基于小波变换和改进残差神经网络的故障诊断方法

doi:10.12305/j.issn.1001-506X.2026.06.23

Abstract

Abstract:

Traditional deep neural networks need to increase the number of network layers to enhance the characterization ability and improve the network fault diagnosis accuracy when dealing with large-volume, multi-category, multi-degree fault sample data, but the increase in network depth can easily lead to problems such as gradient disappearance, which results in a low upper limit of recognition accuracy. A method that combines the wavelet packet transform with the residual neural network is presented to enhance the recognition accuracy of large-volume motor fault sample data. The residual neural network is improved using an exponential linear unit. The results demonstrate that employing exponential linear unit significantly enhances residual neural network training stability and prediction accuracy. On a 42-types multi-sensor electromechanical fault dataset, the proposed method achieves at least 13% higher recognition accuracy than comparable networks without residual layers and exponential linear unit. Therefore, the proposed method, which integrates the continuous wavelet transform with a residual neural network enhanced by linear exponential units, significantly mitigates the vanishing gradient problem and effectively overcomes the accuracy limitations of traditional deep neural networks, thereby providing a highly reliable solution for the intelligent diagnosis of multiple faults in complex electromechanical systems.

Key words: deep learning, fault diagnosis, wavelet packet transform, residual neural network

CLC Number:

TP 183

Shengzhi YUAN, Shaolei WANG, Jing LI, Renkai XIAO, Haotian TAN, Jianwei LAI, Jiangtao XU. Fault diagnosis method based on wavelet packet transform and improved residual neural network[J]. Systems Engineering and Electronics, 2026, 48(6): 2031-2041.

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References 31

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层类型	具体参数
输入层	输入数据二维图
卷积层1（Conv1）	卷积核大小7×7，步长2
最大池化层（Maxpool）	池化窗口大小3×3，步长2
卷积层2（Conv2）	$ \left[\begin{array}{l}3 \times 3,64 \\3 \times 3,64\end{array}\right] \times 2 $，步长2
卷积层3（Conv3）	$ \left[\begin{array}{l}3 \times 3,128 \\3 \times 3,128\end{array}\right] \times 2 $，步长2
卷积层4（Conv4）	$ \left[\begin{array}{l}3 \times 3,256 \\3 \times 3,256\end{array}\right] \times 2 $，步长2
卷积层5（Conv5）	$ \left[\begin{array}{l}3 \times 3,512 \\3 \times 3,512\end{array}\right] \times 2 $，步长2
全连接层（Fc）	输入维度512，输出维度42

信号状况	样本集数量	标签
正常数据	2900	0
不平衡故障	2900	1
水平平行错位	2900	2
垂直平行错位	2900	3
内轨道故障	2900	4
外轨道故障	2900	5

项目	训练耗时	检测耗时
CNN网络训练	4376	8.55
ResNet网络训练	5408	10.62
Transformer网络训练	58101	116.22
ConvNext网络训练	57665	115.33

项目	计算占用/GB
CNN网络训练	14.2
ResNet网络训练	14.6
Transformer网络训练	14.1
ConvNext网络训练	10.9

训练样本数据量	小波降噪+ MLP	小波降噪+ CNN	小波频域分解降噪+CNN	小波频域分解降噪+ResNet
原数据大小	12.07	58.85	77.85	92.95
4倍降采样	15.09	63.26	75.33	92.32
8倍降采样	16.54	60.50	73.25	92.25

Fault diagnosis method based on wavelet packet transform and improved residual neural network

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Figures/Tables 15

References 31

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层名称	ReLU激活概率	ELU激活概率
Conv1	0.522388	1
Conv2-Block1	0.872158	1
Conv2-Block2	0.712385	1
Conv3-Block1	0.730315	1
Conv3-Block2	0.503255	1
Conv4-Block1	0.657838	1
Conv4-Block2	0.50858	1
Conv5-Block1	0.657683	1
Conv5-Block2	0.494633	1

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