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A new two-stage fuzzy inference system-based approach to prioritize failures in failure mode and effect analysis

Jee, Tze Ling, Tay, Kai Meng and Lim, Chee Peng 2015, A new two-stage fuzzy inference system-based approach to prioritize failures in failure mode and effect analysis, IEEE transactions on reliability, vol. 64, no. 3, pp. 869-877, doi: 10.1109/TR.2015.2420300.

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Title A new two-stage fuzzy inference system-based approach to prioritize failures in failure mode and effect analysis
Author(s) Jee, Tze Ling
Tay, Kai Meng
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Journal name IEEE transactions on reliability
Volume number 64
Issue number 3
Start page 869
End page 877
Total pages 9
Publisher IEEE
Place of publication Champaign, Ill.
Publication date 2015-09-01
ISSN 0018-9529
1558-1721
Summary 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.
Language eng
DOI 10.1109/TR.2015.2420300
Field of Research 0803 Computer Software
0906 Electrical And Electronic Engineering
080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079904

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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Citation counts: TR Web of Science Citation Count  Cited 11 times in TR Web of Science
Scopus Citation Count Cited 4 times in Scopus
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