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Fault diagnosis of rolling element bearings using artificial neural networks

机译:基于人工神经网络的滚动轴承故障诊断

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The basic concept of neural network and its application to diagnosis of rolling element bearings is described. Neural networks based on neurobiological systems are more adept at classification and identification tasks than conventional statistical and expert systems. In this paper the unsupevised learning method based on Kohonen network is used to study the classification of problem of common faults in rolling element bearing. Example pattrns based on vibration analysis are used to train the network. The method successfully predicts the class of faults.
机译:描述了神经网络的基本概念及其在滚动轴承的诊断中的应用。与传统的统计和专家系统相比,基于神经生物学系统的神经网络更擅长分类和识别任务。本文采用基于Kohonen网络的未完成学习方法对滚动轴承的常见故障进行分类研究。基于振动分析的示例模式用于训练网络。该方法成功地预测了故障的类别。

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