A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis

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.

1. Abbreviations & Acronyms

AARS

Approximate Analogical Reasoning Schema

D

Detection

FCBGA

Flip Chip Ball Grid Array

FIS

Fuzzy Inference System

FMEA

Failure Mode and Effect Analysis

FRI

Fuzzy Rule Interpolation

FRPN

fuzzy RPN

GA

Genetic Algorithm

O

Occurrence

MF

membership function

NLP

Non-Linear Programming

RPN

Risk Priority Number

S

Severity

S-1 FRs

Stage 1 Fuzzy Rules

S-2 FRs

Stage 2 Fuzzy Rules

SQP

Sequential Quadratic Programming

SR

Similarity Reasoning