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A Bayesian approach for a damage growth model using sporadically measured and heterogeneous on-site data from a steam turbine

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  • Choi, Woosung
  • Youn, Byeng D.
  • Oh, Hyunseok
  • Kim, Nam H.

Abstract

Accurate prediction of the remaining useful life (RUL) of plant turbines is a major scientific challenge for effective operation and maintenance in the power plant industry. This paper proposes an RUL prediction methodology that incorporates a damage index into the damage growth model. A Bayesian inference technique is used to consider uncertainties while estimating the probability distribution of a damage index from on-site hardness measurements. A Bayesian approach is proposed for the damage growth model for use with aged steam turbines. The predictive distribution of the damage index is estimated using its mean and standard deviation. As a case study, real steam turbines from power plants are examined to demonstrate the effectiveness of the proposed Bayesian approach. The results from the proposed damage growth model can be used to predict the RULs of the steam turbines of power plants regardless of load types (peak-load or base-load) of the power plant.

Suggested Citation

  • Choi, Woosung & Youn, Byeng D. & Oh, Hyunseok & Kim, Nam H., 2019. "A Bayesian approach for a damage growth model using sporadically measured and heterogeneous on-site data from a steam turbine," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 137-150.
    • Handle: RePEc:eee:reensy:v:184:y:2019:i:c:p:137-150
    DOI: 10.1016/j.ress.2018.03.012
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    References listed on IDEAS

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    3. 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.
    4. 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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    2. Wang, Run-Zi & Gu, Hang-Hang & Zhu, Shun-Peng & Li, Kai-Shang & Wang, Ji & Wang, Xiao-Wei & Hideo, Miura & Zhang, Xian-Cheng & Tu, Shan-Tung, 2022. "A data-driven roadmap for creep-fatigue reliability assessment and its implementation in low-pressure turbine disk at elevated temperatures," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Chang, Mingu & Lee, Jongsoo, 2020. "Early stage data-based probabilistic wear life prediction and maintenance interval optimization of driving wheels," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    4. Jagtap, Hanumant P. & Bewoor, Anand K. & Kumar, Ravinder & Ahmadi, Mohammad Hossein & Chen, Lingen, 2020. "Performance analysis and availability optimization to improve maintenance schedule for the turbo-generator subsystem of a thermal power plant using particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 204(C).

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