IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v184y2019icp137-150.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832017310888
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2018.03.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Qin, H. & Zhou, W. & Zhang, S., 2015. "Bayesian inferences of generation and growth of corrosion defects on energy pipelines based on imperfect inspection data," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 334-342.
    2. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Daoqing & He, Jingjing & Du, Yi-Mu & Sun, C.P. & Guan, Xuefei, 2021. "Probabilistic information fusion with point, moment and interval data in reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Hachem, Hassan & Vu, Hai Canh & Fouladirad, Mitra, 2024. "Different methods for RUL prediction considering sensor degradation," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Hu, Yang & Baraldi, Piero & Di Maio, Francesco & Zio, Enrico, 2015. "A particle filtering and kernel smoothing-based approach for new design component prognostics," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 19-31.
    4. Asadzadeh, S.M. & Azadeh, A., 2014. "An integrated systemic model for optimization of condition-based maintenance with human error," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 117-131.
    5. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    6. Zhang, Jian-Xun & Hu, Chang-Hua & He, Xiao & Si, Xiao-Sheng & Liu, Yang & Zhou, Dong-Hua, 2017. "Lifetime prognostics for deteriorating systems with time-varying random jumps," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 338-350.
    7. Merainani, Boualem & Laddada, Sofiane & Bechhoefer, Eric & Chikh, Mohamed Abdessamed Ait & Benazzouz, Djamel, 2022. "An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples," Renewable Energy, Elsevier, vol. 182(C), pages 1141-1151.
    8. Zhang, Jian-Xun & Si, Xiao-Sheng & Du, Dang-Bo & Hu, Chang-Hua & Hu, Chen, 2020. "A novel iterative approach of lifetime estimation for standby systems with deteriorating spare parts," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    9. Pradeep Kundu & Makarand S.Kulkarni & Ashish K.Darpe, 2023. "A hybrid prognosis approach for life prediction of gears subjected to progressive pitting failure mode," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1325-1346, March.
    10. Zhou, Shirong & Tang, Yincai & Xu, Ancha, 2021. "A generalized Wiener process with dependent degradation rate and volatility and time-varying mean-to-variance ratio," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    11. Hai-Kun Wang & Yan-Feng Li & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Remaining useful life estimation under degradation and shock damage," Journal of Risk and Reliability, , vol. 229(3), pages 200-208, June.
    12. Wang, Yiwei & Gogu, Christian & Kim, Nam H. & Haftka, Raphael T. & Binaud, Nicolas & Bes, Christian, 2019. "Noise-dependent ranking of prognostics algorithms based on discrepancy without true damage information," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 86-100.
    13. Blancke, Olivier & Tahan, Antoine & Komljenovic, Dragan & Amyot, Normand & Lévesque, Mélanie & Hudon, Claude, 2018. "A holistic multi-failure mode prognosis approach for complex equipment," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 136-151.
    14. Yan, Bingxin & Ma, Xiaobing & Yang, Li & Wang, Han & Wu, Tianyi, 2020. "A novel degradation-rate-volatility related effect Wiener process model with its extension to accelerated ageing data analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    15. Bai, Guangxing & Su, Yunsheng & Rahman, Maliha Maisha & Wang, Zequn, 2023. "Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    16. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    17. Wang, Han & Wang, Dongdong & Liu, Haoxiang & Tang, Gang, 2022. "A predictive sliding local outlier correction method with adaptive state change rate determining for bearing remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    18. Mason, Paolo, 2017. "A Bayesian analysis of component life expectancy and its implications on the inspection schedule," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 87-94.
    19. Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.
    20. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Bian, Linkan & Si, Xiaosheng, 2019. "Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 88-100.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:184:y:2019:i:c:p:137-150. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.