A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis
This paper presents a new Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model for the prioritization of failures in Failure Mode and Effect Analysis (FMEA). In FMEA, the monotonicity property of the RPN scores is important. To maintain the monotonicity property of an FIS-based RPN mo...
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| Format: | Article |
| Language: | English |
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IEEE
2015
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| Online Access: | http://ir.unimas.my/id/eprint/8142/ http://ir.unimas.my/id/eprint/8142/1/07104185.pdf |
| _version_ | 1848836310561718272 |
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| author | Tay, Kai Meng Jee, T.L Lim, C.P |
| author_facet | Tay, Kai Meng Jee, T.L Lim, C.P |
| author_sort | Tay, Kai Meng |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | This paper presents a new Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model for the prioritization of failures in Failure Mode and Effect Analysis (FMEA). In FMEA, the monotonicity property of the RPN scores is important. To maintain the monotonicity property of an FIS-based RPN model, a complete and monotonically-ordered fuzzy rule base is necessary. However, it is impractical to gather all (potentially a large number of) fuzzy rules from FMEA users. In this paper, we introduce a new two-stage approach to reduce the number of fuzzy rules that needs to be gathered, and to satisfy the monotonicity property. In stage-1, a Genetic Algorithm (GA) is used to search for a small set of fuzzy rules to be gathered from FMEA users. In stage-2, the remaining fuzzy rules are deduced approximately by a monotonicity-preserving similarity reasoning scheme. The monotonicity property is exploited as additional qualitative information for constructing the FIS-based RPN model. To assess the effectiveness of the proposed approach, a real case study with information collected from a semiconductor manufacturing plant is conducted. The outcomes indicate that the proposed approach is effective in developing an FIS-based RPN model with only a small set of fuzzy rules, which is able to satisfy the monotonicity property for prioritization of failures in FMEA. |
| first_indexed | 2025-11-15T06:21:44Z |
| format | Article |
| id | unimas-8142 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:21:44Z |
| publishDate | 2015 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-81422015-07-02T04:32:53Z http://ir.unimas.my/id/eprint/8142/ A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis Tay, Kai Meng Jee, T.L Lim, C.P QA Mathematics This paper presents a new Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model for the prioritization of failures in Failure Mode and Effect Analysis (FMEA). In FMEA, the monotonicity property of the RPN scores is important. To maintain the monotonicity property of an FIS-based RPN model, a complete and monotonically-ordered fuzzy rule base is necessary. However, it is impractical to gather all (potentially a large number of) fuzzy rules from FMEA users. In this paper, we introduce a new two-stage approach to reduce the number of fuzzy rules that needs to be gathered, and to satisfy the monotonicity property. In stage-1, a Genetic Algorithm (GA) is used to search for a small set of fuzzy rules to be gathered from FMEA users. In stage-2, the remaining fuzzy rules are deduced approximately by a monotonicity-preserving similarity reasoning scheme. The monotonicity property is exploited as additional qualitative information for constructing the FIS-based RPN model. To assess the effectiveness of the proposed approach, a real case study with information collected from a semiconductor manufacturing plant is conducted. The outcomes indicate that the proposed approach is effective in developing an FIS-based RPN model with only a small set of fuzzy rules, which is able to satisfy the monotonicity property for prioritization of failures in FMEA. IEEE 2015 Article PeerReviewed text en http://ir.unimas.my/id/eprint/8142/1/07104185.pdf Tay, Kai Meng and Jee, T.L and Lim, C.P (2015) A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis. IEEE Transaction on Reliability (99). pp. 1-9. ISSN 0018-9529 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7104185&filter%3DAND(p_IS_Number%3A4378406) 10.1109/TR.2015.2420300 |
| spellingShingle | QA Mathematics Tay, Kai Meng Jee, T.L Lim, C.P A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis |
| title | A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis |
| title_full | A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis |
| title_fullStr | A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis |
| title_full_unstemmed | A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis |
| title_short | A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis |
| title_sort | new two-stage fuzzy inference system-based approach to prioritize failures in failure mode and effect analysis |
| topic | QA Mathematics |
| url | http://ir.unimas.my/id/eprint/8142/ http://ir.unimas.my/id/eprint/8142/ http://ir.unimas.my/id/eprint/8142/ http://ir.unimas.my/id/eprint/8142/1/07104185.pdf |