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Risk ranking of wind turbine systems through an improved FMEA based on probabilistic linguistic information and the TODIM method

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  • Sang-sang He
  • Yi-ting Wang
  • Juan-juan Peng
  • Jian-qiang Wang

Abstract

The technology of wind power is being widely developed worldwide. Ensuring the reliable operation of wind turbine systems is of significance. A popular tool to identify the potential risk of an engineering system is the failure mode and effect analysis (FMEA) method. However, when conducting a FMEA in a complex and uncertain environment, experts may find it challenging to select an appropriate linguistic term for the evaluation. The probabilistic linguistic term sets (PLTSs) are a useful fuzzy set to help experts in describing their assessment. However, different experts may assign different semantic values to the same linguistic terms, and this aspect has not been extensively examined in the existing studies. Therefore, considering the psychological behaviour of experts and the semantics of linguistic terms in the risk ranking process, an improved FMEA based on the probabilistic linguistic information and TODIM (an acronym in Portuguese of interactive and multicriteria decision making) method was developed to identify the risks in wind turbine systems. Furthermore, to demonstrate the utility of the proposed model, it was applied in a floating offshore wind turbine system. The effectiveness and the validity of the model were verified by comparing with some other methods.

Suggested Citation

  • Sang-sang He & Yi-ting Wang & Juan-juan Peng & Jian-qiang Wang, 2022. "Risk ranking of wind turbine systems through an improved FMEA based on probabilistic linguistic information and the TODIM method," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(3), pages 467-480, March.
    • Handle: RePEc:taf:tjorxx:v:73:y:2022:i:3:p:467-480
    DOI: 10.1080/01605682.2020.1854629
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    Cited by:

    1. Liu, Fan & Liao, Huchang & Al-Barakati, Abdullah, 2023. "Physician selection based on user-generated content considering interactive criteria and risk preferences of patients," Omega, Elsevier, vol. 115(C).
    2. Decui Liang & Fangshun Li & Xinyi Chen, 2024. "Failure mode and effect analysis by exploiting text mining and multi-view group consensus for the defect detection of electric vehicles in social media data," Annals of Operations Research, Springer, vol. 340(1), pages 289-324, September.
    3. Wu, Qun & Liu, Xinwang & Qin, Jindong & Zhou, Ligang & Mardani, Abbas & Deveci, Muhammet, 2022. "An integrated multi-criteria decision-making and multi-objective optimization model for socially responsible portfolio selection," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

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