基于决策树与神经网络结合的滚动轴承故障诊断方法
首发时间:2018-07-20
摘要:提出了一种基于决策树与神经网络方法结合的改进滚动轴承故障诊断方法。该方法对滚动轴承振动信号进行EMD分解,使用决策树对分解信号进行故障预测,然后使用属性融合神经网络对决策树预测结果进行学习,将决策树故障特征融合到神经网络分类器中。结果表明,该方法具有更高的故障识别率,可以准确、有效地识别滚动轴承的故障类型。
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A rolling bearing fault diagnosis method based on decision tree and neural network
Abstract:This paper proposes an improved fault diagnosis method based on the fusion of decision tree and neural network. This method performs EMD decomposition on the rolling bearing vibration signal, uses a decision tree to predict the failure of the decomposition signal, and then uses the attribute fusion neural network to learn the decision tree prediction results, and integrates the fault features of the decision tree into the neural network classifier. The results show that this method has a higher fault recognition rate and can accurately and effectively identify the type of faults in rolling bearings.
Keywords: decision tree neural network fault diagnosis
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基于决策树与神经网络结合的滚动轴承故障诊断方法
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