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.
- Gil MÁ, Hryniewicz O (2009) Statistics with imprecise data. In: Meyers RA (ed) Encyclopedia of complexity and systems science. Springer, New York, pp 8679–8690Google Scholar
- Liu L, Li X-Y, Zhang W, Jiang T-M (2015) Fuzzy reliability prediction of rotating machinery product with accelerated testing data. J Vibroeng 17(8):4193–4210Google Scholar
- Nelson W (2009) Accelerated testing: statistical models, test plans, and data analysis. Wiley, New JerseyGoogle Scholar
- Shafiq M, Viertl R (2015b) Generalized Kaplan Meier estimator for fuzzy survival times. Sil Stat Rev 13(19):7–14Google Scholar
- Viertl R (2004) Accelerated life testing, fuzzy information and generalized probability. In: Balakrishnan N, Nikulin M, Mesbah M, Limnios N (eds) Parametric and semiparametric models with applications to reliability, survival analysis, and quality of life, statistics for industry and technology. Birkhäuser, Boston, pp 99–105Google Scholar
- Xu H, Li X, Liu L (2015) Statistical analysis of accelerated life testing under Weibull distribution based on fuzzy theory. In: 2015 annual reliability and maintainability symposium (RAMS), IEEE, Palm Harbor, FL, pp 1–5Google Scholar