A particle filtering and kernel smoothing-based approach for new design component prognostics

Reliability Engineering and System Safety

Volumn 134, Issue , 2015, Pages 19-31

  Hu, Yang ^a   Baraldi, Piero ^a   Di Maio, Francesco ^a   Zio, Enrico ^a,b  

^a POLITECNICO DI MILANO   (Italy)

^b EDF R AND D   (France)

Author keywords

Battery; Kernel smoothing; Parameter estimation; Particle filtering; Prognostics; Remaining useful life

Indexed keywords

FORECASTING; LITHIUM-ION BATTERIES; MONTE CARLO METHODS; SYSTEMS ENGINEERING;

BATTERY; KERNEL SMOOTHING; PARTICLE FILTERING; PROGNOSTICS; REMAINING USEFUL LIVES;

PARAMETER ESTIMATION;

EID: 84908463816     PISSN: 09518320     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ress.2014.10.003     Document Type: Article

References (64)

1. E. Zio Prognostics and health management of industrial equipment Diagn Progn Eng Syst: Methods Tech 2012 333 356
2. X.-S. Si, W. Wang, C.-H. Hu, and D.-H. Zhou Remaining useful life estimation - a review on the statistical data driven approaches Eur J Oper Res 213 2011 1 14
3. J. Sikorska, M. Hodkiewicz, and L. Ma Prognostic modelling options for remaining useful life estimation by industry Mech Syst Signal Process 25 2011 1803 1836
4. W.B. Wang, and M. Pecht Economic analysis of canary-based prognostics and health management IEEE Trans Ind Electron 58 2011 3077 3089
5. J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, and D. Siegel Prognostics and health management design for rotary machinery systems-reviews, methodology and applications Mech Syst Signal Process 2013
6. Sankavaram C, Pattipati B, Kodali A, Pattipati K, Azam M, Kumar S, et al. Model-based and data-driven prognosis of automotive and electronic systems. In: IEEE international conference on automation science and engineering CASE; 2009. p. 96-101.
7. J. Liu, W. Wang, F. Ma, Y. Yang, and C. Yang A data-model-fusion prognostic framework for dynamic system state forecasting Eng Appl Artif Intell 25 2012 814 823
8. P. Baraldi, F. Cadini, F. Mangili, and E. Zio Model-based and data-driven prognostics under different available information Probab Eng Mech 32 2013 66 79
9. M.E. Orchard, P. Hevia-Koch, B. Zhang, and L. Tang Risk measures for particle-filtering-based state-of-charge prognosis in lithium-ion batteries IEEE Trans Ind Electron 60 2013 5260 5269
10. M. Han, and Y. Zhao Dynamic control model of BOF steelmaking process based on ANFIS and robust relevance vector machine Expert Syst Appl 38 2011 14786 14798
11. F. Di Maio, K.L. Tsui, and E. Zio Combining relevance vector machines and exponential regression for bearing residual life estimation Mech Syst Signal Process 31 2012 405 427
12. W.-Y. Chang Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA Int J Electr Power 53 2013 603 611
13. A. Eddahech, O. Briat, N. Bertrand, J.-Y. Delétage, and J.-M. Vinassa Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks Int J Electr Power 42 2012 487 494
14. W. Wang A model to predict the residual life of rolling element bearings given monitored condition information to date IMA J Manag Math 13 2002 3 16
15. Z. Wang, C. Hu, W. Wang, Z. Zhou, and X. Si A case study of remaining storage life prediction using stochastic filtering with the influence of condition monitoring Reliab Eng Syst Saf 132 2014 186 195
16. D. Andre, A. Nuhic, T. Soczka-Guth, and D.U. Sauer Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electricvehicles Eng Appl Artif Intell 26 2013 951 961
17. S. Sepasi, R. Ghorbani, and B.Y. Liaw Improved extended Kalman filter for state of charge estimation of battery pack J Power Sources 255 2014 368 376
18. S. Sepasi, R. Ghorbani, and B.Y. Liaw A novel on-board state-of-charge estimation method for aged Li-ion batteries based on model adaptive extended Kalman filter J Power Sources 245 2014 337 344
19. P. Baraldi, F. Mangili, and E. Zio Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data Reliab Eng Syst Saf 112 2013 94 108
