Abstract
As a typical tool of risk analysis in practical engineering, failure mode and effects analysis (FMEA) theory is a well known method for risk prediction and prevention. However, how to quantify the uncertainty of the subjective assessments from FMEA experts and aggregate the corresponding uncertainty to the classical FMEA approach still needs further study. In this paper, we argue that the subjective assessments of FMEA experts can be adopted to model the weight of each FMEA expert, which can be regarded as a data-driven method for ambiguity information modeling in FMEA method. Based on this new perspective, a modified FMEA approach is proposed, where the subjective uncertainty of FMEA experts is handled in the framework of Dempster–Shafer evidence theory (DST). In the improved FMEA approach, the ambiguity measure (AM) which is an entropy-like uncertainty measure in DST framework is applied to quantify the uncertainty degree of each FMEA expert. Then, the classical risk priority number (RPN) model is improved by aggregating an AM-based weight factor into the RPN function. A case study based on the new RPN model in aircraft turbine rotor blades verifies the applicable and useful of the proposed FMEA approach.
Highlights
Failure of risk management in a complex system or a key component may lead to a total disaster [1].Risk modeling and analysis is a hot topic in practical applications such as complex networks [2], human reliability analysis [3], maintenance of complex systems [4] and so on
We argue that the assessment information itself implicates uncertainty of a failure mode and effects analysis (FMEA) expert, which should be considered when modeling the relative importance of a FMEA expert
An improved FMEA approach with a newly defined risk priority number (RPN) is proposed in this paper, where the ambiguity measure (AM)
Summary
Failure of risk management in a complex system or a key component may lead to a total disaster [1]. This paper proposes an improved RPN method by considering the relative importance of each FMEA member to contribute a more accurate method in uncertainty modeling and fusion of FMEA experts’ subjective evaluation. AM satisfies all five requirements for AU measures including probability consistency, set consistency, subadditivity, additivity and so on Based on this important feature for uncertain information processing, AM is chosen to quantify the uncertainty of FMEA experts’ evaluations. We argue that the assessment information itself implicates uncertainty of a FMEA expert, which should be considered when modeling the relative importance of a FMEA expert Based on this perception, an improved RPN model is designed to model the weight of a FMEA member with respect to all the FMEA experts in a typical FMEA team.
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