威布尔分布
人工神经网络
计算机科学
背景(考古学)
可靠性(半导体)
故障率
前馈神经网络
集合(抽象数据类型)
人工智能
累积分布函数
机器学习
数学优化
可靠性工程
数学
工程类
概率密度函数
统计
古生物学
程序设计语言
功率(物理)
物理
生物
量子力学
作者
Emanuel Federico Alsina,Giacomo Cabri,Alberto Regattieri
摘要
The failure prediction of components plays an increasingly important role in manufacturing. In this context, new models are proposed to better face this problem, and, among them, artificial neural networks are emerging as effective. A first approach to these networks can be complex, but in this paper, we will show that even simple networks can approximate the cumulative failure distribution well. The neural network approach results are often better than those based on the most useful probability distribution in reliability, the Weibull. In this paper, the performances of multilayer feedforward basic networks with different network configurations are tested, changing different parameters (e.g., the number of nodes, the learning rate, and the momentum). We used a set of different failure data of components taken from the real world, and we analyzed the accuracy of the approximation of the different neural networks compared with the least squares method based on the Weibull distribution. The results show that the networks can satisfactorily approximate the cumulative failure distribution, very often better than the least squares method, particularly in cases with a small number of available failure times. Copyright © 2015 John Wiley & Sons, Ltd.
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