基于变分模态提取及轻量级网络的滚动轴承故障诊断

doi:10.13229/j.cnki.jdxbgxb.20231047

Abstract

Abstract:

A rolling bearing fault diagnosis method combining variational mode extraction （VME） and lightweight convolutional neural network （CNN） was designed to solve the problems of low diagnostic performance of CNN in complex industrial environments as well as the problem of large number of parameters. VME was used to extract the desired modes in the vibration signals collected from multiple sensors and construct the multi-sensor grayscale feature maps to eliminate information interference while enabling data fusion. The residual structure and ultra-lightweight subspace attention module（ULSAM） are introduced on the basis of SqueezeNet to construct a lightweight residual attention convolutional neural network （LRACNN）. The method has a high fault recognition rate and diagnostic stability in complex environments.

Key words: fault diagnosis, rolling bearing, convolutional neural network, attention mechanism, lightweight

CLC Number:

TH17

Zhi-gang FENG,Shou-qi WANG,Ming-yue YU. Rolling bearing fault diagnosis based on variational mode extraction and lightweight network[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(6): 1883-1891.

Figures/Tables 16

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Table 1

Fig.7

Fig.8

Table 2

Center frequencies of bearing vibration signals of different fault types at the DE and FE under different working conditions"

标签	$ω d / H z$
	A0		A1		A2		A3
	DE	FE	DE	FE	DE	FE	DE	FE
Nor	1 000	1 000	1 100	1 000	1 100	1 000	2 100	2 100
Ba1	3 300	4 100	3 400	4 100	3 400	4 100	3 400	1 400
Ba2	3 200	4 100	3 300	4 100	3 200	1 400	1 400	1 400
Ba3	3 200	4 100	3 300	400	3 400	1 100	3 300	1 100
In1	3 600	1 500	3 500	1 500	3 500	1 400	3 600	1 400
In2	3 400	4 300	3 500	4 300	3 500	4 300	1 400	1 100
In3	2 900	500	2 900	1 800	2 800	3 300	2 800	3 300
Ou1	3 400	3 300	2 800	3 300	2 800	3 300	3 300	3 300
Ou2	3 400	4 100	3 400	4 100	3 500	4 100	3 400	4 100
Ou3	2 800	4 100	3 400	600	3 500	600	3 400	5 100

Table 2

Fig.9

Table 3

Table 4

Fig.10

Table 5

Table 6

References 15

[1]	Yang D, Karimi H R, Sun K. Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples[J]. Neural Networks, 2021, 14: 133-144.
[2]	陈晓雷, 孙永峰, 李策, 等. 基于卷积神经网络和双向长短期记忆的稳定抗噪声滚动轴承故障诊断[J]. 吉林大学学报: 工学版, 2022, 52(2): 296-309.
	Chen Xiao-lei, Sun Yong-feng, Li Ce, et al. Stable anti⁃noise fault diagnosis of rolling bearing based on CNN⁃BiLSTM[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(2): 296-309.
[3]	Gu J, Peng Y, Lu H, et al. A novel fault diagnosis method of rotating machinery via VMD, CWT and improved CNN[J]. Measurement, 2022, 200: No.111635.
[4]	Yao D, Liu H, Yang J, et al. A lightweight neural network with strong robustness for bearing fault diagnosis[J]. Measurement, 2020, 159: No.107756.
[5]	Bai R, Xu Q, Meng Z, et al. Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation[J]. Measurement, 2021, 184: No.109885.
[6]	Zhong H, Lv Y, Yuan R, et al. Bearing fault diagnosis using transfer learning and self-attention ensemble lightweight convolutional neural network[J]. Neurocomputing, 2022, 501: 765-777.
[7]	Wang S, Feng Z. Multi-sensor fusion rolling bearing intelligent fault diagnosis based on VMD and ultra-lightweight GoogLeNet in industrial environments[J]. Digital Signal Processing, 2024, 145: No.104306.
[8]	王军, 张维通, 闫正兵, 等. 基于迁移QCNN的孪生网络轴承故障诊断方法[J]. 计算机测量与控制, 2024, 32(4): 6-12.
	Wang Jun, Zhang Wei-tong, Yan Zheng-bing, et al. Twin network-based bearing fault diagnosis method with transfer QCNN[J]. Computer Measurement & Control, 2024, 32(4): 6-12.
[9]	Nazari M, Sakhaei S M. Variational mode extraction: A new efficient method to derive respiratory signals from ECG[J]. IEEE Journal of Biomedical and Health Informatics, 2018, 22(4): 1059-1067.
[10]	Saini R, Jha N K, Das B, et al. ULSAM: Ultra-ightweight ubspace ttention odule for ompact onvolutional eural etworks[C]∥2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass, CO, USA, 2020: 1616-1625.
[11]	Ye M, Yan X, Chen N, et al. Intelligent fault diagnosis of rolling bearing using variational mode extraction and improved one-dimensional convolutional neural network[J]. Applied Acoustics, 2023, 202: No.109143.
[12]	Smith W A, Randall R B. Rolling element bearing diagnostics using the case western reserve university data: a benchmark study[J]. Mechanical Systems and Signal Processing, 2015, 64-65: 100-131.
[13]	Wang Y, Yan J, Sun Q, et al. Bearing intelligent fault diagnosis in the industrial internet of things context: a lightweight convolutional neural network[J]. IEEE Access, 2020, 8: 87329-87340.
[14]	Ling L, Wu Q, Huang K, et al. A lightweight bearing fault diagnosis method based on multi-channel depthwise separable convolutional neural network[J]. Electronics, 2022, 11(24): No.4110.
[15]	Xu Z, Mei X, Wang X, et al. Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors[J]. Renewable Energy, 2022, 182: 615-626.

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标签	故障类型	传感器	负载/HP	故障直径/mm	数据长度	样本数量
Nor	健康	DE和FE	0~3	—	1 024	200
Ba1	滚动体			0.177 8	1 024	200
Ba2	滚动体			0.355 6	1 024	200
Ba3	滚动体			0.533 4	1 024	200
In1	内圈			0.177 8	1 024	200
In2	内圈			0.355 6	1 024	200
In3	内圈			0.533 4	1 024	200
Ou1	外圈			0.177 8	1 024	200
Ou2	外圈			0.355 6	1 024	200
Ou3	外圈			0.533 4	1 024	200

方法	VME	VMD	EMD	ITD	WPD
正确率/%	100	99.50	97.55	98.80	99.25
时间/s	0.131	2.057	0.182	0.148	0.168

模型	正确率/%	训练时间/s	模型参数/MB
LRACNN	100	73.40	0.045
SqueezeNet	99.200	83.80	0.057
LCNN^［13］	97.386	1 623.00	15.522
ShuffleNet^［13］	92.094	962.00	4.385
MCDS-CNN^［14］	99.790	123.62	1.580

模型	SNR/dB
模型	-4	-2	0	2	4	6
LRACNN	93.30	94.65	96.50	97.10	97.75	98.65
SqueezeNet	91.60	92.85	94.60	96.65	96.70	97.40
CNN+ Attention^［2］	83.96	87.57	90.33	92.82	93.69	95.08
AAnNet^［2］	81.49	88.22	95.44	96.68	97.39	98.33
MSCNN- BiLSTM^［15］	87.00	93.00	96.70	97.50	99.24	99.24

负载/HP	正确率/%
负载/HP	无干扰	50 Hz干扰	60 Hz干扰
0	99.90	99.85	99.75
1	100	99.95	99.90
2	99.95	99.90	99.75
3	99.95	99.65	99.70

Rolling bearing fault diagnosis based on variational mode extraction and lightweight network

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