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Assessment of steam turbine blade failure and damage mechanisms using a Bayesian network

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  • Quintanar-Gago, David A.
  • Nelson, Pamela F.
  • Díaz-Sánchez, à ngeles
  • Boldrick, Michael S.

Abstract

Damage mechanisms that affect components within complex machines are often hard to detect and identify, especially if they are difficult to access, inspect and/or that are under continuous duty, compromising the reliability and performance of systems. In this paper, a Bayesian network model is developed to handle the interactions among common damage mechanisms and failure modes in nuclear steam turbine rotating blades. This model enables maintenance and inspection planning to better predict which portions(s) of the turbine will need repair. To compute the conditional probability tables, the model's unique quantification method combines expert judgement, the Recursive Noisy OR, and a damage mechanism susceptibility ranking that takes into account the synergistic interactions of the damage mechanisms. The approach can be suited to different turbine designs and purposes. The Bayesian network model development is described in detail, validated, and several examples of its application are presented.

Suggested Citation

  • Quintanar-Gago, David A. & Nelson, Pamela F. & Díaz-Sánchez, à ngeles & Boldrick, Michael S., 2021. "Assessment of steam turbine blade failure and damage mechanisms using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:reensy:v:207:y:2021:i:c:s095183202030822x
    DOI: 10.1016/j.ress.2020.107329
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    1. Özgür-Ünlüakın, Demet & Türkali, Busenur & Karacaörenli, Ayşe & Çağlar Aksezer, S., 2019. "A DBN based reactive maintenance model for a complex system in thermal power plants," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    2. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2015. "Bayesian belief networks for human reliability analysis: A review of applications and gaps," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 1-16.
    3. Røed, Willy & Mosleh, Ali & Vinnem, Jan Erik & Aven, Terje, 2009. "On the use of the hybrid causal logic method in offshore risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 445-455.
    4. Sýkora, Miroslav & Marková, Jana & Diamantidis, Dimitris, 2018. "Bayesian network application for the risk assessment of existing energy production units," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 312-320.
    5. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2016. "Methods for building Conditional Probability Tables of Bayesian Belief Networks from limited judgment: An evaluation for Human Reliability Application," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 93-112.
    6. Jones, B. & Jenkinson, I. & Yang, Z. & Wang, J., 2010. "The use of Bayesian network modelling for maintenance planning in a manufacturing industry," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 267-277.
    7. Kabir, Golam & Tesfamariam, Solomon & Francisque, Alex & Sadiq, Rehan, 2015. "Evaluating risk of water mains failure using a Bayesian belief network model," European Journal of Operational Research, Elsevier, vol. 240(1), pages 220-234.
    8. Zwirglmaier, Kilian & Straub, Daniel & Groth, Katrina M., 2017. "Capturing cognitive causal paths in human reliability analysis with Bayesian network models," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 117-129.
    9. Amir Masoud Mirhosseini & S. Adib Nazari & A. Maghsoud Pour & S. Etemadi Haghighi & M. Zareh, 2019. "Probabilistic failure analysis of hot gas path in a heavy-duty gas turbine using Bayesian networks," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 1173-1185, October.
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    Cited by:

    1. Ballester-Ripoll, Rafael & Leonelli, Manuele, 2022. "Computing Sobol indices in probabilistic graphical models," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Wu, Jiansong & Zhang, Linlin & Bai, Yiping & Reniers, Genserik, 2022. "A safety investment optimization model for power grid enterprises based on System Dynamics and Bayesian network theory," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Chen, Zhen & Zhou, Di & Zio, Enrico & Xia, Tangbin & Pan, Ershun, 2023. "Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    4. Sahu, Atma Ram & Palei, Sanjay Kumar, 2022. "Fault analysis of dragline subsystem using Bayesian network model," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Compare, Michele & Antonello, Federico & Pinciroli, Luca & Zio, Enrico, 2022. "A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    6. Ji, Chenyi & Su, Xing & Qin, Zhongfu & Nawaz, Ahsan, 2022. "Probability Analysis of Construction Risk based on Noisy-or Gate Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

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