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

The investigation of lifetime reliability analysis is vital for confirming the quality of devices, equipment, electronic tube flops, and so forth. Statistical investigators have become more interested in lifetime model exploration in recent years, particularly in the last decade, without considering...

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Vydané v:Quality and reliability engineering international Ročník 39; číslo 6; s. 2398 - 2421
Hlavní autori: Sindhu, Tabassum Naz, Çolak, Andaç Batur, Lone, Showkat Ahmad, Shafiq, Anum
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bognor Regis Wiley Subscription Services, Inc 01.10.2023
Predmet:
Artificial neural networks
Bayesian analysis
Mathematical analysis
Multilayers
Power law
Probability distribution functions
Regularization
Reliability analysis
ISSN:0748-8017, 1099-1638
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Shrnutí:The investigation of lifetime reliability analysis is vital for confirming the quality of devices, equipment, electronic tube flops, and so forth. Statistical investigators have become more interested in lifetime model exploration in recent years, particularly in the last decade, without considering the issue of modeling the metrics of model reliability using artificial neural networks (ANNs). This study addresses this vacuum by discussing the multilayer ANN with Bayesian regularization modeling for reliability metrics of generalized exponential model based on inverse power law (IPL). The numerical findings of the reliability investigations and the values obtained from the ANN have been examined and analyzed carefully. The findings show that ANNs are a powerful and useful mathematical tool for analyzing the reliability of lifetime model based on IPL. Finally, a real life framework is implemented that support the theory of a research study.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0748-8017
1099-1638
DOI:10.1002/qre.3352