Continuous-state reliability measures based on fuzzy sets

This article proposes to use the theory and methods of fuzzy sets to model the reliability of a component or system experiencing continuous stochastic performance degradation. The performance characteristic variable, which indicates the continuous performance levels of degradable systems, is used to fuzzify the states of a component or system. The concept of an engineering or technological performance variable is understood by both customers and system designers and can be used to represent different degrees of success. Thus, the imprecision in the meaning of success/failure is quantified through the fuzzy success/failure membership function, which is defined over the performance characteristic variable. The proposed fuzzy reliability measures provide an alternative to model the continuous state behavior for a component or system as it evolves from a binary state to a multi-state and finally to a fuzzy state. The dynamic behavior of fuzzy reliability is investigated using the concept of a fuzzy random variable under appropriate stochastic performance degradation processes. This article also develops some reliability performance metrics that are able to capture the cumulative experiences of customers with the system. In addition, the perception and utility from the customers are utilized to develop customer-centric reliability performance measures.

[1]  Kailash C. Kapur An Integrated Customer-Focused Approach for Quality and Reliability , 1998 .

[2]  Witold Pedrycz,et al.  Fuzzy Systems Engineering - Toward Human-Centric Computing , 2007 .

[3]  Yu Liu,et al.  Reliability and performance assessment for fuzzy multi-state elements , 2008 .

[4]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[5]  Li Bing,et al.  A practical engineering method for fuzzy reliability analysis of mechanical structures , 2000 .

[6]  L. Zadeh Probability measures of Fuzzy events , 1968 .

[7]  Kai-Yuan Cai,et al.  Fuzzy states as a basis for a theory of fuzzy reliability , 1993 .

[8]  Ajit K. Verma,et al.  FUZZY DYNAMIC RELIABILITY EVALUATION OF A DETERIORATING SYSTEM UNDER IMPERFECT REPAIR , 2004 .

[9]  G. Levitin,et al.  Multi-state system reliabilit y , 2003 .

[10]  Vincenzo Cutello,et al.  Structure functions with fuzzy states , 1996, Fuzzy Sets Syst..

[11]  T. Aven On performance measures for multistate monotone systems , 1993 .

[12]  Ching-Hsue Cheng,et al.  Fuzzy system reliability analysis by interval of confidence , 1993 .

[13]  Zhaojun Li and Kailash C. Kapur Models and Measures for Fuzzy Reliability and Relationship to Multi-State Reliability , 2011 .

[14]  Kailash C. Kapur,et al.  Customer driven reliability: integration of QFD and robust design , 1997, Annual Reliability and Maintainability Symposium.

[15]  Richard E. Barlow,et al.  Coherent Systems with Multi-State Components , 1978, Math. Oper. Res..

[16]  Shyi-Ming Chen FUZZY SYSTEM RELIABILITY-ANALYSIS USING FUZZY NUMBER ARITHMETIC OPERATIONS (VOL 64, PG 31, 1994) , 1994 .

[17]  Kai-Yuan Cai,et al.  Introduction to Fuzzy Reliability , 1996 .

[18]  Yubin Liu,et al.  Fuzzy random reliability of structures based on fuzzy random variables , 1997, Fuzzy Sets Syst..

[19]  D. Pandey,et al.  Profust reliability of a gracefully degradable system , 2007, Fuzzy Sets Syst..

[20]  Jan M. van Noortwijk,et al.  A survey of the application of gamma processes in maintenance , 2009, Reliab. Eng. Syst. Saf..

[21]  Hsien-Chung Wu,et al.  Fuzzy Bayesian estimation on lifetime data , 2004, Comput. Stat..

[22]  Hsien-Chung Wu,et al.  Evaluate Fuzzy Riemann Integrals Using the Monte Carlo Method , 2001 .

[23]  Huibert Kwakernaak,et al.  Fuzzy random variables - I. definitions and theorems , 1978, Inf. Sci..

[24]  Gregory Levitin,et al.  Multi-State System Reliability - Assessment, Optimization and Applications , 2003, Series on Quality, Reliability and Engineering Statistics.

[25]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning - II , 1975, Inf. Sci..

[26]  D. Singer A fuzzy set approach to fault tree and reliability analysis , 1990 .

[27]  Chong-Hyung Lee Reliability Analysis of Multistate Systems with Multistate Components , 2007 .

[28]  Kailash C. Kapur,et al.  Customer-driven reliability models for multistate coherent systems , 1994 .

[29]  Dug Hun Hong,et al.  Fuzzy system reliability analysis by the use of Tω (the weakest t-norm) on fuzzy number arithmetic operations , 1997, Fuzzy Sets Syst..

[30]  María Angeles Gil,et al.  Fuzzy random variables , 2001, Inf. Sci..

[31]  Kailash C. Kapur,et al.  Dynamic performance measures for tools with multi-state wear processes and their applications for tool design and selection , 2010 .

[32]  Kai-Yuan Cai,et al.  Street-lighting lamps replacement: a fuzzy viewpoint , 1990 .

[33]  Yi Ding,et al.  Fuzzy universal generating functions for multi-state system reliability assessment , 2008, Fuzzy Sets Syst..

[34]  Kai Xu,et al.  A practical engineering method for fuzzy reliability analysis of mechanical structures , 2000, Reliab. Eng. Syst. Saf..

[35]  Kailash C. Kapur,et al.  Customer-centered reliability methodology , 1997, Annual Reliability and Maintainability Symposium.

[36]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[37]  Yung Wen Liu,et al.  Reliability measures for dynamic multistate nonrepairable systems and their applications to system performance evaluation , 2006 .

[38]  Hideo Tanaka,et al.  Fault-Tree Analysis by Fuzzy Probability , 1983 .

[39]  K. B. Misra,et al.  Use of fuzzy set theory for level-I studies in probabilistic risk assessment , 1990 .

[40]  Hideo Tanaka,et al.  Fault-Tree Analysis by Fuzzy Probability , 1981, IEEE Transactions on Reliability.

[41]  K. Kapur,et al.  Review and classification of reliability measures for multistate and continuum models , 1999 .

[42]  Witold Pedrycz,et al.  Fuzzy Systems Engineering , 2007 .

[43]  Yung Wen Liu,et al.  New patient-centered models of quality-of-life measures for evaluation of interventions for multi-stage diseases , 2008 .

[44]  Hong-Zhong Huang,et al.  Bayesian reliability analysis for fuzzy lifetime data , 2006, Fuzzy Sets Syst..

[45]  Yi Ding,et al.  Fuzzy Multi-State Systems: General Definitions, and Performance Assessment , 2008, IEEE Transactions on Reliability.