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Prognostics 102: Efficient Bayesian-Based Prognostics Algorithm in MATLAB

In: Fault Detection, Diagnosis and Prognosis

Author

Listed:
  • Ting Dong
  • Dawn An
  • Nam-Ho Kim

Abstract

An efficient Bayesian-based algorithm is presented for physics-based prognostics, which combines a physical model with observed health monitoring data. Unknown model parameters are estimated using the observed data, from which the remaining useful life (RUL) of the system is predicted. This paper focuses on the Bayesian method for parameter estimation of a damage degradation model where epistemic uncertainty in model parameters is reduced with the observed data. Markov-chain Monte Carlo sampling is used to generate samples from the posterior distribution, which are then propagated through the physical model to estimate the distribution of the RUL. A MATLAB script of 76 lines is included in this paper with detailed explanations. A battery degradation model and crack growth model are used to explain the process of parameter estimation, the evolution of degradation and RUL prediction. The code presented in this paper can easily be altered for different applications. This code may help beginners to understand and use Bayesian method-based prognostics.

Suggested Citation

  • Ting Dong & Dawn An & Nam-Ho Kim, 2020. "Prognostics 102: Efficient Bayesian-Based Prognostics Algorithm in MATLAB," Chapters, in: Fausto Pedro Garcia Marquez (ed.), Fault Detection, Diagnosis and Prognosis, IntechOpen.
    • Handle: RePEc:ito:pchaps:180352
    DOI: 10.5772/intechopen.82781
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    More about this item

    Keywords

    Bayesian method; physics-based prognostics; remaining useful life; MATLAB code; crack growth; battery degradation;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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