Volume 12, Issue 4 (12-2022)
J Health Saf Work 2022, 12(4): 854-871 |
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Mangeli Kamsefidi M, Shahraki A, Hosseinzadeh Saljooghi F. Improving the Calculation of RPN in the FMEA Method by Combining a Nonlinear Model with Revised TOPSIS and Fuzzy Logic. J Health Saf Work 2022; 12 (4) :854-871
URL: http://jhsw.tums.ac.ir/article-1-6775-en.html
URL: http://jhsw.tums.ac.ir/article-1-6775-en.html
1- Department of Industrial Engineering, University of Sistan and Baluchestan, Iran
2- Department of Industrial Engineering, University of Sistan and Baluchestan, Iran ,shahrakiar@hamoon.usb.ac.ir
3- Department of Mathematics, University of Sistan and Baluchestan, Iran
2- Department of Industrial Engineering, University of Sistan and Baluchestan, Iran ,
3- Department of Mathematics, University of Sistan and Baluchestan, Iran
Abstract: (1661 Views)
Introduction: Failure Mode and Effects Analysis (FMEA) is a structured way to find and understand the states of a system’s failure and to calculate the resulting effects. In this method, which has been criticized by many researchers, the risk priority number is obtained for each failure mode based on the multiplication of the three parameters of occurrence (O), severity (S) and detection (D). In order to overcome the disadvantages of the traditional method of FMEA, such as ranking the failure and weighting the parameters, this research proposes a model in the fuzzy set.
Material and Methods: The model proposed in this paper is a nonlinear model for weighting the parameters of the FMEA and the revised TOPSIS method for ranking the failures, which is used for the first time to improve the FMEA method.
Results: The proposed model was presented in the Copper Complex of Shahr-e-Babak to assess safety risks. Based on the results of the study, it was found that in this proposed model, the weights of severity and detection were 0.479 and 0.186, respectively, and the results of the ranking showed that the risks of falling from height and getting stuck between objects had the highest and lowest priorities, respectively.
Conclusion: In the proposed model, based on Logarithmic Fuzzy Preference Programming and the revised TOPSIS method, the definite weights of the parameters were presented without any fuzzy number ranking and risk ranking with more criteria, respectively. Therefore, the proposed model has a higher ability compared to the traditional FMEA, and its application can be recommended to determine the ranking of risks.
Material and Methods: The model proposed in this paper is a nonlinear model for weighting the parameters of the FMEA and the revised TOPSIS method for ranking the failures, which is used for the first time to improve the FMEA method.
Results: The proposed model was presented in the Copper Complex of Shahr-e-Babak to assess safety risks. Based on the results of the study, it was found that in this proposed model, the weights of severity and detection were 0.479 and 0.186, respectively, and the results of the ranking showed that the risks of falling from height and getting stuck between objects had the highest and lowest priorities, respectively.
Conclusion: In the proposed model, based on Logarithmic Fuzzy Preference Programming and the revised TOPSIS method, the definite weights of the parameters were presented without any fuzzy number ranking and risk ranking with more criteria, respectively. Therefore, the proposed model has a higher ability compared to the traditional FMEA, and its application can be recommended to determine the ranking of risks.
Keywords: Failure Mode and Effects Analysis, risk assessment, fuzzy logic, Logarithmic Fuzzy Preference Programming, revised TOPSIS
Type of Study: Research |
Received: 2022/12/26 | Accepted: 2022/12/31 | Published: 2022/12/31
Received: 2022/12/26 | Accepted: 2022/12/31 | Published: 2022/12/31
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