基于轻量化神经网络Shuffle⁃SENet的高速动车组轴箱轴承故障诊断方法

doi:10.13229/j.cnki.jdxbgxb20210644

Abstract

Abstract:

Aiming at the problem of difficult to accurately diagnose axle bearing faults in high-speed train under complex operating conditions， this paper proposes a novel design method named Shuffle-SE neural network unit to address this issue. A lightweight neural network called Shuffle-SENet is developed to diagnose high-speed train axle bearing fault on this foundation. The proposed Shuffle-SENet unit is based on the ShufflNet V2 unit. It carries out local optimization of the network structure while retaining lightweight frame， and further integrates the Squeeze-and-Exception（SE） network structure. The proposed network model reduces the need for complex computations while making network operation more efficient and fault diagnosis accuracy significantly improved. In addition， the influence of the Shuffle-SE unit numbers and reduction dimension coefficient of SE unit on the performance of the proposed model is also analyzed in this paper. The experimental results that the proposed method canbe effectively used for fault diagnosis of axle box bearing of the high-speed train under various complex conditions. Compared with the lightweight network models such as MobileNet V2， Shufflenet V1/V2， ResNets， the proposed method guarantees the high efficiency of network operation and greatly improves the diagnostic accuracy of model fault diagnosis. This paper provides a new solution for deep learning technology to be applied in engineering practice and overcome the limitation of high computer hardware demand。

Key words: fault diagnosis, axle bearing, high-speed train, lightweight neural network, ShuffleNet unit

CLC Number:

TH17

Fei-yue DENG, LYUHao-yang,Xiao-hui GU,Ru-jiang HAO. Fault diagnosis of high⁃speed train axle bearing based on a lightweight neural network Shuffle⁃SENet[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 474-482.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 1

Comparison results of different unit numbers under -8 dB noise"

单元数量	模型训练时间/s	模型参数量/ $105$	FLOPs/ $105$	测试精度/%
2	1710	0.25	0.50	55.78
3	2196	0.94	1.89	62.14
4	3061	3.63	7.27	60.92
5	5019	14.26	28.52	60.64

Table 1

Table 2

Comparison results of different dimensionality reduction ratios under -8 dB noise"

r	模型训练时间/s	模型参数量/ $105$	FLOPs/ $105$	测试精度/%
4	2196	0.94	1.89	62.14
8	2165	0.73	1.45	60.97
16	2108	0.62	1.24	60.36
32	2091	0.56	1.13	60.94
64	2087	0.54	1.08	59.83

Table 2

Table 3

Comparison result of operation efficiency and complexity of different methods"

信噪比/dB	方法	模型训练时间/s	模型参数量/ $105$	FLOPs/ $105$
-8	MobileNet V2	1 943	0.70	1.41
	ShuffleNet V1	5 548	0.83	1.66
	ShuffleNet V2	2 945	0.73	1.45
	ResNets	12 416	24.02	47.99
	本文方法	2 196	0.94	1.89
-5	MobileNet V2	1 894	0.70	1.41
	ShuffleNet V1	5 399	0.83	1.66
	ShuffleNet V2	2 928	0.73	1.45
	ResNets	12 464	24.02	47.99
	本文方法	2 112	0.94	1.89
-3	MobileNet V2	1 963	0.70	1.41
	ShuffleNet V1	5 646	0.83	1.66
	ShuffleNet V2	2 912	0.73	1.45
	ResNets	12 741	24.02	47.99
	本文方法	2 134	0.94	1.89
原信号	MobileNet V2	1 899	0.70	1.41
	ShuffleNet V1	5 449	0.83	1.66
	ShuffleNet V1	2 880	0.73	1.45
	ResNets	12 462	24.02	47.99
	本文方法	2 121	0.94	1.89

