Extended Kalman filter development in Lebesgue sampling framework with an application to Li-ion battery diagnosis and prognosis
Extended Kalman filter in Riemann sampling framework (RS-EKF) has been widely used in diagnosis and prognosis, navigation systems, and GPS for its advantage of simplicity and reasonable solution for nonlinear systems. New particle filter based fault diagnosis and prognosis algorithms in Lebesgue sampling framework have been developed to enable the implementation on systems with limited computational sources, such as embedded systems. In this Lebesgue sampling-based approach, Lebesgue states are defined on the fault dimension axis and algorithm is executed only when the measurement causes a transition from one Lebesgue state to another, or an event happens. This is a need-based fault diagnosis and prognosis (FDP) philosophy in which the algorithm is executed only when necessary, thus less computational resources are required. In order to make algorithms more efficient, EKF algorithm is developed in Lebesgue sampling
framework (LS-EKF). With the philosophy of “execution only when necessary”, the proposed approach is able to eliminate unnecessary computations, especially in the scenario that the fault grows slowly. The prediction horizon defined by Lebesgue states on the fault dimension axis is usually small and, therefore, LS-EKF naturally benefits the uncertainty management by reducing the uncertainty accumulation. One feature of diagnosis and prognosis in Lebesgue sampling is that it requires two models, one for diagnosis and one for prognosis. The diagnostic model describes the dynamics of fault and is used to estimate the fault state. Prognostic model for LS-EKF describes the time for fault state reaching each defined Lebesgue state. The new algorithms is verified with an application to the diagnosis and prognosis of the state of health of Li-ion battery. The results show that LSEKF and RS-EKF have comparable performance in diagnosis Wuzhao Yan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. but LS-EKF has much less computation. Moreover, LS-EKF is more accurate and time-efficient on long term prognosis than RS-EKF algorithms, which makes it a promising solution for FDP in distributed applications.
How to Cite
Capolino, G.-A., & Filippetti, F. (2013). Introduction to the special section on advances in diagnosis for electrical machines, power electronics, and drives-part i. Industrial Electronics, IEEE Transactions on, 60(8), 3396–3397.
Cheng, S., & Pecht, M. (2009). A fusion prognostics method for remaining useful life prediction of electronic products. In Automation science and engineering, 2009. case 2009. ieee international conference on (pp. 102–107).
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
He, W., Williard, N., Osterman, M., & Pecht, M. (2011). Prognostics of lithium-ion batteries based on dempster–shafer theory and the bayesian monte carlo method. Journal of Power Sources, 196(23), 10314–10321.
Immovilli, F., Bianchini, C., Cocconcelli, M., Bellini, A., & Rubini, R. (2013). Bearing fault model for induction motor with externally induced vibration. Industrial Electronics, IEEE Transactions on, 60(8), 3408–3418.
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
Lall, P., Lowe, R., & Goebel, K. (2011, June). Extended kalman filter models and resistance spectroscopy for prognostication and health monitoring of leadfree electronics under vibration. In Prognostics and health management (phm), 2011 ieee conference on (p. 1-12). doi: 10.1109/ICPHM.2011.6024324
Lall, P.,Wei, J.,&Goebel, K. (2012). Comparison of kalmanfilter and extended kalman-filter for prognostics health management of electronics. In Thermal and thermomechanical phenomena in electronic systems (itherm), 2012 13th ieee intersociety conference on (pp. 1281–1291).
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
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
Lou, X., & Loparo, K. A. (2004). Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical systems and signal processing, 18(5), 1077–1095.
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. E., Hevia-Koch, P., Zhang, B., & Tang, L. (2013). Risk measures for particle-filtering-based state-of-charge prognosis in lithium-ion batteries. Industrial Electronics, IEEE Transactions on, 60(11), 5260–5269.
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
Saha, B., Goebel, K., & Christophersen, J. (2009). Comparison of prognostic algorithms for estimating remaining useful life of batteries. Transactions of the Institute of Measurement and Control.
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.
Scacchioli, A., Rizzoni, G., Salman, M., Li, W., Onori, S., & Zhang, X. (2014, Jan). Model-based diagnosis of an automotive electric power generation and storage system. Systems, Man, and Cybernetics:
Systems, IEEE Transactions on, 44(1), 72-85. doi: 10.1109/TSMCC.2012.2235951
Strangas, E. G., Aviyente, S., Neely, J. D., & Zaidi, S. S. H. (2013). The effect of failure prognosis and mitigation on the reliability of permanent-magnet ac motor drives. Industrial Electronics, IEEE Transactions on, 60(8), 3519–3528.
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:
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., Dou,W., Liu, D., Peng, Y., & Zhang, B. (2015). Parameters adaption of lebesgue sampling-based diagnosis and prognosis for li-ion batteries. In Annual conference of the prognostics and health management society 2015 (Vol. 6).
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., Tang, L., DeCastro, J., Roemer, M., & Goebel, K. (2014). Autonomous vehicle battery state-of-charge prognostics enhanced mission planning. Int. J. Prognost. Health Manage, 5(2), 1–12.
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).
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.