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 |
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01.10.2023
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| Abstract | 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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| AbstractList | 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. 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. |
| Author | Çolak, Andaç Batur Sindhu, Tabassum Naz Lone, Showkat Ahmad Shafiq, Anum |
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| SubjectTerms | Artificial neural networks Bayesian analysis Mathematical analysis Multilayers Power law Probability distribution functions Regularization Reliability analysis |
| Title | Reliability study of generalized exponential distribution based on inverse power law using artificial neural network with Bayesian regularization |
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