Some Diagnostic and Prognostic Methods for Components Supporting Electrical Energy Management in a Military Vehicle

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Guillaume Bastard

Abstract

This work investigates the field of Integrated Vehicle Health Management (IVHM) and more specifically on the components which are producing or consuming electricity. Firstly, diagnostic and prognostic characteristics are defined. This allows later, from the mapped characteristics, to sort the most relevant methods for critical components. The mapping leads finally to define some scientific issues to be solved in order to improve the diagnostic and prognostic of such components.

How to Cite

Bastard, G. (2014). Some Diagnostic and Prognostic Methods for Components Supporting Electrical Energy Management in a Military Vehicle. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1458
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Keywords

diagnostics and prognostics, Electronic PHM, IVHM

References
Amirat, Y., Choqueuse, V., & Benbouzid, M. E. H. (2010). Wind turbines condition monitoring and fault diagnosis using generator current amplitude demodulation. In Energy Conference and Exhibition (EnergyCon), 2010 IEEE International (pp. 310–315).
Balaban, E., Narasimhan, S., Daigle, M., Celaya, J., Roychoudhury, I., & Saha, B. (2010). A Mobile Robot Testbed for Prognostics-Enabled Autonomous Decision Making. In Annual conference of the prognostics and health management society (pp. 1–16).
Baysse, C., Bihannic, D., Gegout-petit, A., & Prenat, M. (2013). Maintenance Optimisation of Optronic Equipment. In Prognostics and System Health Management Conference (Vol. 33, pp. 709–714). doi:10.3303/CET1333119
Benedettini, O., Baines, T. S., Lightfoot, H. W., & Greenough, R. M. (2009). State-of-the-art in integrated vehicle health management. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 223(2), 157–170. doi:10.1243/09544100JAERO446
Byington, C. S., Roemer, M. J., Kacprzynski, G. J., & Drive, T. P. (2002). Prognostic Enhancements to Diagnostic Systems for Improved Condition-Based Maintenance 1. In Aerospace Conference Proceedings.
Chen, H. (2011). Predicting the Remaining Useful Life of Lithium- ion Batteries with Active Learning and Good- Turing Usage Profile Estimation. In PHM workshop on battery.
Didier, G., Ternisien, E., Caspary, O., & Razik, H. (2007). A New Approach to Detect Broken Rotor Bars in Induction Machines by Current Spectrum Analysis. Mechanical Systems and Signal Processing, 2007, 21(2), 1127–1142.
EN. (2001). 13306. Maintenance Terminology.
Goodman, D., Hofmeister, J., & Judkins, J. (2007). Electronic prognostics for switched mode power supplies. Microelectronics Reliability, 47(12), 1902–1906. doi:10.1016/j.microrel.2007.02.021
Hameed, Z., Hong, Y. S., Cho, Y. M., Ahn, S. H., & Song, C. K. (2009). Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable Energy Reviews, 13(1), 1–39. doi:10.1016/j.rser.2007.05.008
Haus, S., Mikat, H., Nowara, M., Kandukuri, S. T., Klingauf, U., & Buderath, M. (2013). Fault Detection based on MCSA for a 400Hz Asynchronous Motor for Airborne Applications, 1–19.
Hu, C., Youn, B. D., Chung, J., & Kim, T. J. (2011). Online Estimation of Lithium ‐ Ion Battery SOC and Capacity with Multiscale Filtering Technique for EVs / HEVs. In PHM workshop on battery.
Impact Technologies. (2011). Avionics and e-PHM Applications Overview. Retrieved from http://www.impact-tek.com/Resources/TechnicalPublicationPDFs/Aerospace/Impact_AAV_AvionicsAndE-PHMApplicationsOverview.pdf
Isermann, R. (1984). Process Fault Detection Based on Modeling and Estimation Methods A Survey. Automatica, 20.
ISO, I. S. O. (2003). Condition monitoring and diagnostics of machines ISO 13374-1. Standard.
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. doi:10.1016/j.ymssp.2005.09.012
Jennions, I. K. (2011). Integrated Vehicle Health Management Perspectives on an Emerging Field. Book.
Lebold, M., & Thurston, M. (2001). Open standards for condition-based maintenance and prognostic systems. Maintenance and Reliability Conference (MARCON).
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1-2), 314–334. doi:10.1016/j.ymssp.2013.06.004
NATO. (2004). All Electric Combat Vehicles ( AECV ) for Future Applications. Technical REPORT TR-AVT-047, 323(July).
Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013). Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. Journal of Power Sources, 239, 680–688. doi:10.1016/j.jpowsour.2012.11.146
Pecht, M. (2011). Battery Health and Safety Management. In PHM workshop on battery.
Pecht, M., & Jaai, R. (2010). A prognostics and health management roadmap for information and electronics-rich systems. Microelectronics Reliability, 50(3), 317–323. doi:10.1016/j.microrel.2010.01.006
Scanff, E., Feldman, K. L., Ghelam, S., Sandborn, P., Glade, M., & Foucher, B. (2007). Life cycle cost impact of using prognostic health management (PHM) for helicopter avionics. Microelectronics Reliability, 47(12), 1857–1864. doi:10.1016/j.microrel.2007.02.014
Uckun, S., Goebel, K., & Lucas, P. J. F. (2008). Standardizing research methods for prognostics. 2008 International Conference on Prognostics and Health Management, 1–10. doi:10.1109/PHM.2008.4711437
Venkat, V., & Raghunathan, R. (2003). A review of process fault detection and diagnosis Part I: Quantitative model-based methods. Computers & Chemical Engineering, 27, 293–311.
Vichare, N. (2006). Prognostics and health management of electronics by utilizing environmental and usage loads. Thesis.
Voisin, A., Levrat, E., Cocheteux, P., & Iung, B. (2010). Generic prognosis model for proactive maintenance decision support: application to pre-industrial e-maintenance test bed. Journal of Intelligent .
Wilkinson, C., Humphrey, D., Vermeire, B., & Houston, J. (2004). Prognostic and Health Management for Avionics. In Aerospace Conference, Proceedings IEEE.
Zhang, Y., & Jiang, J. (2008). Bibliographical review on reconfigurable fault-tolerant control systems. Annual Reviews in Control, 32(2), 229–252. doi:10.1016/j.arcontrol.2008.03.008
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