This paper proposes a novel approach based on a Particle Filtering technique and an Optimized Tuning Kernel Smoothing method for the prediction on the Remaining Useful Life (RUL) of a degrading component. We consider a case in which a model describing the degradation process is available, but the exact values of the model parameters are unknown and observations of historical degradation trajectories in similar components are unavailable. A numerical application concerning the prediction of the RUL of degrading Lithium-ion batteries is considered. The obtained results show that the proposed method can provide a satisfactory RUL prediction as well as the parameters estimation.
How to Cite
Model-based Prognostics, Remaining Useful Life, Parameter Estimation, Particle Filtering, Optimized Tuning Kernel Smoothing, Battery
Arulampalam, M Sanjeev, Maskell, Simon, Gordon, Neil, & Clapp, Tim. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. Signal Processing, IEEE Transactions on, 50(2), 174-188.
Chen, Tao, Morris, Julian, & Martin, Elaine. (2005). Particle filters for state and parameter estimation in batch processes. Journal of Process Control, 15(6), 665-673.
Ching, Jianye, Beck, James L., & Porter, Keith A. (2006). Bayesian state and parameter estimation of uncertain dynamical systems. Probabilistic Engineering Mechanics, 21(1), 81-96. doi: http://dx.doi.org/10.1016/j.probengmech.2005.08.003
Corbetta, Matteo, Sbarufatti, Claudio, Manes, Andrea, & Giglio, Marco. (2013). Stochastic Definition of State Space Equation for Particle Filtering Algorithms. Prognostic and System Health Management Conference, Milan, Italy.
Daigle, M. J., & Goebel, K. (2013). Model-Based Prognostics With Concurrent Damage Progression Processes. IEEE Transactions on Systems Man Cybernetics-Systems, 43(3), 535-546. doi: Doi
Daum, Fred. (2005). Nonlinear filters: beyond the Kalman filter. Aerospace and Electronic Systems Magazine, IEEE, 20(8), 57-69.
Douc, Randal, & Cappé, Olivier. (2005). Comparison of resampling schemes for particle filtering. Image and Signal Processing and Analysis, ISPA 2005. Proceedings of the 4th International Symposium on.
He, Wei, Williard, Nicholas, Osterman, Michael, & Pecht, Michael. (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.
Higuchi, Tomoyuki. (1997). Monte Carlo filter using the genetic algorithm operators. Journal of Statistical Computation and Simulation, 59(1), 1-23.
Hu, Yang, Baraldi, Piero, Maio, Francesco Di, & Zio, Enrico. (2013). A Particle Filtering and Kernel Smoothing Approach for Component Prognostics based on a Model with Unknown Parameters.
Reliability Engineering & System Safety, under review.
Liu, Jane, & West, Mike. (2001). Combined parameter and state estimation in simulation-based filtering: Springer.
Marcicki, James, Todeschini, Fabio, Onori, Simona, & Canova, Marcello. (2012). Nonlinear parameter estimation for capacity fade in Lithium-ion cells based on a reduced-order electrochemical model. American Control Conference (ACC), 2012.
Orchard, Marcos E, & Vachtsevanos, George J. (2009). A particle-filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, 31(3-4), 221-246.
Saha, Bhaskar, Goebel, Kai, Poll, Scott, & Christophersen, Jon. (2009). Prognostics methods for battery health monitoring using a Bayesian framework. Instrumentation and Measurement, IEEE Transactions on, 58(2), 291-296.
Sankavaram, Chaitanya, Pattipati, Bharath, Kodali, Anuradha, Pattipati, Krishna, Azam, Mohammad, Kumar, Sachin, & Pecht, Michael. (2009). Model-based and data-driven prognosis of automotive and electronic systems. Automation Science and Engineering, 2009. CASE 2009. IEEE International Conference on.
Tulsyan, Aditya, Huang, Biao, Bhushan Gopaluni, R, & Fraser Forbes, J. (2013). On simultaneous on-line state and parameter estimation in non-linear state-space models. Journal of Process Control, 23(4), 516-526.
Wan-ping, Wang, Sheng, Liao, & Ting-wen, Xing. (2009). Particle filter for state and parameter estimation in passive ranging. Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on.
Zhang, Jingliang, & Lee, Jay. (2011). A review on prognostics and health monitoring of Li-ion battery. Journal of Power Sources, 196(15), 6007-6014.
Zio, Enrico, & Peloni, Giovanni. (2011). Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliability Engineering & System Safety, 96(3), 403-409.
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.