A decreasing failure rate model with a novel approach to enhance the artificial neural network's structure for engineering and disease data analysis
A decreasing failure rate model with a novel approach to enhance the artificial neural network's structure for engineering and disease data analysis
复制标题
降低故障率模型，采用新方法增强工程和疾病数据分析的人工神经网络结构
DOI：
10.1016/j.triboint.2023.109231
复制
发表时间：
2024
影响因子：
6.2
通讯作者：
Tahani A. Abushal
中科院分区：
文献类型：
--
作者：
T. Sindhu;A. B. Çolak;S. Lone;Anum Shafiq;Tahani A. Abushal
该研究的重点是用于检查可靠性和生存理论研究中寿命随机变量分布特征的关键指标。在此分析中，包括概率密度函数时间、平均剩余寿命、平均故障间隔时间、危险率和可靠性函数在内的指标至关重要。探究的重点是这些与 Burr-Hatke 指数模型相关的重要参数。该研究的重点是用于检查可靠性和生存理论研究中寿命随机变量分布特征的关键指标。在此分析中，包括概率密度函数时间、平均剩余寿命、平均故障间隔时间、危险率和可靠性函数在内的指标至关重要。调查的重点是这些与 Burr-Hatke 指数模型相关的重要参数。该研究的一个关键组成部分是对人工智能方法的结果与传统的基于文献的方法的结果进行比较。这项比较研究揭示了人工神经网络框架在评估 Burr-Hatke 指数模型的技术特征时的表现如何。该研究可以通过计算多种性能指标来全面分析不断增长的神经网络的训练和预测能力。这种综合策略提高了我们对模型生存特征和可靠性的理解，为更广泛的研究领域做出了重大贡献。对于第一个神经网络模型，网络结构的均方误差估计为5.19E-04，其决定系数值为0.99987。对于第二个神经网络模型，决定系数值为0.99999，均方误差值为4.58E-06。结果充分揭示了神经网络结构极高的预测精度以及预测输出与最大似然估计技术的一致程度。
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 determination 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.