Beyond precision: accelerated life testing for fuzzy life time data
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Reliability analysis comprises statistical analysis techniques that make inferences based on life time data. Swift progress has been observed in life time data analyses during the last few decades. Accelerated life testing models are regarded as the most popular techniques for engineering life time data analysis. Their main aim is to model life times under different stress levels that are more severe than the usual stress level. The existing techniques consider life times as precise measurements and do not contemplate the imprecision of observations. In fact, life time measurements are not precise quantities but more or less fuzzy. Therefore, in addition to standard statistical tools, fuzzy model approaches are also essential. The current study generalizes some parametric and nonparametric classical estimation procedures for accelerated life testing in order to accommodate both fuzziness and random variation. The proposed estimators cover both uncertainties, which make them more applicable and practicable for life time analysis. The results of fuzzy life times are considered under various stress conditions, and comparisons with precise life time analysis are further presented in examples.
KeywordsAccelerated life testing Characterizing function Fuzzy number Non-precise data Real measurements
Compliance with ethical standards
Conflict of interest
In this paper, the authors have no conflict of interest.
Human and animal rights
Furthermore, this study does not have any involvement with human-related data and only used computer-simulated data.
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