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A joint particle filter and expectation maximization approach to machine condition prognosis

Author

Listed:
  • Jinjiang Wang

    (China University of Petroleum)

  • Robert X. Gao

    (Case Western Reserve University)

  • Zhuang Yuan

    (China University of Petroleum)

  • Zhaoyan Fan

    (Oregon State University)

  • Laibin Zhang

    (China University of Petroleum)

Abstract

This paper presents a probabilistic model based approach for machinery condition prognosis based on particle filter by integrating physical knowledge with in-process measurements into a state space framework to account for uncertainty and nonlinearity in machinery degradation process. One limitation of conventional particle filter is that condition prognosis is performed based on the model with predetermined parameters obtained from simulation studies or lab-controlled tests. Due to the stochastic nature of machinery defect propagation under varying operating conditions, model parameters may vary in practice which causes prediction errors. To address it, an integrated state prediction and parameter estimation framework based on particle filter and expectation-maximization algorithm is formulated and investigated. The model parameters are adaptively estimated based on expectation-maximization algorithm utilizing hidden degradation state and available in-process measurements. Particle filter is then performed on the identified model with estimated parameters following Bayesian inference scheme to improve the robustness and accuracy of machinery condition prognosis. The effectiveness of the developed method is demonstrated through a simulation study and an experimental run-to-failure bearing test in a wind turbine.

Suggested Citation

  • Jinjiang Wang & Robert X. Gao & Zhuang Yuan & Zhaoyan Fan & Laibin Zhang, 2019. "A joint particle filter and expectation maximization approach to machine condition prognosis," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 605-621, February.
  • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1268-0
    DOI: 10.1007/s10845-016-1268-0
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    References listed on IDEAS

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    1. Zio, Enrico & Peloni, Giovanni, 2011. "Particle filtering prognostic estimation of the remaining useful life of nonlinear components," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 403-409.
    2. An, Dawn & Choi, Joo-Ho & Kim, Nam Ho, 2013. "Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 161-169.
    3. Mkhadri, Abdallah, 1998. "On the rate of convergence of the ECME algorithm," Statistics & Probability Letters, Elsevier, vol. 37(1), pages 81-87, January.
    4. Michael Basin & Alexander Loukianov & Miguel Hernandez-Gonzalez, 2013. "Joint state and parameter estimation for uncertain stochastic nonlinear polynomial systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(7), pages 1200-1208.
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    Cited by:

    1. Matteo Barbieri & Khan T. P. Nguyen & Roberto Diversi & Kamal Medjaher & Andrea Tilli, 2021. "RUL prediction for automatic machines: a mixed edge-cloud solution based on model-of-signals and particle filtering techniques," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1421-1440, June.
    2. Han Cheng & Xianguang Kong & Qibin Wang & Hongbo Ma & Shengkang Yang & Gaige Chen, 2023. "Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 587-613, February.
    3. Li, Guofa & Wei, Jingfeng & He, Jialong & Yang, Haiji & Meng, Fanning, 2023. "Implicit Kalman filtering method for remaining useful life prediction of rolling bearing with adaptive detection of degradation stage transition point," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Saideep Nannapaneni & Sankaran Mahadevan & Abhishek Dubey & Yung-Tsun Tina Lee, 2021. "Online monitoring and control of a cyber-physical manufacturing process under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1289-1304, June.

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