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Risk-based decision making method for maintenance policy selection of thermal power plant equipment

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  • Carazas, F.G.
  • Souza, G.F.M.

Abstract

This study presents a decision-making method for maintenance policy selection of power plants equipment. The method is based on risk analysis concepts. The method first step consists in identifying critical equipment both for power plant operational performance and availability based on risk concepts. The second step involves the proposal of a potential maintenance policy that could be applied to critical equipment in order to increase its availability. The costs associated with each potential maintenance policy must be estimated, including the maintenance costs and the cost of failure that measures the critical equipment failure consequences for the power plant operation. Once the failure probabilities and the costs of failures are estimated, a decision-making procedure is applied to select the best maintenance policy. The decision criterion is to minimize the equipment cost of failure, considering the costs and likelihood of occurrence of failure scenarios. The method is applied to the analysis of a lubrication oil system used in gas turbines journal bearings. The turbine has more than 150MW nominal output, installed in an open cycle thermoelectric power plant. A design modification with the installation of a redundant oil pump is proposed for lubricating oil system availability improvement.

Suggested Citation

  • Carazas, F.G. & Souza, G.F.M., 2010. "Risk-based decision making method for maintenance policy selection of thermal power plant equipment," Energy, Elsevier, vol. 35(2), pages 964-975.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:2:p:964-975
    DOI: 10.1016/j.energy.2009.06.054
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    References listed on IDEAS

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    Cited by:

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    3. Muhammad Zubair & Gyunyoung Heo, 2013. "Advancement in living probabilistic safety assessment to increase safety of nuclear power plants," Journal of Risk and Reliability, , vol. 227(5), pages 534-539, October.
    4. Yuyama, Ayumi & Kajitani, Yoshio & Shoji, Gaku, 2018. "Simulation of operational reliability of thermal power plants during a power crisis: Are we underestimating power shortage risk?," Applied Energy, Elsevier, vol. 231(C), pages 901-913.
    5. Zhou, Dengji & Zhang, Huisheng & Weng, Shilie, 2014. "A novel prognostic model of performance degradation trend for power machinery maintenance," Energy, Elsevier, vol. 78(C), pages 740-746.
    6. Carnero, María Carmen & Gómez, Andrés, 2017. "Maintenance strategy selection in electric power distribution systems," Energy, Elsevier, vol. 129(C), pages 255-272.
    7. Rusin, Andrzej & Wojaczek, Adam, 2019. "Improving the availability and lengthening the life of power unit elements through the use of risk-based maintenance planning," Energy, Elsevier, vol. 180(C), pages 28-35.
    8. Tang, Yang & Liu, Qingyou & Jing, Jiajia & Yang, Yan & Zou, Zhengwei, 2017. "A framework for identification of maintenance significant items in reliability centered maintenance," Energy, Elsevier, vol. 118(C), pages 1295-1303.
    9. Martyna Tomala & Andrzej Rusin & Adam Wojaczek, 2020. "Risk-Based Planning of Diagnostic Testing of Turbines Operating with Increased Flexibility," Energies, MDPI, vol. 13(13), pages 1-16, July.
    10. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    11. Li, Wanhong & Liu, Guangzhong, 2022. "Dynamic failure mode analysis approach based on an improved Taguchi process capability index," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    12. Arthur H.A. Melani & Carlos A. Murad & Adherbal Caminada Netto & Gilberto F.M. Souza & Silvio I. Nabeta, 2019. "Maintenance Strategy Optimization of a Coal-Fired Power Plant Cooling Tower through Generalized Stochastic Petri Nets," Energies, MDPI, vol. 12(10), pages 1-28, May.
    13. Rusin, Andrzej & Bieniek, Michał, 2017. "Maintenance planning of power plant elements based on avoided risk value," Energy, Elsevier, vol. 134(C), pages 672-680.

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