A comparative analysis of maximum likelihood estimation and artificial neural network modeling to assess electrical component reliability
Yükleniyor...
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
John Wiley and Sons
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
Özet
This study focuses on accurately predicting the behavior of new power functiondistribution using neural network and optimizing it using maximum likelihoodestimation. The main motivation of this study is that there is no study inthe literature that optimizes and predicts the reliability analysis of lifetimemodels by combining artificial neural networks and maximum likelihoodestimation methods. The numerical findings of the reliability investigationsand the values got from maximum likelihood estimation and artificial neuralnetwork modeling have been examined and investigated carefully. For theartificial neural network models, the R value was 0.99999 and the deviationratios were lower than 0.08%. The findings reveal that artificial neural networksare a powerful and useful mathematical tool for analyzing the reliabilityof lifetime models and numerical study findings via maximum likelihoodestimation are completely in accord with artificial neural network predictionresults.
Açıklama
Anahtar Kelimeler
mean time to failure, reliability function, maximum likelihood estimation, artificial neuralnetwork, failure rate function
Kaynak
Quality and Reliability Engineering International
WoS Q Değeri
Q3
Scopus Q Değeri
N/A
