Risk Evaluation in Failure Mode and Effects Analysis Using Fuzzy Measure and Fuzzy Integral
Failure mode and effects analysis (FMEA) is a popular and useful approach applied to examine potential failures in different products, designs, processes, and services. As a vital index, the risk priority number (RPN) can determine the risk priorities of failure modes by some risk factors such as oc...
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doaj-cb95971b0c25481798fc9f0a50e69f642020-11-25T00:40:22ZengMDPI AGSymmetry2073-89942017-08-019816210.3390/sym9080162sym9080162Risk Evaluation in Failure Mode and Effects Analysis Using Fuzzy Measure and Fuzzy IntegralHaibin Liu0Xinyang Deng1Wen Jiang2School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaFailure mode and effects analysis (FMEA) is a popular and useful approach applied to examine potential failures in different products, designs, processes, and services. As a vital index, the risk priority number (RPN) can determine the risk priorities of failure modes by some risk factors such as occurrence (O), severity (S), and detection (D). However, in FMEA, the traditional risk priority number approach has some shortcomings, especially in setting the weight of risk factors. This paper presents an improved risk priority number approach based on a fuzzy measure and fuzzy integral. A fuzzy measure is used to reflect the importance of the individual indicators and the indicator set and a fuzzy integral is a nonlinear function defined on the basis of fuzzy measure. The weights of risk factors given by domain experts are seen as fuzzy densities to generate a λ -fuzzy measure which can reflect the weights’ difference and relevance about risk factors. Then, the Choquet integral is used to fuse every value of risk factors about failure modes so as to obtain the comprehensive evaluation result. The result can reflect the comprehensive risk level, so it has a definite physical significance. Finally, an illustrative example and a comparison with another approach are given to show the effectiveness of the proposed approach in the paper.https://www.mdpi.com/2073-8994/9/8/162failure modeeffects analysisrisk priority numberfuzzy measurefuzzy integralChoquet integral |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Haibin Liu Xinyang Deng Wen Jiang |
spellingShingle |
Haibin Liu Xinyang Deng Wen Jiang Risk Evaluation in Failure Mode and Effects Analysis Using Fuzzy Measure and Fuzzy Integral Symmetry failure mode effects analysis risk priority number fuzzy measure fuzzy integral Choquet integral |
author_facet |
Haibin Liu Xinyang Deng Wen Jiang |
author_sort |
Haibin Liu |
title |
Risk Evaluation in Failure Mode and Effects Analysis Using Fuzzy Measure and Fuzzy Integral |
title_short |
Risk Evaluation in Failure Mode and Effects Analysis Using Fuzzy Measure and Fuzzy Integral |
title_full |
Risk Evaluation in Failure Mode and Effects Analysis Using Fuzzy Measure and Fuzzy Integral |
title_fullStr |
Risk Evaluation in Failure Mode and Effects Analysis Using Fuzzy Measure and Fuzzy Integral |
title_full_unstemmed |
Risk Evaluation in Failure Mode and Effects Analysis Using Fuzzy Measure and Fuzzy Integral |
title_sort |
risk evaluation in failure mode and effects analysis using fuzzy measure and fuzzy integral |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2017-08-01 |
description |
Failure mode and effects analysis (FMEA) is a popular and useful approach applied to examine potential failures in different products, designs, processes, and services. As a vital index, the risk priority number (RPN) can determine the risk priorities of failure modes by some risk factors such as occurrence (O), severity (S), and detection (D). However, in FMEA, the traditional risk priority number approach has some shortcomings, especially in setting the weight of risk factors. This paper presents an improved risk priority number approach based on a fuzzy measure and fuzzy integral. A fuzzy measure is used to reflect the importance of the individual indicators and the indicator set and a fuzzy integral is a nonlinear function defined on the basis of fuzzy measure. The weights of risk factors given by domain experts are seen as fuzzy densities to generate a λ -fuzzy measure which can reflect the weights’ difference and relevance about risk factors. Then, the Choquet integral is used to fuse every value of risk factors about failure modes so as to obtain the comprehensive evaluation result. The result can reflect the comprehensive risk level, so it has a definite physical significance. Finally, an illustrative example and a comparison with another approach are given to show the effectiveness of the proposed approach in the paper. |
topic |
failure mode effects analysis risk priority number fuzzy measure fuzzy integral Choquet integral |
url |
https://www.mdpi.com/2073-8994/9/8/162 |
work_keys_str_mv |
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1725290583417683968 |