Battery Capacity Anomaly Detection and Data Fusion
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Anomaly detection is a critical enabling technique of PHM, especially in safety critical applications. In order for the PHM system to begin prediction of remaining useful life of a given system or component, the fault must be detected. This paper presents an integrated anomaly detection system for state-of- health of lithium-ion batteries. Two algorithms for state estimation and anomaly detection are used: the extended Kalman filter and the particle filter. A Dempster-Shafer Theory-based fusion approach is implemented to reduce the uncertainty of detection. The results on battery data show that the fusion improves the detection results significantly.
How to Cite
##plugins.themes.bootstrap3.article.details##
PHM
Blank, S., Föhst, T., & Berns, K. (2010). A Fuzzy Approach to Low Level Sensor Fusion with Limited System Knowledge. 13th Conference on Information Fusion (FUSION). Edinburgh.
Box, G. E., Jenkins, G. M., & Reinsel, G. C. (2008). Time Series Analysis: Forecasting and Control. Hoboken: John Wiley & Sons, Inc.
Carl, J. W. (2001). Contrasting Approaches to Combine Evidence. In Handbook of Multisensor Data Fusion. Boca Raton: CRC Press LLC.
Challa, S., & Koks, D. (2004). Bayesian and Dempster- Shafer Fusion. Sādhanā, 29(2), 145-176.
Chen, C., Zhang, B., Vachtsevanos, G., & Orchard, M., (2011). Machine Condition Prediction based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering, IEEE Transactions on Industrial Electronics, 58(9), 4353-4364.
Chen, C., Brown, D., Sconyers, C., Zhang, B., Vachtsevanos, G., & Orchard, M. (2012). An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems, Expert Systems with Applications, 39, 9031-9040.
Gadsden, S. A., & Habibi, S. R. (2011). Model-based fault detection of a battery system in a hybrid electric vehicle. Proceedings of the 7th IEEE Vehicle Power and Propulsion Conference (VPPC '11). Chicago.
Goebel, K., Saha, B., Saxena, A., Celaya, J. R., & Christophersen, J. P. (2008). Prognostics in Battery Health Management. IEEE Instrumentation & Measurement Magazine, pp. 33-40.
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), doi:10314–10321.
Huang, S. (2010). Understanding Extended Kalman Filter - Part III: Extended Kalman Filter. Sydney: University of Technology Sydney.
Kim, J., & Cho, B. H. (2014). An innovative approach for characteristic analysis and state-of-health diagnosis for a Li-ion cell based on the discrete wavelet transform.
Journal of Power Sources, 260, 115–130.
Orchard, M., Hevia-Koch, P., Zhang, B., & Tang, L. (2013). Risk Measures for Particle-filtering-based State-of- Charge Prognosis in Lithium-Ion Batteries, IEEE Transactions on Industrial Electronics, 60(11), 5260-5269.
Parhami, B. (2005). Voting: A Paradigm for Adjunction and Data Fusion in Dependable Systems. In Dependable Computing Systems (pp. 87-114). New York: John Wiley & Sons, Inc.
Ribeiro, M. I. (2004). Kalman and Extended Kalman Filters: Concept: Derivation and Properties. Lisbon: Instituto Superior Técnico.
Russo, F., & Ramponi, G. (1994). Fuzzy Methods for Multisensor Data Fusion. IEEE Transactions on Instrumentation and Measurement, 43(2), 288-294.
Saha, B., & Goebel, K. (2009). Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework. Annual Conference of the Prognostics and Health Management Society. San Diego.
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, 31(3-4), 293-308.
Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton: Princeton University Press.
Shafer, G. (1990). Perspectives on the theory and practice of belief functions. International Journal of Approximate Reasoning, 4(5-6), 323-362.
Sidhu, A., Izadian, A., & Anwar, S. (2015). Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries. IEEE Transactions on Industrial Electronics, 62(2). doi:10.1109/TIE.2014.2336599
Singh, A., Izadian, A., & Anwar, S. (2013). Fault Diagnosis of Li-Ion Batteries Using Multiple-Model Adaptive Estimation. 39th Annual Conference of the IEEE Industrial Electronics Society. Vienna. doi:978-1-4799- 0223-1/13
Viharos, Z. J., & Kis, K. B. (2014). Survey on Neuro-Fuzzy Systems and Their Applications in Technical Diagnostics. 13th IMEKO TC10 Workshop on Technical Diagnostics. Warsaw.
Wu, C., Zhu, C., Ge, Y., & Zhao, Y. (2015). A Review on Fault Mechanism and Diagnosis Approach for Li-Ion Batteries. Journal of Nanomaterials. doi:631263
Wu, H., Siegel, M., Stiefelhagen, R., & Yang, J. (2002). Sensor Fusion Using Dempster-Shafer Theory. IEEE Instrumentation and Measurement Technology Conference. Anchorage.
Zhang, B., Khawaja, T., Patrick, R., & Vachtsevanos, G. (2010). A Novel Blind Deconvolution De-Noise Scheme in Failure Prognosis, Transactions of the Institute of Measurement & Control, 32(1), 3-30.
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.