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A Neural Network Approach to Find The Cumulative Failure Distribution: Modeling and Experimental Evidence
Author(s) -
Alsina Emanuel Federico,
Cabri Giacomo,
Regattieri Alberto
Publication year - 2016
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1773
Subject(s) - weibull distribution , artificial neural network , computer science , context (archaeology) , reliability (semiconductor) , failure rate , feedforward neural network , set (abstract data type) , artificial intelligence , cumulative distribution function , machine learning , mathematical optimization , reliability engineering , mathematics , engineering , probability density function , statistics , paleontology , power (physics) , physics , quantum mechanics , biology , programming language
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.