20. M. Jouin, R. Gouriveau, D. Hissel, M.-C. Péra, and N. Zerhouni Prognostics of PEM fuel cell in a particle filtering framework Int J Hydrogen Energy 39 2014 481 494
21. E. Zio, and G. Peloni Particle filtering prognostic estimation of the remaining useful life of nonlinear components Reliab Eng Syst Saf 96 2011 403 409
22. Q. Miao, L. Xie, H. Cui, W. Liang, and M. Pecht Remaining useful life prediction of lithium-ion battery with unscented particle filter technique Microelectron Reliab 53 2013 805 810
23. W. He, N. Williard, M. Osterman, and M. Pecht Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method J Power Sources 196 2011 10314 10321
24. D. An, J.-H. Choi, and N.H. Kim Prognostics 101: a tutorial for particle filter-based prognostics algorithm using Matlab Reliab Eng Syst Saf 115 2013 161 169
25. A. Tulsyan, B. Huang, R. Bhushan Gopaluni, and J. Fraser Forbes On simultaneous on-line state and parameter estimation in non-linear state-space models J Process Control 23 2013 516 526
26. Z. Bin, C. Sconyers, C. Byington, R. Patrick, M. Orchard, and G. Vachtsevanos A probabilistic fault detection approach: application to bearing fault detection IEEE Trans Ind Electron 58 2011 2011 2018
27. B. Saha, and K. Goebel Model adaptation for prognostics in a particle filtering framework Int J Progn Health Manag 2 2011 10
28. Chen C, Pecht M. Prognostics of lithium-ion batteries using model-based and data-driven methods. In: Proceedings of the IEEE conference on prognostics and system health management (PHM); 2012. p. 1-6.
29. J. Sun, H. Zuo, W. Wang, and M.G. Pecht Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance Mech Syst Signal Process 28 2012 585 596
30. F. Daum Nonlinear filters: beyond the Kalman filter IEEE Aerosp Electron Syst Mag 20 2005 57 69
31. M. Orchard, F. Tobar, and G. Vachtsevanos Outer feedback correction loops in particle filtering-based prognostic algorithms: statistical performance comparison Stud Inform Control 18 2009 295 304
32. A. Doucet, S. Godsill, and C. Andrieu On sequential Monte Carlo sampling methods for Bayesian filtering Stat Comput 10 2000 197 208
33. Goebel K, Saha B, Saxena A. A comparison of three data-driven techniques for prognostics. In: Proceedings of the 62nd meeting of the society for machinery failure prevention technology (MFPT); 2008. p. 119-31.
34. M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking IEEE Trans Signal Process 50 2002 174 188
35. O. Cappé, S.J. Godsill, and E. Moulines An overview of existing methods and recent advances in sequential Monte Carlo IEEE Proc 95 2007 899 924
36. M.E. Orchard, and G.J. Vachtsevanos A particle-filtering approach for on-line fault diagnosis and failure prognosis Trans Inst Meas Control 31 2009 221 246
37. B. Chen, Y. Zhu, J. Hu, and J.C. Principe 3 - Information Theoretic Parameter Estimation C. Badong, Z. Yu, H. Jinchun, C. Jose, A2 Principe, Y.Z.J.H. Badong Chen, C.P. Jose, System parameter identification 2013 Elsevier Oxford 29 60
38. N. Wicker, J. Muller, R.K.R. Kalathur, and O. Poch A maximum likelihood approximation method for Dirichlet's parameter estimation Comput Stat Data Anal 52 2008 1315 1322
39. Z.G. Zhao, B. Huang, and F. Liu Parameter estimation in batch process using EM algorithm with particle filter Comput. & Chem. Eng. 57 2013 159 172 http://dx.doi.org/10.1016/j.compchemeng.2013.03.024
40. A. Eddahech, M. Ayadi, O. Briat, and J.-M. Vinassa Online parameter identification for real-time supercapacitor performance estimation in automotive applications Int J Electr Power 51 2013 162 167
41. Y. Yang, Y. Liao, G. Meng, and J. Lee A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis Expert Syst Appl 38 2011 11311 11320
42. D. An, J.-H. Choi, and N.H. Kim Identification of correlated damage parameters under noise and bias using Bayesian inference Struct Health Monit 11 2012 293 303
