Abstract
The traditional rotor bearing fault diagnosis and analysis method is difficult to get the prior knowledge and experience, resulting in the low accuracy of fault diagnosis. In this paper, a method of fault feature extraction and diagnosis of rotor bearing based on convolution neural network is proposed. This method uses the chaotic characteristic of the vibration signal of the rotor bearing, uses the phase space reconstruction method to obtain the embedding dimension as the scale of the convolution neural network input composition, avoid the limitation of traditional frequency analysis method in the process of decomposition and transformation, the fault information can be extracted more comprehensively. In order to make full use of the advantages of the convolution neural network in the field of two-dimensional image analysis and improve the accuracy of the fault diagnosis model, a method of learning input form neural network based on convolution neural network for grayscale graph is proposed. The results of the simulation show the effectiveness of the method.
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
Similar content being viewed by others
References
Tang, G.J., Pang, B., Liu, S.K.: Fault diagnosis of rolling bearings based on difference spectrum of singular value and stationary subspace analysis. J. Vib. Shock 34(11), 83–87 (2015)
Zhao, D.Z., Li, J.Y., Cheng, W.D., et al.: Rolling element bearing fault diagnosis based on generalized demodulation algorithm under variable rotational speed. J. Vib. Eng. 2017(5), 865–873 (2017)
Zhang, X.N., Zeng, Q.S., Wan, H.: Bearing fault diagnosis based on improved wavelet denoising and EMD method. Meas. Control Technol. 33(1), 23–26 (2014)
Qin, Y.: A new family of model-based impulsive wavelets and their sparse representation for rolling bearing fault diagnosis. IEEE Trans. Ind. Electron. 65(3), 2716–2726 (2017)
Shao, H., Jiang, H., Li, X., et al.: Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowl.-Based Syst. (2017)
Yuan, J.H., Han, T., Tang, J., et al.: An approach to intelligent fault diagnosis of rolling bearing using wavelet time-frequency representations and CNN. Mach. Des. Res. 2017(2), 93–97 (2017)
He, M., He, D.: Deep learning based approach for bearing fault diagnosis. IEEE Trans. Ind. Appl. 53(3), 3057–3065 (2017)
Oh, H., Jung, J.H., Jeon, B.C., et al.: Scalable and unsupervised feature engineering using vibration-imaging and deep learning for rotor system diagnosis. IEEE Trans. Ind. Electron. 65(4), 3539–3549 (2018)
Zhang, W., Peng, G., Li, C., et al.: A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 17(2), 425 (2017)
Kim, H.S., Eykholt, R., Salas, J.D.: Nonlinear dynamics, delay times, and embedding windows. Phys. D-Nonlinear Phenom. 127(1–2), 48–60 (1999)
Liu, Y.B., He, B., Liu, F., et al.: Comprehensive recognition of rolling bearing fault pattern and fault degrees based on two-layer similarity in phase space. J. Vib. Shock 36(4), 178–184 (2017)
He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
Chang, L., Deng, X.M., Zhou, M.Q., et al.: Convolutional neural networks in image understanding. Acta Autom. Sin. 42(9), 1300–1312 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: 25th International Conference on Neural Information Processing Systems. Curran Associates Inc., pp. 1097–1105. ACM, Lake Tahoe, Nevada (2012)
Lecun, Y., Boser, B., Denker, J.S., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Case western reserve university bearings vibration dataset. http://csegroups.case.edu/bearingdatacenter/home
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, H., Yang, D., Pang, Y., Li, T., Hu, B. (2018). An Approach for Feature Extraction and Diagnosis of Motor Rotor Bearing Based on Convolution Neural Network. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-04167-0_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04166-3
Online ISBN: 978-3-030-04167-0
eBook Packages: Computer ScienceComputer Science (R0)