A Single Input Rule Modules Connected Fuzzy FMEA Methodology for Edible Bird Nest Processing
Despite of the popularity of the fuzzy Failure Mode and Effects Analysis (FMEA) methodology, there are several limitations in combining the Fuzzy Inference System (FIS) and the Risk Priority Number (RPN) model. Two main limitations are: (1) it is difficul
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Abstract Despite of the popularity of the fuzzy Failure Mode and Effects Analysis (FMEA) methodology, there are several limitations in combining the Fuzzy Inference System (FIS) and the Risk Priority Number (RPN) model. Two main limitations are: (1) it is difficult and impractical to form a complete fuzzy rule base when the number of required rules is large; and (2) fulfillment of the monotonicity property is a difficult problem. In this paper, a new fuzzy FMEA methodology with a zero-order Single Input Rule Modules (SIRMs) connected FIS-based RPN model is proposed. An SIRMs connected FIS is adopted as an alternative to the traditional FIS to reduce the number of fuzzy rules required in the modeling process. To preserve the monotonicity property of the SIRMs-connected FIS-based RPN model, a number of theorems in the literature are simplified and adopted as the governing equations for the proposed fuzzy FMEA methodology. A case study relating to edible bird nest (EBN) processing in Sarawak (together with Sabah, known as the world’s number two source area of bird nest after Indonesia) is reported. In short, the findings in this paper contribute towards building a new fuzzy FMEA methodology using the SIRMs connected FIS-based RPN model. Besides that, the usefulness of the simplified theorems in a practical FMEA application is demonstrated.
1 Introduction Failure Mode and Effects Analysis (FMEA) is a popular tool for quality assurance and reliability improvement [1]. In FMEA, a failure mode occurs when a component, system, subsystem, or process fails to meet the designated intent [2, 3]. C. H. Jong K. M. Tay (&) Universiti Malaysia Sarawak, Kota Samarahan, Malaysia e-mail: [email protected] C. P. Lim Centre for Intelligent Systems Research, Deakin University, Geelong, Australia
V. Snášel et al. (eds.), Soft Computing in Industrial Applications, Advances in Intelligent Systems and Computing 223, DOI: 10.1007/978-3-319-00930-8_15, Springer International Publishing Switzerland 2014
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Traditionally, the Risk Priority Number (RPN) model (i.e. Eq. (1)) is used to rank failure modes [2]. Three risk factors, i.e. Severity (S), Occurrence (O) and Detect (D) [3–5], are multiplied to produce an RPN score. S and O are the frequency and seriousness (effects) of a failure mode, and D is the effectiveness to detect a failure mode before it reaches the customer [2]. RPN ¼ S O D
ð1Þ
Fuzzy concept was incorporated to FMEA methodology to allow uncertainty and imprecise information to be included [1]. Bowles and Paláez suggested using a Fuzzy Inference System (FIS) to aggregate S, O, and D ratings (namely an FISbased RPN model), instead of a simple product function [5]. An FIS-based RPN was introduced, for the following reasons. (i) It allows expert knowledge and experience to be incorporated [4, 6]; (ii) It is robust against uncertainty and vagueness [7]; (iii) It allows a nonlinear relationship between the RPN score and the three risk factors to be formed [5]; and (iv) The three risk factors c
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