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Developing Failure Modes and Effect Analysis on Offshore Wind Turbines Using Two-Stage Optimization Probabilistic Linguistic Preference Relations

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Advanced Decision-Making Methods and Applications in System Safety and Reliability Problems

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 211))

Abstract

The interest in offshore wind power operation and exploitation is increasing in many countries. The stakeholders and energy planners need to confidently decide how to minimize the maintenance cost and vulnerabilities of the offshore wind power immaturity as much as possible. Thus, this paper aims to develop an advanced failure mode and effect analysis (FMEA) technique to analyze offshore wind turbines (OWTs) by predicting the weak links of the system and improving the system safety and reliability performance, suggesting the subsequent corrective actions. We put forward to integrate FMEA with probabilistic linguistic preference relations (PLPRs), the multiplicative consistency-based weight tool, and the best–worst method (BWM). The PLPRs describe the risk factors and failure modes elected from a group of decision-makers. A two-stage optimization is used to drive the optimum importance weight of risk factors. In addition, the advanced BWM method is developed to prioritize the failure modes in descending order. The proposed method is then applied to a real case OWT to show its potential and applicability. Finally, a comparison is conducted to validate the model.

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  1. School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen, China

    He Li

  2. Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada

    Mohammad Yazdi

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Li, H., Yazdi, M. (2022). Developing Failure Modes and Effect Analysis on Offshore Wind Turbines Using Two-Stage Optimization Probabilistic Linguistic Preference Relations. In: Advanced Decision-Making Methods and Applications in System Safety and Reliability Problems. Studies in Systems, Decision and Control, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-031-07430-1_4

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