Table 3

Fig.10

References 17

1	院老虎, 连冬杉, 张亮, 等. 基于密集连接卷积网络和支持向量机的飞行器机械部件故障诊断[J]. 吉林大学学报: 工学版, 2021, 51(5): 1635-1641.
	Yuan Lao-hu, Lian Dong-shan, Zhang Lian, et al. Fault diagnosis of key mechanical components of aircraft based on densenet and support vector machine[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1635-1641.
2	张根保, 李浩, 冉琰, 等. 一种用于轴承故障诊断的迁移学习模型[J]. 吉林大学学报: 工学版, 2020, 50(5): 1617-1626.
	Zhang Gen-bao, Li Hao, Ran Yan, et al. A transfer learning model for bearing fault diagnosis[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1617-1626.
3	Wang B, Lei Y G, Yan T, et al. Recurrent convolutional neural network: a new framework for remaining useful life prediction of machinery[J]. Neurocomputing, 2020, 379: 117-129.
4	Wang F, Jiang H, Shao H W, et al. An adaptive deep convolutional neural network for rolling bearing fault diagnosis[J]. Measurement Science and Technology, 2017, 28: 095005.
5	Jing L, Zhao M, Li P, et al. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox[J]. Measurement, 2017, 111: 1-10.
6	Peng D, Liu Z L, Wang H. et al. A novel deeper one-dimensional CNN with residual learning for fault diagnosis of wheelset bearings in high-speed trains[J]. IEEE Access, 2018, 7: 10278-10293.
7	Howard A G, Zhu M, Chen B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications[J]. Computer Vision and Pattern Recognition, 2017: arXiv:.
8	Sandler M, Howard A, Zhu M, et al. Mobilenetv2: inverted residuals and linear bottlenecks[C]∥IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 4510-4520.
9	Zhang X, Zhou X, Lin M, et al. Shufflenet: an extremely efficient convolutional neural network for mobile devices[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 6848-6856.
10	Ma N, Zhang X, Zheng H T, et al. Shufflenet v2: practical guidelines for efficient CNN architecture design[C]∥European Conference on Computer Vision, Springer, Cham, 2018: 122-138.
11	Krizhevsky A, Sutskever I, Hinton G I. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017: 84-90.
12	Jie H, Li S, Gang S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 42(8): 7132-7141.
13	Hoang D T, Kang H J. Rolling element bearing fault diagnosis using convolutional neural network and vibration image[J]. Cognitive Systems Research, 2019, 53: 42-50.
14	Deng F Y, Ding H, Yang S P, et al. An improved deep residual network with multiscale feature fusion for rotating machinery fault diagnosis[J]. Measurement Science and Technology, 2020, 32(2): 024002.
15	Zhao M H, Kang M, Tang B P, et al. Deep residual networks with dynamicallyweighted wavelet coefficients for faultdiagnosis of planetary gearboxes[J]. IEEE Transactions on Industrial Electronics, 2018, 65(5): 4290-4300.
16	Zhang W, Li C H, Peng G L, et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environmentand different working load[J]. Mechanical Systems & Signal Processing, 2017, 100: 439-453.
17	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Seattle, 2016: 770-778.

Related Articles 15

[1]	Jin-hua WANG,Jia-wei HU,Jie CAO,Tao HUANG. Multi⁃fault diagnosis of rolling bearing based on adaptive variational modal decomposition and integrated extreme learning machine [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 318-328.
[2]	Shao-jiang DONG,Peng ZHU,Xue-wu PEI,Yang LI,Xiao-lin HU. Fault diagnosis of rolling bearing under variable operating conditions based on subdomain adaptation [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 288-295.
[3]	Wei LUO,Bo LU,Fei CHEN,Teng MA. Fault diagnosis method of NC turret based on PSO⁃SVM and time sequence [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 392-399.
[4]	Long ZHANG,Tian-peng XU,Chao-bing WANG,Jian-yu YI,Can-zhuang ZHEN. Gearbox fault diagnosis baed on convolutional gated recurrent network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 368-376.
[5]	Xiao⁃lei CHEN,Yong⁃feng SUN,Ce LI,Dong⁃mei LIN. 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.
[6]	Liang DUAN,Chun-yuan SONG,Chao LIU,Wei WEI,Cheng-ji LYU. State recognition in bearing temperature of high-speed train based on machine learning algorithms [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 53-62.
[7]	Dan-tong OUYANG,Bi-ge ZHANG,Nai-yu TIAN,Li-ming ZHANG. Fail data reduction algorithm combining configuration checking with local search [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2144-2153.
[8]	Lao-hu YUAN,Dong-shan LIAN,Liang ZHANG,Yi LIU. Fault diagnosis of key mechanical components of aircraft based on densenet and support vector machine [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1635-1641.
[9]	Wei LI,Jian CHEN,Shan-yong TAO. Method of enhancing stochastic resonance signal of self⁃adaptive coupled periodic potential system [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1091-1096.
[10]	Dan-tong OUYANG,Yang LIU,Jie LIU. Fault diagnosis method based on test set under fault response guidance [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1017-1025.
[11]	Feng-wen PAN,Dong-liang GONG,Ying GAO,Ming-wei XU,Bin MA. Fault diagnosis of current sensor based on linearization model of lithium ion battery [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 435-441.
[12]	Chao JIA,Hong-ze XU,Long-sheng WANG. Nonlinear model predictive control for automatic train operation based on multi⁃point model [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1913-1922.
[13]	Gen-bao ZHANG,Hao LI,Yan RAN,Qiu-jin LI. A transfer learning model for bearing fault diagnosis [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1617-1626.
[14]	WANG De-jun, WEI Wei-li, BAO Ya-xin. Actuator fault diagnosis of ESC system considering crosswind interference [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1548-1555.
[15]	SONG Da-feng, LI Guang-han, ZHANG Lin, PAN Bing, ZENG Xiao-hua, PENG Yu-jun, WANG Qing-nian. Application of fuzzy mathematics in fault diagnosis of motor of hybrid vehicle [J]. 吉林大学学报(工学版), 2016, 46(2): 354-359.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Fault diagnosis of high⁃speed train axle bearing based on a lightweight neural network Shuffle⁃SENet

RICH HTML

PDF (PC)