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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Veröffentlicht in:Quality and reliability engineering international Jg. 39; H. 6; S. 2398 - 2421
Hauptverfasser: Sindhu, Tabassum Naz, Çolak, Andaç Batur, Lone, Showkat Ahmad, Shafiq, Anum
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 01.10.2023
Schlagworte:
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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Zusammenfassung: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.
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ISSN:0748-8017
1099-1638
DOI:10.1002/qre.3352