Parameters Optimization of Lebesgue Sampling-based Fault Diagnosis and Prognosis with Application to Li-ion Batteries



Wuzhao Yan Bin Zhang Marcos Orchard


Lebesgue sampling-based fault diagnosis and prognosis (LSFDP) is developed with the advantage of less computation requirement and smaller uncertainty accumulation. Same as other diagnostic and prognostic approaches, the accuracy and precision of LS-FDP are significantly influenced by the diagnostic and prognostic models. The predicted results will show great discrepancy with the real remaining useful life (RUL) in applications if the model is not accurate. In addition, the fixed model parameters cannot accommodate the varying stress factors that affect the fault dynamics. To address this problem, the parameters in the models are treated as time-varying ones and are adjusted online to accommodate changing dynamics. In this paper, a recursive least square (RLS) based method with a forgetting factor is employed to make the diagnostic and prognostic models online adaptive in LS-FDP. The design and implementation of LS-FDP are based on a particle filtering algorithm and are illustrated with experiments of Li-ion batteries. The experimental results show that the performance of LS-FDP with model adaptation is improved on both battery capacity estimation and RUL prediction.

How to Cite

Yan, W., Zhang, B., & Orchard, M. (2016). Parameters Optimization of Lebesgue Sampling-based Fault Diagnosis and Prognosis with Application to Li-ion Batteries. Annual Conference of the PHM Society, 8(1).
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Bergstra, J. S., Bardenet, R., Bengio, Y., & K´egl, B. (2011). Algorithms for hyper-parameter optimization. In Advances in neural information processing systems (pp. 2546–2554).
Chen, C., Brown, D., Sconyers, C., Zhang, B., Vachtsevanos, G., & Orchard, M. E. (2012). An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems. Expert Systems with Applications, 39(10), 9031 - 9040.
Fu, L., Fei, Q., Guangming, S., & Li, Z. (2009, June). Optimization-based particle filter for state and parameter estimation. Journal of Systems Engineering and Electronics, 20(3), 479-484.
Genc, S., & Lafortune, S. (2007, April). Distributed diagnosis of place-bordered petri nets. Automation Science and Engineering, IEEE Transactions on, 4(2), 206-219. doi: 10.1109/TASE.2006.879916
Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993, April). Novel approach to nonlinear/non-gaussian bayesian state estimation. IEE Proceedings F -
Radar and Signal Processing, 140(2), 107-113. doi: 10.1049/ip-f-2.1993.0015
Jardine, A. K., 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.
Kumar, R., & Takai, S. (2009, July). Inference-based ambiguity management in decentralized decision-making: Decentralized diagnosis of discrete-event systems. Automation Science and Engineering, IEEE Transactions on, 6(3), 479-491. doi: 10.1109/TASE.2009.2021330
Laosiritaworn, W., & Chotchaithanakorn, N. (2009). Artificial neural networks parameters optimization design of experiments: An application in materials modeling.
Lee, J. (2007). A systematic approach for developing and deploying advanced prognostics technologies and tools: methodology and applications. In Proceedings of the second world congress on engineering asset management, harrogate, uk (pp. 1195–1206).
Lefebvre, D. (2014, Oct). Fault diagnosis and prognosis with partially observed petri nets. Systems, Man, and Cybernetics: Systems, IEEE Transactions on, 44(10), 1413-1424. doi: 10.1109/TSMC.2014.2311760
Lin, S.-W., Lee, Z.-J., Chen, S.-C., & Tseng, T.-Y. (2008). Parameter determination of support vector machine and feature selection using simulated annealing approach. Applied soft computing, 8(4), 1505–1512.
Liu, Q., Qin, S., & Chai, T. (2013, July). Decentralized fault diagnosis of continuous annealing processes based on multilevel pca. Automation Science and Engineering, IEEE Transactions on, 10(3), 687-698. doi: 10.1109/TASE.2012.2230628
Olivares, B., Cerda Munoz, M., Orchard, M., & Silva, J. (2013, Feb). Particle-filtering-based prognosis framework for energy storage devices with a statistical characterization of state-of-health regeneration phenomena. Instrumentation and Measurement, IEEE Transactions on, 62(2), 364-376. doi: 10.1109/TIM.2012.2215142
Orchard, M., Hevia-Koch, P., Zhang, B., & Tang, L. (2013, Nov). Risk measures for particle-filtering-based stateof-charge prognosis in lithium-ion batteries. Industrial Electronics, IEEE Transactions on, 60(11), 5260-5269. doi: 10.1109/TIE.2012.2224079
Pola, D., Navarrete, H., Orchard, M., Rabie, R., Cerda, M., Olivares, B., . . . Perez, A. (2015, June). Particlefiltering-based discharge time prognosis for lithium-ion batteries with a statistical characterization of use profiles. Reliability, IEEE Transactions on, 64(2), 710-720. doi: 10.1109/TR.2014.2385069
Qiu, W., Wen, Q., & Kumar, R. (2009, April). Decentralized diagnosis of event-driven systems for safely reacting to failures. Automation Science and Engineering, IEEE Transactions on, 6(2), 362-366. doi: 10.1109/TASE.2008.2009093
Ren, Y.,Wang, A., &Wang, H. (2015, March). Fault diagnosis and tolerant control for discrete stochastic distribution collaborative control systems. Systems, Man, and Cybernetics: Systems, IEEE Transactions on, 45(3), 462-471. doi: 10.1109/TSMC.2014.2358635
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management, 1(1), 20.
Schwabacher, M., & Goebel, K. (2007). A survey of artificial intelligence for prognostics. In Aaai fall symposium: Artificial intelligence for prognostics. Retrieved from
Sidhu, A., Izadian, A., & Anwar, S. (2015, Feb). Adaptive nonlinear model-based fault diagnosis of Li-ion batteries. IEEE Transactions on Industrial Electronics, 62(2), 1002-1011.
Wang, X., & Zhang, B. (2014, Dec). Real-time lebesguesampled model for continuous-time nonlinear systems. In Decision and control (cdc), 2014 ieee
53rd annual conference on (p. 4367-4372). doi: 10.1109/CDC.2014.7040070
Xian, W., Long, B., Li, M., & Wang, H. (2014, Jan). Prognostics of lithium-ion batteries based on the verhulst model, particle swarm optimization and particle filter. Instrumentation and Measurement, IEEE Transactions on, 63(1), 2-17. doi: 10.1109/TIM.2013.2276473
Yan, W., Zhang, B., Wang, X., Dou, W., & Wang, J. (2016, March). Lebesgue-sampling-based diagnosis and prognosis for lithium-ion batteries. IEEE Transactions on Industrial Electronics, 63(3), 1804-1812. doi: 10.1109/TIE.2015.2494529
Zhang, B., Sconyers, C., Byington, C., Patrick, R., Orchard, M., & Vachtsevanos, G. (2011, May). A probabilistic fault detection approach: Application
to bearing fault detection. Industrial Electronics, IEEE Transactions on, 58(5), 2011-2018. doi: 10.1109/TIE.2010.2058072
Zhang, B., &Wang, X. (2014). Fault diagnosis and prognosis based on lebesgue sampling. In Annual conference of the prognostics and health management society 2014 (Vol. 5).
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