Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation
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DOI: 10.1016/j.ress.2021.107662
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- Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
- Chen, Zhen & Li, Yaping & Xia, Tangbin & Pan, Ershun, 2019. "Hidden Markov model with auto-correlated observations for remaining useful life prediction and optimal maintenance policy," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 123-136.
- Ramin Moghaddass & Ming Zuo, 2014. "Multistate degradation and supervised estimation methods for a condition-monitored device," IISE Transactions, Taylor & Francis Journals, vol. 46(2), pages 131-148.
- Paul Fearnhead & Peter Clifford, 2003. "On‐line inference for hidden Markov models via particle filters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 887-899, November.
- Lisnianski, Anatoly & Elmakias, David & Laredo, David & Ben Haim, Hanoch, 2012. "A multi-state Markov model for a short-term reliability analysis of a power generating unit," Reliability Engineering and System Safety, Elsevier, vol. 98(1), pages 1-6.
- Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
- Jiang, Tao & Liu, Yu, 2017. "Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 3-15.
- Moghaddass, Ramin & Zuo, Ming J., 2012. "A parameter estimation method for a condition-monitored device under multi-state deterioration," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 94-103.
- Belyi, Dmitriy & Popova, Elmira & Morton, David P. & Damien, Paul, 2017. "Bayesian failure-rate modeling and preventive maintenance optimization," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1085-1093.
- Compare, M. & Baraldi, P. & Bani, I. & Zio, E. & Mc Donnell, D., 2017. "Development of a Bayesian multi-state degradation model for up-to-date reliability estimations of working industrial components," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 25-40.
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Cited by:
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- Coraça, Eduardo M. & Ferreira, Janito V. & Nóbrega, EurÃpedes G.O., 2023. "An unsupervised structural health monitoring framework based on Variational Autoencoders and Hidden Markov Models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
- Zhao, Yixin & Cozzani, Valerio & Sun, Tianqi & Vatn, Jørn & Liu, Yiliu, 2023. "Condition-based maintenance for a multi-component system subject to heterogeneous failure dependences," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
- Zhao, Yunfei, 2022. "A Bayesian approach to comparing human reliability analysis methods using human performance data," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
- Haoyuan, Shen & Yizhong, Ma & Chenglong, Lin & Jian, Zhou & Lijun, Liu, 2023. "Hierarchical Bayesian support vector regression with model parameter calibration for reliability modeling and prediction," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
- Gámiz, M.L. & Navas-Gómez, F. & Raya-Miranda, R. & Segovia-GarcÃa, M.C., 2023. "Dynamic reliability and sensitivity analysis based on HMM models with Markovian signal process," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
- Wang, Zeyu & Shafieezadeh, Abdollah, 2023. "Bayesian updating with adaptive, uncertainty-informed subset simulations: High-fidelity updating with multiple observations," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
- Wang, Zeyu & Shafieezadeh, Abdollah & Xiao, Xiong & Wang, Xiaowei & Li, Quanwang, 2022. "Optimal monitoring location for tracking evolving risks to infrastructure systems: Theory and application to tunneling excavation risk," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
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Keywords
Sequential Bayesian inference; Importance sampling; Forward–backward algorithm; Model parameter inference; Hidden Markov model; Multi-state system; Multi-source evidence; Information fusion; Nuclear power plant; Maintenance optimization;All these keywords.
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