Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network,Advanced Theory and Simulations

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Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2022-06-01 , DOI: 10.1002/adts.202200100
Anum Shafiq ₁ , Andaç Batur Çolak ₂ , Chetan Swarup ₃ , Tabassum Naz Sindhu ₄ , Showkat Ahmad Lone ₃

Affiliation

The study of reliability analysis of mixture model is essential in confirming the quality of devices, equipment, and electronic tube flops etc. In recent years, statisticians have developed more interest in mixture model research, notably in the last decade, without taking into account the issue of modeling the metrics of reliability of mixture models using artificial neural networks. In the present study, the influence of pertinent parameters on reliability metrics is studied. The effect of components and mixing parameters for failure function, reversed hazard rate function, mean time to failure, hazard rate function, mean inactivity time, mean residual life, reliability function, Mills Ratio profiles are plotted and discussed. A multi-layer artificial neural network is developed using the numerical analysis results obtained using four different scenarios. The values extracted from the artificial neural network and the numerical findings of the reliability studies are extensively compared and examined. The deviation rates obtained for the developed artificial neural network model are obtained at values lower than 0.12%. The outcomes demonstrate that neural networks are a powerful and effective mathematical tool that can be used in the reliability analysis of mixing models.

中文翻译：

基于Lindley分布与人工神经网络混合的可靠性分析

混合模型的可靠性分析研究对于确定设备、设备和电子管触发器等的质量至关重要。近年来，统计学家对混合模型研究产生了更多的兴趣，特别是在过去十年中，没有考虑到使用人工神经网络对混合模型的可靠性指标进行建模的问题。在本研究中，研究了相关参数对可靠性指标的影响。组件和混合参数对故障函数、反向危险率函数、平均故障时间、危险率函数、平均非活动时间、平均剩余寿命、可靠性函数、米尔斯比曲线的影响进行了绘制和讨论。使用四种不同场景获得的数值分析结果开发了多层人工神经网络。从人工神经网络中提取的值和可靠性研究的数值结果进行了广泛的比较和检查。为开发的人工神经网络模型获得的偏差率低于 0.12%。结果表明，神经网络是一种强大而有效的数学工具，可用于混合模型的可靠性分析。

更新日期：2022-06-01

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