A holistic comparison of the different resampling algorithms for particle filter based prognosis using lithium ion batteries as a case study
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[1] Ruqiang Yan,et al. Remaining Useful Life Prediction of Rolling Bearings Using an Enhanced Particle Filter , 2015, IEEE Transactions on Instrumentation and Measurement.
[2] Petar M. Djuric,et al. Resampling Methods for Particle Filtering: Classification, implementation, and strategies , 2015, IEEE Signal Processing Magazine.
[3] Sankalita Saha,et al. Metrics for Offline Evaluation of Prognostic Performance , 2021, International Journal of Prognostics and Health Management.
[4] Daniel D. Frey,et al. Remaining useful life estimation for systems subject to multiple degradation mechanisms , 2015, 2015 IEEE Conference on Prognostics and Health Management (PHM).
[5] Xiaohong Su,et al. Interacting multiple model particle filter for prognostics of lithium-ion batteries , 2017, Microelectron. Reliab..
[6] Liang Tang,et al. Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries , 2013, IEEE Transactions on Industrial Electronics.
[7] Liang Tang,et al. Risk-Sensitive Particle-Filtering-based Prognosis Framework for Estimation of Remaining Useful Life in Energy Storage Devices< , 2010 .
[8] M. Pitt,et al. Filtering via Simulation: Auxiliary Particle Filters , 1999 .
[9] Dieter Fox,et al. KLD-Sampling: Adaptive Particle Filters , 2001, NIPS.
[10] Dawn An,et al. Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab , 2013, Reliab. Eng. Syst. Saf..
[11] Francesco Cadini,et al. Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks , 2017 .
[12] Thomas B. Schön,et al. Marginalized particle filters for mixed linear/nonlinear state-space models , 2005, IEEE Transactions on Signal Processing.
[13] David He,et al. Lithium-ion battery life prognostic health management system using particle filtering framework , 2011 .
[14] Noureddine Zerhouni,et al. Particle filter-based prognostics: Review, discussion and perspectives , 2016 .
[15] N. D. Freitas. Rao-Blackwellised particle filtering for fault diagnosis , 2002 .
[16] Carles Ferrer,et al. Particle filters and resampling techniques: Importance in computational complexity analysis , 2013, 2013 Conference on Design and Architectures for Signal and Image Processing.
[17] Kai Goebel,et al. Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework , 2009 .
[18] Xuefei Guan,et al. Comparison of Two Probabilistic Fatigue Damage Assessment Approaches Using Prognostic Performance Metrics , 2011, International Journal of Prognostics and Health Management.
[19] Sankalita Saha,et al. On Applying the Prognostic Performance Metrics , 2009 .
[20] K. Goebel,et al. Metrics for evaluating performance of prognostic techniques , 2008, 2008 International Conference on Prognostics and Health Management.
[21] Wolfram Burgard,et al. Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.
[22] Wei Liang,et al. Remaining useful life prediction of lithium-ion battery with unscented particle filter technique , 2013, Microelectron. Reliab..
[23] Sankalita Saha,et al. Evaluating algorithm performance metrics tailored for prognostics , 2009, 2009 IEEE Aerospace conference.
[24] Linxia Liao,et al. Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction , 2014, IEEE Transactions on Reliability.
[25] Petar M. Djuric,et al. Resampling Algorithms for Particle Filters: A Computational Complexity Perspective , 2004, EURASIP J. Adv. Signal Process..
[26] Mark Coates,et al. Distributed particle filters for sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.
[27] Enrico Zio,et al. Ensemble neural network-based particle filtering for prognostics , 2013 .
[28] Yu Peng,et al. Comparison of resampling algorithms for particle filter based remaining useful life estimation , 2014, 2014 International Conference on Prognostics and Health Management.
[29] Michael G. Pecht,et al. Predicting long-term lumen maintenance life of LED light sources using a particle filter-based prognostic approach , 2015, Expert Syst. Appl..
[30] Belkacem Ould-Bouamama,et al. Particle filter based hybrid prognostics for health monitoring of uncertain systems in bond graph framework , 2016 .
[31] Kwok-Leung Tsui,et al. An ensemble model for predicting the remaining useful performance of lithium-ion batteries , 2013, Microelectron. Reliab..
[32] Brian A. Weiss,et al. A review of diagnostic and prognostic capabilities and best practices for manufacturing , 2019, J. Intell. Manuf..
[33] Christian Musso,et al. Improving Regularised Particle Filters , 2001, Sequential Monte Carlo Methods in Practice.
[34] Fredrik Gustafsson,et al. On Resampling Algorithms for Particle Filters , 2006, 2006 IEEE Nonlinear Statistical Signal Processing Workshop.
[35] Julien Bourgeois,et al. A hybrid prognostics approach for MEMS: From real measurements to remaining useful life estimation , 2016, Microelectron. Reliab..
[36] Sankalita Saha,et al. Prognostic Performance Metrics , 2016 .
[37] Phuc Do,et al. A Study on Health Diagnosis and Prognosis of an Industrial Diesel Motor: Hidden Markov Models and Particle Filter Approach , 2017 .
[38] Lifeng Wu,et al. Remaining useful life prognostic of power metal oxide semiconductor field effect transistor based on improved particle filter algorithm , 2017 .