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首页> 外文会议>International conference on neural information processing;Annual conference of Asia-Pacific Neural Network Society >An Approach for Feature Extraction and Diagnosis of Motor Rotor Bearing Based on Convolution Neural Network
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An Approach for Feature Extraction and Diagnosis of Motor Rotor Bearing Based on Convolution Neural Network

机译：基于卷积神经网络的电机转子轴承特征提取与诊断方法

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摘要

The traditional rotor bearing fault diagnosis and analysis method is difficult to get the prior knowledge and experience, resulting in the low accuracy of fault diagnosis. In this paper, a method of fault feature extraction and diagnosis of rotor bearing based on convolution neural network is proposed. This method uses the chaotic characteristic of the vibration signal of the rotor bearing, uses the phase space reconstruction method to obtain the embedding dimension as the scale of the convolution neural network input composition, avoid the limitation of traditional frequency analysis method in the process of decomposition and transformation, the fault information can be extracted more comprehensively. In order to make full use of the advantages of the convolution neural network in the field of two-dimensional image analysis and improve the accuracy of the fault diagnosis model, a method of learning input form neural network based on convolution neural network for grayscale graph is proposed. The results of the simulation show the effectiveness of the method.
机译：传统的转子轴承故障诊断和分析方法难以获得先验知识和经验，导致故障诊断的准确性较低。提出了一种基于卷积神经网络的转子轴承故障特征提取与诊断方法。该方法利用了转子轴承振动信号的混沌特性，利用相空间重构方法获得了嵌入维数作为卷积神经网络输入成分的尺度，避免了传统频率分析方法在分解过程中的局限性。通过转换，可以更全面地提取故障信息。为了充分利用卷积神经网络在二维图像分析领域的优势，提高故障诊断模型的准确性，提出了一种基于卷积神经网络的灰度图学习输入神经网络的方法。建议的。仿真结果表明了该方法的有效性。

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