A lightweight neural network with strong robustness for bearing fault diagnosis

Measurement: Journal of the International Measurement Confederation

Volumn 159, Issue , 2020, Pages

  Yao, Dechen ^a   Liu, Hengchang ^a   Yang, Jianwei ^a   Li, Xi ^b  

^a BEIJING UNIVERSITY OF CIVIL ENGINEERING AND ARCHITECTURE   (China)

^b Beijing Mass Transit Railway Co   (China)

Author keywords

Fault diagnosis; Fault severity; Lightweight neural network; Robustness; Rolling bearing

Indexed keywords

CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; FAILURE ANALYSIS; ROBUSTNESS (CONTROL SYSTEMS); ROLLER BEARINGS;

BEARING FAULT DIAGNOSIS; CONVOLUTION NEURAL NETWORK; DIAGNOSTIC EFFICIENCY; DIAGNOSTIC EQUIPMENT; FAULT SEVERITIES; INTELLIGENT BEARINGS; PERFORMANCE REQUIREMENTS; ROLLING BEARINGS;

FAULT DETECTION;

EID: 85082957418     PISSN: 02632241     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.measurement.2020.107756     Document Type: Article

References (33)

1. Yi, C., Wang, D., Fan, W., Tsui, K., Lin, J., EEMD-based steady-state indexes and their applications to condition monitoring and fault diagnosis of railway axle bearings. Sensors, 18(3), 2018, 704, 10.3390/s18030704.
2. Symonds, N., Corni, I., Wood, R., Wasenczuk, A., Vincent, D., Observing early stage rail axle bearing damage. Eng. Fail. Anal. 56 (2015), 216–232, 10.1016/j.engfailanal.2015.02.008.
3. Li, Y., Liang, X., Lin, J., Chen, Y., Liu, J., Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter. Mech. Syst. Signal Process 101 (2018), 435–448, 10.1016/j.ymssp.2017.09.007.
4. Fallahnezhad, K., Liu, S., Brinji, S., Marker, M., Meehan, P., Monitoring and modelling of false brinelling for railway bearings. Wear 424–425 (2019), 151–164, 10.1016/j.wear.2019.02.004.
5. Yan, X., Jia, M., A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing 60 (2018), 47–64, 10.1016/j.neucom.2018.05.002.
6. Ma, S., Cheng, B., Shang, Z., Liu, G., Scattering transform and LSPTSVM based fault diagnosis of rotating machinery. Mech. Syst. Signal Process 104 (2018), 155–174, 10.1016/j.ymssp.2017.10.026.
7. Chen, T., Wang, Z., Yang, X., Jiang, K., A deep capsule neural network with stochastic delta rule for bearing fault diagnosis on raw vibration signals. Measurement, 148, 2019, 106857, 10.1016/j.measurement.2019.106857.
8. Cui, L., Huang, J., Zhang, F., Quantitative and localization diagnosis of a defective ball bearing based on vertical-horizontal synchronization signal analysis. IEEE Trans. Ind. Electron. 64 (2017), 8695–8706, 10.1109/Tie.2017.2698359.
9. Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., Gao, R., Deep learning and its applications to machine health monitoring. Mech. Syst. Signal Process 115 (2019), 213–237, 10.1016/j.ymssp.2018.05.050.
10. Hu, Z., Wang, Y., Ge, M., Liu, J., Data-driven fault diagnosis method based on compressed sensing and improved multi-scale network. IEEE Trans. Ind. Electron. 67 (2019), 3216–3225, 10.1109/TIE.2019.2912763.
11. Yang, B., Lei, Y., Jia, F., Xing, S., An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mech. Syst. Signal Process 122 (2019), 692–706, 10.1016/j.ymssp.2018.12.051.
12. Qian, W., Li, S., Yi, P., Zhang, K., Transfer learning with neural networks for bearing fault diagnosis in changing working conditions. Measurement 138 (2019), 514–525, 10.1016/j.measurement.2019.02.073.
13. Xu, G., Liu, M., Jiang, Z., Shen, W., Huang, C., Online fault diagnosis method based on transfer convolutional neural networks. IEEE Trans. Instrum. Meas., 2019, 10.1109/TIM.2019.2902003.
14. Lei, J., Liu, C., Jiang, D., Fault diagnosis of wind turbine based on Long Short-term memory networks. Renew. Eng 133 (2019), 422–432, 10.1016/j.renene.2018.10.031.
15. Yu, L., Qu, J., Gao, F., Tian, Y., A novel hierarchical algorithm for bearing fault diagnosis based on stacked LSTM. Shock Vib., 2019, 10.1155/2019/2756284.
16. Zhao, R., Yan, R., Wang, J., Mao, K., Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors, 17, 2017, 273, 10.3390/s17020273.
17. Ma, S., Chu, F., Han, Q., Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions. Mech. Syst. Signal Process. 127 (2019), 190–201, 10.1016/j.ymssp.2019.02.055.
18. Zhang, W., Li, X., Ding, Q., Deep residual learning-based fault diagnosis method for rotating machinery. ISA T, 2018, 10.1016/j.isatra.2018.12.025.
19. Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks. Adv. Neural Info. Process. Syst., 2012, 1097–1105.
20. He, M., He, D., Deep learning based approach for bearing fault diagnosis. IEEE Trans. Ind. Appl. 53 (2017), 3057–3065, 10.1109/tia.2017.2661250.
21. Lu, C., Wang, Z., Zhou, B., Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification. Adv. Eng. Inf. 32 (2017), 139–151 http://refhub.elsevier.com/S0263-2241(19)30497-X/h0145.
22. Guo, S., Yang, T., Guo, W., Zhang, C., A novel fault diagnosis method for rotating machinery based on a convolutional neural network. Sensors 18 (2018), 1429–1445, 10.3390/s18051429.
23. Zhuang, Z., Lv, H., Xu, J., Huang, Z., Qin, W., A deep learning method for bearing fault diagnosis through stacked residual dilated convolutions. Appl. Sci, 9, 2019, 1823, 10.3390/app9091823.
24. Ma, S., Cai, W., Liu, W., Shang, Z., Liu, G., A lighted deep convolutional neural network based fault diagnosis of rotating machinery. Sensors, 19, 2019, 2381, 10.3390/s19102381.
25. Landola, F., Han, S., Moskewicz, M., Ashraf, K., Dally, W., Keutzer, K., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. Comput. Sci., 2016.
26. Chollet, F., Xception: deep learning with depthwise separable convolutions. Comput. Sci., 2016.
27. Howard, A., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., et al. MobileNets: efficient convolutional neural networks for mobile vision applications. Comput. Sci., 2017 https://arxiv.org/abs/1704.04861.
28. Zhang, X., Zhou, X., Lin, M., Sun, J., ShuffleNet: an extremely efficient convolutional neural network for mobile devices. Comput. Sci., 2017.
29. Liu, W., Guo, P., Ye, L., A Low-Delay Lightweight Recurrent Neural Network (LLRNN) for Rotating Machinery Fault Diagnosis. Sensors, 19, 2019, 3109, 10.3390/s19143109.
30. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L., MobileNetV2: inverted residuals and linear bottlenecks. Comput. Sci., 2018 https://arxiv.org/abs/1801.04381.
31. Lin, M., Chen, Q., Yan, S., Network in network. Comput. Sci., 2013 https://arxiv.org/abs/1312.4400.
32. Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition. Comput. Sci., 2014.
33. K.A. Loparo, Case Western University Bearing Data Center, 2012, http://csegroups.case.edu/bearingdatacenter/home.