Abstract
Most existing deep learning models use a single sensor signal as input data, which makes them susceptible to external variables and cannot represent the operating state of a certain component. To achieve intelligent fault diagnosis of the rolling bearing in permanent magnet synchronous motors (PMSM) under cross-working conditions, a novel method based on a multi-sensor feature fusion convolutional neural network (MFFCN) is presented. The proposed model consists of a core network and two sub-networks to achieve information sharing and exchange. The deep features in multi-sensor signals are extracted by using dilated convolution blocks in the subnetwork. An attention mechanism is introduced in the core network to flexibly extract and fuse more effective features. The proposed model is verified by a fusion of vibration and current signal for bearing fault diagnosis of the PMSM. Experimental results show that the proposed model has higher diagnostic accuracy in fault diagnosis under various working conditions. In addition, the interpretability of the network model is improved through network visualization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, F., Liu, R., Hu, Q., Chen, X.: Cascade convolutional neural network with progressive optimization for motor fault diagnosis under nonstationary conditions. IEEE Trans. Ind. Inf. 17(4), 2511–2521 (2020)
Xu, Z., Li, C., Yang, Y.: Fault diagnosis of rolling bearings using an improved multi-scale convolutional neural network with feature attention mechanism. ISA Trans. 110, 379–393 (2021)
Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531–544 (2013)
Batista, F.B., Lamim Filho, P.C.M., Pederiva, R., Silva, V.A.D.: An empirical demodulation for electrical fault detection in induction motors. IEEE Trans. Instrum. Meas. 65(3), 559–569 (2016)
Peng, Z., Peter, W.T., Chu, F.: A comparison study of improved Hilbert-Huang transform and wavelet transform: application to fault diagnosis for rolling bearing. Mech. Syst. Signal Process. 19(5), 974–988 (2005)
Wang, D.: K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: revisited. Mech. Syst. Signal Process. 70, 201–208 (2016)
Soualhi, A., Medjaher, K., Zerhouni, N.: Bearing health monitoring based on Hilbert-Huang transform, support vector machine, and regression. IEEE Trans. Instrum. Meas. 64(1), 52–62 (2014)
Palácios, R.H.C., Da Silva, I.N., Goedtel, A., Godoy, W.F.: A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electr. Power Syst. Res. 127, 249–258 (2015)
Pandarakone, S.E., Mizuno, Y., Nakamura, H.: Evaluating the progression and orientation of scratches on outer-raceway bearing using a pattern recognition method. IEEE Trans. Ind. Electron. 66(2), 1307–1314 (2018)
Hu, Q., He, Z., Zhang, Z., Zi, Y.: Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mech. Syst. Signal Process. 21(2), 688–705 (2007)
Chang, Y., Chen, J., Qu, C., Pan, T.: Intelligent fault diagnosis of wind turbines via a deep learning network using parallel convolution layers with multi-scale kernels. Renew. Energy 153, 205–213 (2020)
Kao, I.H., Wang, W.J., Lai, Y.H., Perng, J.W.: Analysis of permanent magnet synchronous motor fault diagnosis based on learning. IEEE Trans. Instrum. Meas. 68(2), 310–324 (2018)
Ye, Z., Yu, J.: AKRNet: a novel convolutional neural network with attentive kernel residual learning for feature learning of gearbox vibration signals. Neurocomputing 447, 23–37 (2021)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Liu, Y., Yan, X., Zhang, C.A., Liu, W.: An ensemble convolutional neural networks for bearing fault diagnosis using multi-sensor data. Sensors 19(23), 5300 (2019)
Acknowledgements
Natural Science Foundation of Jiangsu Higher Education Institutions (20KJB510040); Natural Science Foundation of Jiangsu Province (BK20200887); the Doctor Program of Mass Entrepreneurship and Innovation of Jiangsu Province.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhong, X., Song, X., Wang, Z. (2022). Bearing Fault Diagnosis Method Based on Multi-sensor Feature Fusion Convolutional Neural Network. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-13841-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13840-9
Online ISBN: 978-3-031-13841-6
eBook Packages: Computer ScienceComputer Science (R0)