A decreasing failure rate model with a novel approach to enhance the artificial neural network's structure for engineering and disease data analysis
Yükleniyor...
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
Özet
The study focuses on key metrics used to examine the characteristics of a lifetime random variable distribution in
reliability and survival theory research. In this analysis, metrics including the probability density function time,
mean residual lifespan, mean time between failures, hazard rate, and reliability function are essential. The focus
of the inquiry is these important parameters in relation to the Burr-Hatke exponential model specifically. The
study focuses on key metrics used to examine the characteristics of a lifetime random variable distribution in
reliability and survival theory research. In this analysis, metrics including the probability density function time,
mean residual lifespan, mean time between failures, hazard rate, and reliability function are essential The focus
of the inquiry is these important parameters in relation to the Burr-Hatke exponential model specifically. A key
component of the research is a comparison of the outcomes from the artificial intelligence approach and those
from conventional literature-based methodologies. This comparison study sheds light on how well the artificial
neural network framework performs while evaluating the Burr-Hatke exponential model’s technical features. The
study allows a comprehensive analysis of the training and prediction capabilities of the growing neural network
by calculating multiple performance measures. This comprehensive strategy improves our comprehension of the
model’s survival traits and reliability, offering significant contributions to the larger field of study. The network
structure’s mean square error was estimated to be 5.19E-04, and its coefficient of determination value was
0.99987 for the first neural network model. For the second neural network model, the coefficient of determi nation value was 0.99999 and the mean square error value was 4.58E-06. The outcomes amply revealed the
neural network structure’s extraordinarily high prediction accuracy and the degree to which the prediction
outputs agree with those of the Maximum Likelihood Estimation technique.
Açıklama
Anahtar Kelimeler
Artificial neural network, BHE Model, Mean residual lifetime, Reliability function
Kaynak
Tribology International
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
192
