On fuzzy inference system based failure mode and effect analysis (FMEA) methodology
Failure Mode and Effect Analysis (FMEA) is a popular problem prevention methodology. It utilizes a Risk Priority Number (RPN) model to evaluate the risk associated to each failure mode. The conventional RPN model is simple, but, its accuracy is argued. A fuzzy RPN model is proposed as an alternative...
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| Format: | Proceeding |
| Language: | English |
| Published: |
2009
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| Online Access: | http://ir.unimas.my/id/eprint/18496/ http://ir.unimas.my/id/eprint/18496/1/05370989%20%28abstrak%29.pdf |
| _version_ | 1848838526170300416 |
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| author | Tay, Kai Meng |
| author_facet | Tay, Kai Meng |
| author_sort | Tay, Kai Meng |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Failure Mode and Effect Analysis (FMEA) is a popular problem prevention methodology. It utilizes a Risk Priority Number (RPN) model to evaluate the risk associated to each failure mode. The conventional RPN model is simple, but, its accuracy is argued. A fuzzy RPN model is proposed as an alternative to the conventional RPN. The fuzzy RPN model allows the relation between the RPN score and Severity, Occurrence and Detect ratings to be of non-linear relationship, and it maybe a more realistic representation. In this paper, the efficiency of the fuzzy RPN model in order to allow valid and meaningful comparisons among different failure modes in FMEA to be made is investigated. It is suggested that the fuzzy RPN should be subjected to certain theoretical properties of a length function e.g. monotonicity, sub-additivity and etc. In this paper, focus is on the monotonicity property. The monotonicity property for the fuzzy RPN is firstly defined, and a sufficient condition for a FIS to be monotone is applied to the fuzzy RPN model. This is an easy and reliable guideline to construct the fuzzy RPN in practice. Case studies relating to semiconductor industry are then presented. |
| first_indexed | 2025-11-15T06:56:57Z |
| format | Proceeding |
| id | unimas-18496 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:56:57Z |
| publishDate | 2009 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-184962017-11-09T05:12:58Z http://ir.unimas.my/id/eprint/18496/ On fuzzy inference system based failure mode and effect analysis (FMEA) methodology Tay, Kai Meng TS Manufactures Failure Mode and Effect Analysis (FMEA) is a popular problem prevention methodology. It utilizes a Risk Priority Number (RPN) model to evaluate the risk associated to each failure mode. The conventional RPN model is simple, but, its accuracy is argued. A fuzzy RPN model is proposed as an alternative to the conventional RPN. The fuzzy RPN model allows the relation between the RPN score and Severity, Occurrence and Detect ratings to be of non-linear relationship, and it maybe a more realistic representation. In this paper, the efficiency of the fuzzy RPN model in order to allow valid and meaningful comparisons among different failure modes in FMEA to be made is investigated. It is suggested that the fuzzy RPN should be subjected to certain theoretical properties of a length function e.g. monotonicity, sub-additivity and etc. In this paper, focus is on the monotonicity property. The monotonicity property for the fuzzy RPN is firstly defined, and a sufficient condition for a FIS to be monotone is applied to the fuzzy RPN model. This is an easy and reliable guideline to construct the fuzzy RPN in practice. Case studies relating to semiconductor industry are then presented. 2009-12 Proceeding PeerReviewed text en http://ir.unimas.my/id/eprint/18496/1/05370989%20%28abstrak%29.pdf Tay, Kai Meng (2009) On fuzzy inference system based failure mode and effect analysis (FMEA) methodology. In: International Conference on Soft Computing and Pattern Recognition, 4 December 2009 through 7 December 2009, Malacca; Malaysia. http://ieeexplore.ieee.org/document/5370989/ |
| spellingShingle | TS Manufactures Tay, Kai Meng On fuzzy inference system based failure mode and effect analysis (FMEA) methodology |
| title | On fuzzy inference system based failure mode and effect analysis (FMEA) methodology |
| title_full | On fuzzy inference system based failure mode and effect analysis (FMEA) methodology |
| title_fullStr | On fuzzy inference system based failure mode and effect analysis (FMEA) methodology |
| title_full_unstemmed | On fuzzy inference system based failure mode and effect analysis (FMEA) methodology |
| title_short | On fuzzy inference system based failure mode and effect analysis (FMEA) methodology |
| title_sort | on fuzzy inference system based failure mode and effect analysis (fmea) methodology |
| topic | TS Manufactures |
| url | http://ir.unimas.my/id/eprint/18496/ http://ir.unimas.my/id/eprint/18496/ http://ir.unimas.my/id/eprint/18496/1/05370989%20%28abstrak%29.pdf |