Reliability inference in Weibull model using type II fuzzy censored data

For manufacturers, assessing the reliability of an existing product is a fundamental task for improving the product’s reliability or quality. Conventional reliability estimation methods for Weibull distribution are based on precise lifetime data. However, in real world situations, the results of the experimental performance cannot always be recorded or measured precisely, but each observable event may only be identified with a fuzzy subset of the sample space. In this paper the model properties and reliability measures of Weibull distribution are derived and studied when the observations are fuzzy. For the estimation purposes of the parameter and other reliability characteristics maximum likelihood and Bayes approaches are used. Monte Carlo simulation study is conducted to compare the performance of the various estimates developed. In view of cost and time constraints, type II censored sample data are used in estimation.