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

被引:184
作者
Kai Meng Tay [1 ]
Lim, Chee [1 ,2 ]
机构
[1] Univ Sci Malaysia, Sch Elect & Elect Engn, George Town, Penang, Malaysia
[2] Japan Soc Promot Machine Ind, Tokyo, Japan
关键词
Failure modes and effects analysis; Risk analysis; Fuzzy control; Production processes;
D O I
10.1108/02656710610688202
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
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.
引用
收藏
页码:1047 / +
页数:25
相关论文
共 25 条
[1]  
BELL D, 1992, P ANNU REL MAINT SYM, P343
[2]  
Ben-Daya M., 1996, INT J QUAL RELIABIL, V13, P43, DOI DOI 10.1108/02656719610108297
[3]   FUZZY-LOGIC PRIORITIZATION OF FAILURES IN A SYSTEM FAILURE MODE, EFFECTS AND CRITICALITY ANALYSIS [J].
BOWLES, JB ;
PELAEZ, CE .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 1995, 50 (02) :203-213
[4]  
Chrysler Corporation Ford Motor Company and General Motors Corporation, 1995, POT FAIL MOD EFF AN
[5]   Effects analysis fuzzy inference system in nuclear problems using approximate reasoning [J].
Guimaraes, ACF ;
Lapa, CMF .
ANNALS OF NUCLEAR ENERGY, 2004, 31 (01) :107-115
[6]  
Hunt J. E., 1993, Intelligent Systems Engineering, V2, P119, DOI 10.1049/ise.1993.0012
[7]  
Ireson G W, 1995, HDB RELIABILITY ENG
[8]  
ISHIBUCHI H, 1993, SECOND IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1 AND 2, P1119, DOI 10.1109/FUZZY.1993.327358
[9]   Elicitation and fine-tuning of fuzzy control rules using symbiotic evolution [J].
Jamei, M ;
Mahfouf, M ;
Linkens, DA .
FUZZY SETS AND SYSTEMS, 2004, 147 (01) :57-74
[10]  
Jang J.S.R., 2005, FUZZY LOGIC TOOL BOX