基于跳跃连接-CNN-BiLSTM的轴承故障诊断

doi:10.16180/j.cnki.issn1007-7820.2023.11.009

Abstract

Abstract:

In view of the problem that bearing fault data contains irrelevant components such as noise and most bearing fault diagnosis methods cannot make full use of fault data, a fault diagnosis model based on skip connection- convolutional neural network- bidirectional long short-term memory network is proposed. The original vibration signal is converted into a two-dimensional time-frequency image using short-time Fourier transform, and the spatial and temporal features of the time-frequency image are extracted by convolutional neural network and long-short-term memory network respectively, and the classification is realized by combining the fully connected layer. Adding the structure of soft threshold attention and skip connection can make full use of the output features of different network levels while filtering out irrelevant components. The proposed diagnostic model is verified by MFPT(Machinery Failure Prevention Technology) bearing data, and the experimental results show that the proposed model can achieve a fault identification accuracy of 99.79%.

Key words: fault diagnosis, deep learning, convolutional neural network, bidirectional long short-term memory network, skip connection, attention mechanism, short-time Fourier transform, soft threshold

CLC Number:

TP389.1

YU Guangzeng,ZHANG Qiaoling,ZHOU Yurong. Bearing Fault Diagnosis Based on SC-CNN-BiLSTM[J].Electronic Science and Technology, 2023, 36(11): 56-65.

Figures/Tables 16

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编号	网络层	输出维度
1	Input Layer	(32, 1, 224, 224)
2	Conv2D	(32, 32, 224, 224)
3	MaxPooling2D	(32, 32, 112, 112)
4	Conv2D	(32, 64, 112, 112)
5	MaxPooling2D	(32, 64, 56, 56)
6	软阈值注意力模块	(32, 64, 56, 56)
7	Flatten	(32, 200 704)
8	Linear	(32, 64)
9	Flatten	(32, 64, 3 136)
10	Bidirectional (LSTM)	(32, 64)
11	Add	(32, 64)
12	Dropout	(32, 64)
13	Linear	(32, 256)
14	Dropout	(32, 256)
15	Linear	(32, 6)

编号	轴承类型	训练集	测试集	验证集
1	正常状态	672	68	100
2	外圈故障	672	68	100
3	内圈故障	672	68	100
4	中间轴轴承	160	20	20
5	油泵轴轴承	160	20	20
6	行星轴承	160	20	20
-	总数	2 496	264	360

编号	准确率/%	编号	准确率/%	平均准确率/%
1	100.00	6	99.74
2	99.74	7	100.00
3	99.48	8	99.74	99.79
4	99.22	9	100.00
5	100.00	10	100.00

编号	模型	准确率/%
1	CNN-BiLSTM	95.31
2	CNN-BiLSTM(跳跃连接)	97.40
3	CNN-BiLSTM(软阈值注意力)	97.92
4	SC-CNN-BiLSTM	99.79

编号	模型	准确率/%
1	CNN	92.42
2	CNN-BiLSTM	95.31
3	CNN-RF	94.80
4	SC-CNN-BiLSTM	99.79

Bearing Fault Diagnosis Based on SC-CNN-BiLSTM

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