Reliability study of generalized Rayleigh distribution based on inverse power law using artificial neural network with Bayesian regularization
Yükleniyor...
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Mean time to failure, Reliability function, Artificial neural network, Mean residual life
Kaynak
Tribology International
WoS Q Değeri
Q1
Scopus Q Değeri
N/A
Cilt
185
