Failure Mode and Effect Analysis Based on Probabilistic Linguistic Preference Relations and Gained and Lost Dominance Score Method

Failure mode and effect analysis (FMEA) is a widely used reliability management technology to evaluate the risk of potential failures in a system, product, or service. Nevertheless, the normal risk priority number (RPN) method has been extensively criticized for many deficiencies in practical applic...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2023-03, Vol.53 (3), p.1566-1577
Main Authors: Liu, Zheng, Mou, Xun, Liu, Hu-Chen, Zhang, Ling
Format: Article
Language:English
Subjects:
Attitudes
Decision making
Failure
Failure analysis
Failure mode and effect analysis (FMEA)
Failure modes
gained and lost dominance score (GLDS) method
linguistic decision making
Linguistics
Optimization
Optimization models
probabilistic linguistic preference relation (PLPR)
Probabilistic logic
Reliability
Reliability analysis
Risk analysis
Risk assessment
Risk management
Technology assessment
Uncertainty
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Summary:Failure mode and effect analysis (FMEA) is a widely used reliability management technology to evaluate the risk of potential failures in a system, product, or service. Nevertheless, the normal risk priority number (RPN) method has been extensively criticized for many deficiencies in practical applications. To overcome the drawbacks of traditional FMEA, plenty of methods have been suggested in previous studies. But majority of them evaluated the risk factors of each failure mode directly and cannot take group and individual risk attitudes into account. In this article, we put forward a new FMEA approach integrating probabilistic linguistic preference relations (PLPRs) and gained and lost dominance score (GLDS) method. The PLPRs are adopted to describe the risk evaluations of experts by pairwise comparison of failure modes. An extended GLDS method is introduced to derive the risk ranking of failure modes considering both group and individual risk attitudes. Moreover, a two-step optimization model is proposed to determine the weights of risk factors when their weighing information is unknown. Finally, a load-haul-dumper machine risk analysis case is presented to demonstrate the proposed FMEA. It is shown that the approach being proposed in this study provides a practical and effective way for risk evaluation in FMEA.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2021.3105742