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Residual life prediction based on dynamic weighted Markov model and particle filtering

Author

Listed:
  • Shuai Zhang

    (Naval University of Engineering Power Engineering Marine Engineering)

  • Yongxiang Zhang

    (Naval University of Engineering Power Engineering Marine Engineering)

  • Jieping Zhu

    (Naval University of Engineering Power Engineering Marine Engineering)

Abstract

In order to improve the prediction accuracy of non-Gaussian data and build reasonably the prediction model, a novel residual life prediction method is proposed. A dynamic weighted Markov model is constructed by real time data and historical data, and the residual life is predicted by particle filter. The particles of the state vector are predicted and updated instantaneously using particle filter. The probability distribution of the predicted value is estimated by the updated particles. The residual life can be predicted using the set threshold of the state. This method improves the accuracy of residual life prediction. Finally, the advantage of this proposed method was shown experimentally using the bearings’ full cycle data.

Suggested Citation

  • Shuai Zhang & Yongxiang Zhang & Jieping Zhu, 2018. "Residual life prediction based on dynamic weighted Markov model and particle filtering," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 753-761, April.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:4:d:10.1007_s10845-015-1127-4
    DOI: 10.1007/s10845-015-1127-4
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    References listed on IDEAS

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    1. Ng, Selina S.Y. & Xing, Yinjiao & Tsui, Kwok L., 2014. "A naive Bayes model for robust remaining useful life prediction of lithium-ion battery," Applied Energy, Elsevier, vol. 118(C), pages 114-123.
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    Cited by:

    1. Chia-Hung Wang & Qigen Zhao & Rong Tian, 2023. "Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network," Energies, MDPI, vol. 16(11), pages 1-24, May.

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