A Prognostic Approach Based on Particle Filtering and Optimized Tuning Kernel Smoothing

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Yang Hu Piero Baraldi Francesco Di Maio Enrico Zio

Abstract

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

Hu, Y., Baraldi, P., Maio, F. D., & Zio, E. (2014). A Prognostic Approach Based on Particle Filtering and Optimized Tuning Kernel Smoothing. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1501
Abstract 68 | PDF Downloads 36

##plugins.themes.bootstrap3.article.details##

Keywords

Model-based Prognostics, Remaining Useful Life, Parameter Estimation, Particle Filtering, Optimized Tuning Kernel Smoothing, Battery

References
An, D., Choi, J. H., & Kim, N. H. (2012). A comparison study of methods for parameter estimation in the physics-based prognostics, In 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 2012
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
10.1109/Tsmca.2012.2207109
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.
Section
Technical Papers

Similar Articles

1 2 3 4 5 6 7 > >> 

You may also start an advanced similarity search for this article.