1. Military Diagnostics, Prognostics, and Logistics - A Way Forward Graham Tebby – Chief Engineer (presenter) graham.tebby@pi-shurlok.com David Stamm – Principal Engineer (author) david.stamm@pi-shurlok.com

2. MAE Conference 2009 Copyright © 2009 Presentation Structure Who is Pi Shurlok & what can we supply to the discussion? Definitions and terminology Problem statements and observations Scope of proposed solutions Future systems architecture Implications to end-users Future work Summary Whitepaper available at www.pi-shurlok.com

3. MAE Conference 2009 Copyright © 2009 Who is Pi Shurlok? We have a long history of working in the commercial world  Over 15 years developing systems for US medium & heavy trucks  Supplied systems / technology to a majority of the world’s car makers  Involved in architecture designs for complex vehicles  e.g. luxury and high performance sports cars  History of developing controls for high reliability systems And a recent history of working on military ground transportation  Involved in a number of active military programmes for the last 4 years  Providing electronics for military vehicles  Many thousands of miles of testing completed What can we supply to discussion?  A fresh pair of eyes looking at military diagnostic / prognostic architectures

4. MAE Conference 2009 Copyright © 2009 Definitions & Terminology - Diagnostics Diagnostics  A system that estimates the current status of systems Examples:  Low Oil Pressure Lamp (simple)  Open Circuit Monitor (medium)  Sensor rationality in redundant systems (complex)

5. MAE Conference 2009 Copyright © 2009 Definitions & Terminology - Prognostics Prognostics  A system that estimates the future status of systems Examples:  Current draw by an electric motor (simple)  Air filter capacity (medium)  Class I & II hydraulic circuit leaks (complex)

6. MAE Conference 2009 Copyright © 2009 Problem Statements & Observations Problem:The military wants to reduce the logistics tail costs and more efficiently use resources for war-fighting. Solution:Introduce comprehensive system diagnostics and prognostics to enable condition based maintenance (CBM and CBM+) OBSERVATIONS Observations indicate the proposed solutions are not leveraging COTS technology and intellectual property. Observations indicate many proposed solutions are bespoke designs requiring large development costs and producing niche solutions. Effort to determine percent life remaining is consuming development and testing budgets.

7. MAE Conference 2009 Copyright © 2009 Concerns with currently observed solutions DV / PV testing costs to support percent life estimates are in the millions of dollars for a total vehicle. This does not include the development costs for the software algorithms for calculation of percent life remaining. Algorithm development for percent life is a complex topic that is unique to each vehicle platform or vehicle family. Mathematical models estimating percent life are highly dependent on the duty cycle they were “trained” with. Changing the duty cycle often invalidates the percent life calculation. Additional capital expenditure when system evolves or changes. Not a plug and play solution. Is percent life the right unit or metric for all systems under diagnostics or prognostics? Summary: cost, complexity, maintainability, and flexibility concerns make the observed systems impractical in large volumes.

8. MAE Conference 2009 Copyright © 2009 Scope of whitepaper limited to on-vehicle architecture

9. MAE Conference 2009 Copyright © 2009 Future Systems Architecture Key Facets  Move away from complex percent life calculations based on historical performance of other components.  Focus on real-time monitoring of data for trends, variation, and or deviations for the actual system of interest.  COTS-IP from commercial OBD experts on monitor design & architecture  Regression of raw data on-vehicle  Only store & transmit regressed data  Common diagnostics and prognostics communication interface  COTS-IP from commercial vehicle industry  Migrate to an centralized system approach. No overlay / appliqué monitoring systems. No HUMS (Health and Utilization Monitoring System)

10. MAE Conference 2009 Copyright © 2009 Primary Assets of Proposed Architecture  Minimize off-vehicle communications bandwidth requirements.  Does not require extensive test-to-destruction data.  Data regression in real-time which reduces memory needs: only store regressed data.  Improved fidelity of results since regression is not encumbered by resolution or off-vehicle communications sampling rate.  Less risk in making incorrect decisions due to time-shifted data, versus overlaying multiple data streams after the fact.  Data is an objective measurement of a system, not an abstract percent life calculation.  Diagnostic and prognostic algorithms are very similar, if not identical.

11. MAE Conference 2009 Copyright © 2009 Primary Liabilities of Proposed Architecture  Not all systems can be directly monitored.  End-user has no control over prognostic algorithms for the regression of data.  Deployment of enhancements or updates to prognostic systems is multiplied by the number of vehicles fielded.  Computational overhead for a given system increases to handle the on- board data regression and storage.

12. MAE Conference 2009 Copyright © 2009 Components of diagnostics & prognostics – future architecture

13. MAE Conference 2009 Copyright © 2009 Implications to end user of diagnostics & prognostics Maintenance  Only perform maintenance on vehicles that require it  Less time maintaining vehicles that don’t need it  Fewer breakdowns due to incorrect modelling of percentage lifetime.  Fewer people required for maintenance on average  Workload more difficult to plan or anticipate Logistics  Reduce the strain on service parts acquisition.  Improves efficiency in transport & mobility Mission Planning  Hand-select vehicles based on their suitability and health

14. MAE Conference 2009 Copyright © 2009 Future Work Intra-vehicle Communications Architecture  The military supports far too many electronic communications standards and protocols. No economy of scale. Stifles competition for smaller companies.  Mil-specs are lagging with regard to electronics and communications protocols.  Leverage of J1939 perhaps? Must adopt a standard for all electronic systems to report diagnostic and prognostic information. Centralization of Systems  Cost benefits of considering diagnostics and prognostics in the initial vehicle design is significant.  Overlay / appliqué systems are costly to maintain and are a stop-gap measure only. Military Standardization  Drive standardization for core electrical devices.  Costs to produce militarized electronics is high.

15. MAE Conference 2009 Copyright © 2009 Summary Many technologies already exist (COTS) that can be leveraged to build a military solution for diagnostics & prognostics. Military and commercial vehicle designers perform vehicle development along very similar paths. There is good cross-over in the mechanical arena, but relatively little cross-over has been observed in the electronic / control domains. Integration of these systems at the point of vehicle design, versus overlay / appliqué systems must be performed. Standardisation of the diagnostics & prognostics communications infrastructure is required. Providing a solid vehicle level methodology will make a more robust solution for end-users.