Abstract
The Internet of Things is booming in the aviation industry, thus proper collection and analysis methods for fault data could support the realization of intelligent maintenance support and management of components. Failure of aeronautical equipment is typically described by a Weibull distribution. Here we carry out a sensitivity analysis using an objective Bayesian method to investigate the effect of descriptive statistics on reliability evaluations of random right censored aeronautical equipment failure data. We design a multiple Markov chain algorithm with censoring rate- and sample size-dependent variables. The algorithm estimates the scale and shape parameters of a Weibull distribution, with prior information of a large variance gamma distribution, for different censoring rates and sample sizes. Estimation deviations can be evaluated by mean time between failures, and by the mean and the variation factor of the distribution parameter. The numerical results show that, in a Weibull distribution, the estimation accuracy of an objective Bayesian method is acceptable when the sample size is more than 10 or the censoring rate less than 0.5. Otherwise, a better reliability evaluation method for small samples and high censoring rates should be explored.
Published Version
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