Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Downtime Data Analysis Based on Maximum Likelihood (MLE) to Estimate Parameters of Reliability Distributions

Version 1 : Received: 15 October 2020 / Approved: 16 October 2020 / Online: 16 October 2020 (14:52:26 CEST)

How to cite: Al-Bossly, A. Downtime Data Analysis Based on Maximum Likelihood (MLE) to Estimate Parameters of Reliability Distributions. Preprints 2020, 2020100355 (doi: 10.20944/preprints202010.0355.v1). Al-Bossly, A. Downtime Data Analysis Based on Maximum Likelihood (MLE) to Estimate Parameters of Reliability Distributions. Preprints 2020, 2020100355 (doi: 10.20944/preprints202010.0355.v1).

Abstract

Reliability analysis techniques are customary standard tools that are used for evaluating the performance of different equipment and devices in order to minimize their downtime. To predict the reliability, life data from a sample that is satisfactorily representative of the equipment should be fitted to the suitable statistical distribution. The parameterized distribution may be used to estimate essential characteristics such as failure rate; and probability at a precise time, as well as system reliability. In the current study, Weibull++/ALTA software package is used as a novel tool to fit the available data set to estimate the best fitted probability density function (PDF) using Maximum Likelihood (MLE) for parameter estimation. The determined distributions are then assessed using goodness-of-fit test to define how well it fits the available data set. There are multiple methods for determining goodness-of-fit. Weibull distributions and their special cases’ parameters have an effect on life times.

Subject Areas

Probability Distribution Function; Weibull Distribution; Parameter Estimation; Mean Time Between Failures; Failure Rate; Mean Time To Repair; Downtime and Reliability

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