Remaining Life Prognostics for an Army Ground Vehicle System

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Richard Heine Donald Barker

Abstract

Reliability is a key parameter for the development of safe and effective military vehicles with a reasonable life cycle cost. One innovative technology that is being promoted in the Department of Defense is the use of Health and Usage Monitoring Systems and remaining life prognostics to improve reliability and availability. The feasibility of using data collected from a limited set of existing and simple add-on sensors to make fatigue damage estimations on a complexly loaded component within a military wheeled vehicle system was investigated. Methods for identifying the critical inputs for fatigue estimation are evaluated and compared. A baseline physics of failure analysis was performed on an example component to evaluate the proposed HUMS algorithms and demonstrate the accuracy of resulting fatigue predictions.

How to Cite

Heine, R. ., & Barker, D. . (2010). Remaining Life Prognostics for an Army Ground Vehicle System. Annual Conference of the PHM Society, 2(1). https://doi.org/10.36001/phmconf.2010.v2i1.1866
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Keywords

health monitoring, military vehicles, physics of failure, prognostics, multiaxial fatigue

References
Anonymous. 2003. Defense Acquisitions: Assessmenst of Major Weapons Programs. United States General Accounting Office Report to Congressional Committees. GAO-03-476.
Anonymous. 2004. Prognostics and Health Management for the JSF Program. Sound and Vibration, 38 (5): 8.
Barone, S., Ambrosio, P. D., Erto, P., 2006, A Statistical Monitoring Approach for Automotive On-board Diagnostic Systems, Quality and Reliability Engineering International, 23: 565-575.
Bechhoefer, E., A. Bernhard. 2004. HUMS optimal Weighting of Condition Indicators to Determine the Health of a Component. Annual Forum Proceeding – American Helicopotor Society. 1: 1186-1191.
Brannen, K. 10 March 2010. U.S. Army Plan Takes Fire From Congress, DoD GAO. DefenseNews.
Ellerbrock, P. J., Z. Halmos, P. Shanthakumaran. 1999. Development of New Health and Usage Monitoring System Tools Using a NASA/Military Rotorcraft. Annual Forum Proceedings - American Helicopter Society. 2: 2337-2348.
Evans, A. 2002. Flight Deck Indication of Health Monitoring Data - A Critique Proceeding of the Institution of Mechanical Engineers Part G – Journal of Aerospace Engineering. 216 (G5): 249- 257.
Gandhi, T., Chang, R., Trivedi, M. M., 2007, Video and Seismic Sensor-Based Structural Health Monitoring: Framework, Algorithms, and Implementation, IEEE Transactions on Intelligent Transportation Systems, 8, (2) 169-180.
Gordon, A. C. 1991. Development to Production of an Integrated Health and Usage Monitoring System for Helicopters. Aerospace Technology Conference and Exposition SAE Technical Paper Series. 1-31.
Greitzer, F. L., R. A. Pawlowski. 2002. Embedded Prognostics Health Monitoring. Proceedings of the 48th International Instrumentation Symposium. 48: 301-310.
Heine, R., Barker, D. 2007. Simplified Terrain Identification and Component Fatigue Damage Estimation Model for use in a Health and Usage Monitoring System. Microelectronics Reliability, doi:10.1016/j.microrel.2007.02.017.
Heine, R., Barker, D. 2008. Health and Usage Monitoring Algorithm Based on Terrain Identification for Mechanical Components on an Army Ground Vehicle System. Journal of the IEST. 51 (2): 31-41.
Heine, R., Barker, D. 2009. Acceleration-Based Remaining Life Prognostics for Terrain Loaded Components on an Army Ground Vehicle System. Journal of IEST. 52 (2): 40-49.
Hunt, S, R., I.G. Hebden. 2001. Validation of the Eurofighter Typhoon structural health and usage monitoring system. Smart Materials & Structures 10 (3): 497-503.
Jarrell, D. B., L. J. Bond. 2006. Physics-Based Prognostics for Optimizing Plant Operations. Sound and Vibration. 40 (2):12-15.
Li, C. J., A. Ray. 1995. Neural-Network Representation of Fatigue Damage Dynamics. Smart Materials & Structures. 4 (2): 126-133.
Martin, W., Collingwood, G., Barndt, G., 1999, “Structural Life Monitoring of the V-22,” Annual Forum Proceedings – American Helicoptor Society, 2, 2373-2386.
Mourna, J. D., Steffen, V., 2006, “Impedance-based Health Monitoring for Aeronautic Structures using Statistical Meta-modeling,” Journal of Intelligent Material Systems and Structures, 17, 1023-1036.
Ng, H. K., Chen, R. H., Speyer, J. L., 2006, A Vehicle Health Monitoring System Evaluated Experimentally on a Passenger Vehicle, IEEE Transactions on Control Systems Technology, 14 (5), 854-870.
Rabeno, E., M. Bounds. 2009. Condition Based Maintenance of Military Ground Vehicles. 2009 IEEE Aerospace Conference.
Schuster, E., K. C. Gross. 2004. Multi-Frequency Sinusoidal Perturbation Method for Dynamic Characterization of Multi-Processor Computer Servers. Sun Microsytems Laboratories Technical Report Series. SMLI TR-2004-130: 1-22.
Trammel, C., G. Vossler, M. Feldmann. 1997. UK Ministry of Defense Generic Health and Usage Monitoring System (GenHUMS). Aircraft Engineering and Aerospace Technology, 69 (5):414+.
Yan, J. H., J. Lee. 2005. Degradation Assessment and Fault Modes Classification Using Logistic Regression. Journal of Manufacturing Science and Engineering – Transaction of the ASME. 127 (4): 912-914.
Section
Technical Papers