43. B.-J. Park, Y. Zhang, and D. Lord Bayesian mixture modeling approach to account for heterogeneity in speed data Transp Res Part B: Methodol 44 2010 662 673
44. An D, Choi J-H, Kim NH. A comparison study of methods for parameter estimation in the physics-based prognostics. In: Proceeedings of the 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference; 2012.
45. J. Liu, and M. West Combined parameter and state estimation in simulation-based filtering[M]//Sequential Monte Carlo methods in practice 2001 Springer New York 197 223
46. J. Ching, J.L. Beck, and K.A. Porter Bayesian state and parameter estimation of uncertain dynamical systems Probab Eng Mech 21 2006 81 96
47. L.E. Olivier, B. Huang, and I.K. Craig Dual particle filters for state and parameter estimation with application to a run-of-mine ore mill J Process Control 22 2012 710 717
48. Li T, Sun S, Sattar TP, Corchado JM. Fighting against sample degeneracy and impoverishment in particle filters: particularly on intelligent choices. arXiv:13082443 2013.
49. S. Shi, and D. Chen Enhancing particle image tracking performance with a sequential Monte Carlo method: the bootstrap filter Flow Meas Instrum 22 2011 190 200
50. T. Li, T.P. Sattar, and S. Sun Deterministic resampling: unbiased sampling to avoid sample impoverishment in particle filters Signal Process 92 2012 1637 1645
51. Y. Petetin, and F. Desbouvries Optimal SIR algorithm vs. fully adapted auxiliary particle filter: a non asymptotic analysis Stat Comput 23 2013 759 775
52. Corbetta M, Sbarufatti C, Manes A, Giglio M. Stochastic definition of state space equation for particle filtering algorithms. In: Proceedings of the prognostic and system health management conference, Milan, Italy; 2013.
53. T. Higuchi Monte Carlo filter using the genetic algorithm operators J Stat Comput Simulation 59 1997 1 23
54. Orchard M, Kacprzynski G, Goebel K, Saha B, Vachtsevanos G. Advances in uncertainty representation and management for particle filtering applied to prognostics. In: Proceedings of the IEEE international conference on prognostics and health management, PHM; 2008. p. 1-6.
55. M.J. Daigle, and K. Goebel Model-based prognostics with concurrent damage progression processes IEEE Trans Syst Man Cybern 43 2013 535 546
56. T. Chen, J. Morris, and E. Martin Particle filters for state and parameter estimation in batch processes J Process Control 15 2005 665 673
57. Wan-ping W, Sheng L, Ting-wen X Particle filter for state and parameter estimation in passive ranging. In: Proceedings of the IEEE international conference on intelligent computing and intelligent systems, ICIS; 2009. p. 257-61.
58. Orchard ME. A particle filtering-based framework for on-line fault diagnosis and failure prognosis[D], Georgia Institute of Technology, 2007, PhD thesis, http://hdl.handle.net/1853/19752
59. B. Saha, K. Goebel, S. Poll, and J. Christophersen Prognostics methods for battery health monitoring using a Bayesian framework IEEE Trans Instrum Meas 58 2009 291 296
60. J. Zhang, and J. Lee A review on prognostics and health monitoring of Li-ion battery J Power Sources 196 2011 6007 6014
61. Marcicki J, Todeschini F, Onori S, Canova M. Nonlinear parameter estimation for capacity fade in Lithium-ion cells based on a reduced-order electrochemical model. In: Proceedings of the IEEE American Control Conference (ACC); 2012. p. 572-7.
62. Mundnich Batic KB. Early detection of high volatility clusters using particle filters[D]. Universidad de Chile, 2013 5. PhD thesis
63. Mirabdollah MH, Mertsching B. Bearing only mobile robots'localization: observability and formulation using SIS particle filters. In: Proceedings of the International Conference on IEEE Communications, Computing and Control Applications, CCCA; 2011. p. 1-5.
64. B.E. Olivares, C. Munoz, M.E. Orchard, and J.F. Silva Particle-filtering-based prognosis framework for energy storage devices with a statistical characterization of state-of-health regeneration phenomena IEEE Trans Instrum Meas 62 2013 364 376