architecture
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Concept of On-board IVHM Operation
Vehicle Sensor Information State Detection Data
Health Data (Vehicle Subsystems Health Data)
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Networked on-board and off-board IVHM System
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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