Fuzzy FMEA with a guided rules reduction system for prioritization of failures

Tay, Kai Meng and Lim, Chee Peng 2006, Fuzzy FMEA with a guided rules reduction system for prioritization of failures, International Journal of Quality and Reliability Management, vol. 23, no. 8, pp. 1047-1066, doi: 10.1108/02656710610688202.

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Title Fuzzy FMEA with a guided rules reduction system for prioritization of failures
Author(s) Tay, Kai Meng
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Journal name International Journal of Quality and Reliability Management
Volume number 23
Issue number 8
Start page 1047
End page 1066
Total pages 20
Publisher Emerald
Place of publication Bingley, U. K.
Publication date 2006
ISSN 0265-671X
Keyword(s) Failure modes and effects analysis
Fuzzy control
Production processes
Risk analysis
Failure modes and effects analysis
Summary Purpose – To propose a generic method to simplify the fuzzy logic-based failure mode and effect analysis (FMEA) methodology by reducing the number of rules that needs to be provided by FMEA users for the fuzzy risk priority number (RPN) modeling process.

Design/methodology/approach – The fuzzy RPN approach typically requires a large number of rules, and it is a tedious task to obtain a full set of rules. The larger the number of rules provided by the users, the better the prediction accuracy of the fuzzy RPN model. As the number of rules required increases, ease of use of the model decreases since the users have to provide a lot of information/rules for the modeling process. A guided rules reduction system (GRRS) is thus proposed to regulate the number of rules required during the fuzzy RPN modeling process. The effectiveness of the proposed GRRS is investigated using three real-world case studies in a semiconductor manufacturing process.

Findings – In this paper, we argued that not all the rules are actually required in the fuzzy RPN model. Eliminating some of the rules does not necessarily lead to a significant change in the model output. However, some of the rules are vitally important and cannot be ignored. The proposed GRRS is able to provide guidelines to the users which rules are required and which can be eliminated. By employing the GRRS, the users do not need to provide all the rules, but only the important ones when constructing the fuzzy RPN model. The results obtained from the case studies demonstrate that the proposed GRRS is able to reduce the number of rules required and, at the same time, to maintain the ability of the Fuzzy RPN model to produce predictions that are in agreement with experts' knowledge in risk evaluation, ranking, and prioritization tasks.

Research limitations/implications – The proposed GRRS is limited to FMEA systems that utilize the fuzzy RPN model.

Practical implications – The proposed GRRS is able to simplify the fuzzy logic-based FMEA methodology and make it possible to be implemented in real environments.

Originality/value – The value of the current paper is on the proposal of a GRRS for rule reduction to enhance the practical use of the fuzzy RPN model in real environments.
Language eng
DOI 10.1108/02656710610688202
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2006 Emerald
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048773

Document type: Journal Article
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