Reliability and Maintenance Cost Forecasting for Systems with Multistate Components Using Artificial Neural Networks

P. Do, B. Iung, C. Cavalcante
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引用次数: 2

Abstract

In this paper, a study on the use of artificial neural networks for predicting the system reliability and maintenance cost of a system with multistate components is presented. TensorFlow and Keras APIs are used to build and train deep learning models under Python environment. Different numerical experimentations are carried out to illustrate the use of the robustness of the prediction approach. The obtained results show that artificial neural networks with TensorFlow and Keras APIs are a relevant tool for reliability and maintenance cost prediction.
基于人工神经网络的多状态部件系统可靠性与维护成本预测
本文研究了利用人工神经网络预测多状态部件系统的可靠性和维护成本。使用TensorFlow和Keras api在Python环境下构建和训练深度学习模型。进行了不同的数值实验来说明预测方法的鲁棒性。结果表明,基于TensorFlow和Keras api的人工神经网络是预测可靠性和维护成本的有效工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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