基于BN-1DCNN的旋转机械故障诊断研究

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摘要为了对旋转机械的故障特征进行自适应提取，实现智能故障诊断，提出了一种基于批量归一化的一维卷积神经网络（convolutional neural networks, CNN）模型。由于卷积神经网络通常应用于二维图像或三维视频领域，故通过将卷积核改进为一维卷积核来实现对采集的一维振动数据的直接卷积，并且采用了批归一化层来防止过拟合，采用HZXT-008小型转子实验台采集的数据对该方法进行验证。试验结果表明该方法平均诊断准确率高达98.43%，并且与其他模型相比稳定性更高。该方法实现了大量样本下旋转机械不同故障类型的故障特征自适应提取与故障类型的准确识别。

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	冯浩楠
	付胜
	胥永刚

关键词 ：深度学习, 卷积神经网络（CNN）, 旋转机械, 故障诊断

Abstract：Here, to adaptively extract fault features of rotating machinery and realize intelligent fault diagnosis, a 1D convolutional neural network (CNN) model based on batch normalization, i.e., BN-1DCNN model was proposed. Because CNN is usually used in fields of 2D image or 3D video, the convolution kernel was changed to 1D one to realize direct convolution for the collected 1D vibration data, and the batch normalization layer was used to prevent over-fitting. Finally, the data collected using HZXT-008 small rotor test platform was used to verify the proposed BN-1DCNN model. The test results showed that the average diagnostic accuracy of the proposed BN-1DCNN model can reach 98.43%, this model is more stable than other models; BN-1DCNN model can realize adaptive fault feature extraction and fault type identification of rotating machinery under a large number of samples.

Key words： deep learning convolutional neural networks (CNN) rotating machinery fault diagnosis

收稿日期: 2020-03-06 出版日期: 2021-10-15

引用本文:

冯浩楠，付胜，胥永刚. 基于BN-1DCNN的旋转机械故障诊断研究[J]. 振动与冲击, 2021, 40(19): 302-308.
FENG Haonan, FU Sheng, XU Yonggang. Fault diagnosis of rotating machinery based on BN-1DCNN model. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(19): 302-308.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2021/V40/I19/302

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