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

Using the generalized Rayleigh distribution and the inverse power law, this paper proposes a new reliability model and investigates the effect of the key parameters on reliability measurements. This proposed new model offers a more accurate way to model the performance of electronic components over their lifetimes. In order to analyze the reliability parameters, a multi-layer artificial neural network model has been developed by using the datasets generated by numerical methods and obtained in four different scenarios. Using the artificial neural network model with 5 neurons in the hidden layer, the reliability parameters Hazard Rate Function, Odds function, Reversed Hazard Rate Function, Mean Time to Failure and Mean Time Between Failures have been estimated. The results obtained have been analyzed comprehensively and explained with graphics. The study findings showed that there was a direct relationship between the reliability parameters examined in all scenarios and an increase in the Mean Time Between Failures value appeared for each scenario. However, it has also been seen that the developed artificial neural network can make predictions with very high accuracy and is a powerful engineering tool that can be utilized in reliability analysis.

Automated summary

Using the generalized Rayleigh distribution and the inverse power law, this paper proposes a new reliability model and investigates the effect of key parameters on reliability measurements. A multi-layer artificial neural network model is developed to analyze the reliability parameters using datasets obtained in four different scenarios. The results show a direct relationship between the reliability parameters and an increase in Mean Time Between Failures value for each scenario. Additionally, the developed artificial neural network demonstrates high accuracy and is a powerful tool for reliability analysis.