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首页> 外文会议>International Forum on Electrical Engineering and Automation >Improved Convolutional Neural Network Fault Diagnosis Method Based on Dropout
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Improved Convolutional Neural Network Fault Diagnosis Method Based on Dropout

机译：基于辍学的卷积神经网络故障诊断方法

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摘要

Aiming at the over-fitting problem of traditional deep learning method in bearing fault diagnosis model, this paper proposes an improved convolutional neural network fault diagnosis method. This method introduces the Dropout optimization method at the fully connected layer of the neural network model, and temporarily discards some neurons from the neural network, thereby reducing network parameters and achieving data dimensionality reduction. By comparing and analyzing the method described in the paper with the traditional CNN network, the results show that the method described in the paper can effectively alleviate the overfitting phenomenon of the traditional CNN network model in bearing fault diagnosis. The model has a strong generalization ability and diagnosis. The result has a higher accuracy.
机译：针对传统深层学习方法的过度拟合问题在轴承故障诊断模型中，提出了一种改进的卷积神经网络故障诊断方法。该方法在神经网络模型的完全连接层介绍了辍学优化方法，暂时丢弃了神经网络的一些神经元，从而减少了网络参数并实现了数据维度降低。通过使用传统的CNN网络中文所述的方法进行比较和分析，结果表明，论文中描述的方法可以有效地缓解传统CNN网络模型在轴承故障诊断中的过度拟合现象。该模型具有强大的泛化能力和诊断。结果具有更高的准确性。

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