Reliability Analysis for Electronic Devices Using Generalized Exponential Distribution

Abstract

Electronic devices are integral part of our life and modeling their lifetime is the most challenging and interesting field in reliability analysis. To investigate the failure behavior of electronic devices reliability analysis is commonly used. In the literature, however, it is reported that one in five electronic device failure is a result of corrosion and to save electricity and predict future failures, it is important to summarize the data by some flexible probability models. This will not only help the electronic companies, but also the users by providing them information about the maximum voltage level that a particular device can bear. This article deals with the reliability analysis of electronic devices under different voltages assuming modified generalized exponential distribution and beta generalized exponential distribution using the inverse power law rule. The parameters of the modified distribution are estimated assuming Bayesian inference to include prior information. Sensitivity of hyperparameters and selection of an appropriate probability model is also a part of this study.

A reliability analysis for electronic devices under an extension of exponentiated perks distribution

“…Other additive distributions with applications for ED can be seen in Thanh Thach and Briš, 19 Lemonte et al, 20 He et al 21 Some authors propose hybrid distributions with reliability applications, with which they combine two distributions to describe non-monotone failure times. For example Lee et al, 22 Silva et al, 23 Cordeiro et al, 24 Pal et al, 25 Ali et al, 26 Ali et al 27 establish the use of the Exponential and Beta distributions in combination with the WD, with which they can describe the non-monotonic failures exhibited by the ED.…”

A geometric process with Hjorth marginal: Estimation, discrimination, and reliability data modeling

“…Additionally, creating an ASP with the Hjorth distribution, an important probability model for reliability engineering, and obtaining estimators by taking into account different approaches to process parameters, and investigating their statistical properties are still among the topics that need to be investigated. Also, the following distributions provided their merit in the reliability applications: Beta generalized Weibull distribution, 39 the mixture of transmuted Fréchet distribution, 40 and generalized exponential distribution 41 . Therefore, these probability models may be employed as marginal distributions for the GP.…”

“…Also, the following distributions provided their merit in the reliability applications: Beta generalized Weibull distribution, 39 the mixture of transmuted Fréchet distribution, 40 and generalized exponential distribution. 41 Therefore, these probability models may be employed as marginal distributions for the GP.…”

Reliability study of generalized exponential distribution based on inverse power law using artificial neural network with Bayesian regularization

“…Refs. 10,11 have also studied data on the failure time of a SMEC. We first estimate the MLE of the underlying model using the data under consideration, and then we use these MLEs to estimate the reliability of SMEC under IPL-GED.…”

“…The two-parameter Weibull diffusion model was discussed and studied by Nadi et al 8 Rastayesh et al 9 conducted a comparison of the exponential distribution with the Weibull distribution for assessing reliability. Ali et al 10 used the IPL rule to analyze the reliability of electronic equipment under various voltages, assuming modified generalized exponential distribution (GED) and beta GED.…”