臺灣博碩士論文加值系統

失效模式與效應分析(Failure mode and effect analysis; FMEA)是一種被廣泛地運用於各種產業的可靠度與風險評估手法，可用來辨別產品或流程中的潛在失效模式，依該失效所造成的效應來排定進行矯正作業或預防措施的優先順序，並進一步降低該失效發生的機會。此手法透過風險優先數(Risk Priority Number; RPN)的評估，將該失效的重要性加以量化，依其重要性來決定預防措施或矯正作業的優先順序，其中風險優先數為該失效的嚴重度、發生頻率、檢出性的三種等級之乘積。但是，實施失效模式與效應分析時，通常會發現以下幾種缺點: (1)失效模式與效應分析中的資訊通常是以主觀的語言來表示，以致於不客觀與不明確；(2)三種參數之間的相對重要性未必相同；(3)相同的風險優先數，未必代表具有相同的風險；(4)分析小組組員之間的專業知識不容易分享與傳承。為了改善這些缺失，本篇論文將提出一種以模糊理論為基礎的重要性評估手法，並進一步以一座污水處理廠為例，分別以傳統的失效模式與效應分析與以模糊理論為基礎的失效模式與效應分析來進行該污水處理系統的可靠度分析。

Failure mode and effect analysis (FMEA) is a widely used reliability analysis and risk assessment tool in various industries. This risk assessment tool is used to identify the potential failure modes of a product or a process, rank the priority for corrective action according to the respective effects of the failures, and eliminate the chance of the failures occurring. In traditional FMEA method, the risk priority number (RPN) is utilized to rank the failure modes in system, and is determined by finding the multiplication of severity, occurrence, and detection ratings of each failure mode. However, during performing FMEA, FMEA teams usually suffer from several problems: (1) the subjective and qualitative description in natural language; (2) the relative importance among the three parameter ratings; (3) the difference of risk representation between the same RPN; and (4) the knowledge share among FMEA team members. Thus, a new risk assessment system based on the fuzzy set theory and fuzzy rule base theory will be proposed to deal with these drawbacks in this thesis. Furthermore, an analysis of a sewage treatment plant will be presented to demonstrate the traditional FMEA and the proposed FMEA.

摘要 I
Abstract II
Content III
List of Figures V
List of Tables VII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Objective 3
1.4 Organization of Thesis 4
Chapter 2 Literature Review 7
2.1 Failure Mode and Effect Analysis (FMEA) 7
2.1.1 The History of FMEA 7
2.1.2 Design FMEA and Process FMEA 9
2.1.3 The Terminology of FMEA 10
2.2 Fuzzy Theory 15
2.2.1 Classical Set and Fuzzy Set 17
2.2.2 Fuzzy Numbers 21
2.2.3 Fuzzy Rule Base and Fuzzy Inference Engine 22
2.2.4 Defuzzifier 24
Chapter 3 Incorporation Fuzzy Theory in FMEA 28
3.1 The Procedure and Form of FMEA 28
3.1.1 FMEA Procedure 29
3.1.2 FMEA Form 32
3.2 FMEA based on Fuzzy Theory 36
3.2.1 Fuzzy Membership Function 37
3.2.2 Fuzzy Rule Base 42
3.2.3 Fuzzy Inference Process 43
3.2.4 Defuzzification 45
Chapter 4 Case Study 47
4.1 Introduction to Sewage Treatment System (STS) 47
4.2 The Results of the Traditional FMEA Method 51
4.2.1 The Rating Scales for STS 52
4.2.2 The FMEA Form for STS 54
4.2.3 The Results 55
4.3 The Results of the Fuzzy FMEA Method 57
4.3.1 The Membership Functions for STS 58
4.3.2 The Fuzzy IF-THEN Rules for STS 60
4.3.3 The Results 62
4.4 The Analysis of Results 65
Chapter 5 Conclusions 68
Reference 70

Bell, D., L. Cox, S. Jackson, and P. Schaefer [1992], “Using causal reasoning for automated failure modes and effects analysis,” Proceedings of Annual Reliability and Maintainability Symposium, 343-353.
Berkan, R.C. and S.L. Trubatch [1997], Fuzzy Systems Design Principles: Building Fuzzy IF-THEN Rule Bases, New Jersey: IEEE Press.
Besterfield, D.H. et al. [2003], Total Quality Management, 3rd edition, New Jersey: Prentice-Hall.
Bowles, J.B. and C.E. Pelaez [1995], “Fuzzy logic prioritization of failures in a system failure mode, effects, and criticality analysis,” Reliability Engineering and System Safety, 50: 205-213.
Braglia, M., M. Frosolini, and R. Montanari [2003], “Fuzzy criticality assessment model for failure modes and effects analysis,” The International Journal of Quality and Reliability Management, 20(4/5): 503-525.
IEC [1985], Analysis Techniques for System Reliability—Procedure for Failure Mode and Effects Analysis, Geneva: International Electrotechnical Commission.
IEC [1990], International Electrotechnical Vocabulary (IEV), Geneva: International Electrotechnical Commission.
Jenab, K. and B. S. Dhillon [2005], “Group-based failure effects analysis,” International Journal of Reliability, Quality and Safety Engineering, 12(4): 291-307.
Kara-Zaitri, C., A. Z. Keller, and P. V. Fleming [1992], “A smart failure mode and effect analysis package,” Proceedings of Annual Reliability and Maintainability Symposium, 414-421.
McDermott, R.E., R.J. Mikulak, and M.R. Beauregard [1996], The Basics of FMEA, Portland: Productivity, Inc.
MIL-STD-1629 [1974], Procedures for Performing a Failure Mode, Effects, and Criticality Analysis, Washington, D.C.: U.S. Department of Defense.
MIL-STD-1629A [1980], Procedures for Performing a Failure Mode, Effects, and Criticality Analysis, Washington, D.C.: U.S. Department of Defense.
Pelaez, C. E. and J. B. Bowles [1995], “Applying fuzzy cognitive maps knowledge-representation to failure modes effects analysis,” Proceedings of Annual Reliability and Maintainability Symposium, 450-456.
Pillay, A. and J. Wang [2003], “Modified failure mode and effects analysis using approximate reasoning,” Reliability Engineering and System Safety, 79: 69-85.
Teng, S. H. and S. Y. Ho [1996], “Failure mode and effects analysis: an integrated approach for product design and process control,” The International Journal of Quality and Reliability Management, 13(5): 8-26.
Wang, J., T. Ruxton, and C. R. Labrie [1995], “Design for safety of engineering systems with multiple failure state variables,” Reliability Engineering and System Safety, 50: 271-284.
Wang, L.X. [1997], A Course in Fuzzy Systems and Control, New Jersey: Prentice-Hall.
Xu, K., L.C. Tang, M. Xie, S.L. Ho, and M.L. Zhu [2002], “Fuzzy assessment of FMEA for engine systems,” Reliability Engineering and System Safety, 75: 17-29.
Zadeh, L.A. [1965], “Fuzzy sets,” Information and Control, 8: 338-353.
Zimmermann, H.-J. [1996], Fuzzy Set Theory and Its Applications, 3rd edition, Dordrecht: Kluwer Academic Publishers.