A new method in failure mode and effects analysis based on evidential reasoning
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The traditional failure mode and effects analysis (FMEA) is determined by risk priority number (RPN), which is the product of three risk factors occurrence (O), severity (S), and detection (D). One of the open issues is how to precisely determine and aggregate the risk factors. However, the traditional FMEA has been extensively criticized for various reasons. In this paper, a new method in fuzzy FMEA is proposed using evidential reasoning (ER) and the technique for order preference by similarity to ideal solution (TOPSIS). The ER approach is used to express the experts’ assessment information which may be imprecise and uncertain. Considering the experts’ weights, we construct the group assessment. Weighted average method is then utilized to transform the group assessment value into crisp value. TOPSIS is applied to aggregate the risk factors which are taken to account as the multi-attribute, and used to rank the risk priority. By making full use of attribute information, TOPSIS provides a cardinal ranking of alternatives, and does not require the attribute preferences are independent. A numerical example shows that the proposed method is efficient to its applications.
KeywordsFailure mode and effects analysis Risk priority number Evidential reasoning TOPSIS
Project supported by Chongqing Natural Science Foundation (for Distinguished Young Scholars), Grant No. CSCT, 2010BA2003, National Natural Science Foundation of China, Grant No. 61174022, National High Technology Research and Development Program of China (863 Program; No. 2013AA013801), Doctor Funding of Southwest University Grant No. SWU110021, National Key Technology R&D Program (2012BAH07B01). We greatly appreciate the editor’s encouragement and the anonymous reviewer’s valuable comments and suggestions to improve this work.
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