Bayesian reliability analysis for fuzzy lifetime data

  • Authors:

  • Hong-Zhong Huang;Ming J. Zuo;Zhan-Quan Sun

  • Affiliations:

  • School of Mechatronics Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China;Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2G8;School of Mechanical Engineering, Dalian University of Technology, Dalian 116023, China

  • Venue:
  • Fuzzy Sets and Systems
  • Year:
  • 2006

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Abstract

Lifetime data are important in reliability analysis. Classical reliability estimation is based on precise lifetime data. It is usually assumed that observed lifetime data are precise real numbers. However, some collected lifetime data might be imprecise and are represented in the form of fuzzy numbers. Thus, it is necessary to generalize classical statistical estimation methods for real numbers to fuzzy numbers. Bayesian methods have proved to be very useful when the sample size is small. There is little study on Bayesian reliability estimation based on fuzzy lifetime data. Most of the reported works in this area is limited to single parameter lifetime distributions. In this paper, we propose a new method to determine the membership function of the estimates of the parameters and the reliability function of multi-parameter lifetime distributions. An artificial neural network is used to approximate the calculation process of parameter estimation and reliability prediction. The genetic algorithm is used to find the boundary values of the membership function of the estimate of interest at any cut level. This method can be used to determine the membership functions of the Bayesian estimates of multi-parameter distributions. The effectiveness of the proposed method is illustrated with normal and Weibull distributions.