Abstract
Traditional methods for bearing fault diagnosis mostly utilized a shallow model like support vector machine (SVM) that required professional machinery skills and much of knowledge. Deep models like deep belief network (DBN) had shown its advantage in fault feature extraction without prior knowledge. In this paper, an end-to-end approach based on deep convolution neural network (DCNN) is presented. The approach embodying the idea of end to end diagnosis has only one simple and elegant convolution neural network and don’t need any exquisite hierarchical structure that was used in the traditional methods. The samples of time-domain signals are inputted into the proposed model without any frequency transformation, and the approach can diagnosis bearing fault types and fault sizes simultaneously as output. Experimental researches had shown that the approach has the advantages such as a simple structure, less iteration and real-time, while its accuracy on the diagnosis of fault types and fault sizes can still be guaranteed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Venkatsubramanian, V., Rengaswamy, R., Yin, K., et al.: A review of process fault detection and diagnosis Part I: quantitative model-based methods. Comput. Chem. Eng. 27, 293–311 (2003)
Su, Z., Tang, B., Liu, Z., et al.: Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine. Neurocomputing 157, 208–222 (2015)
Chen, Z., Li, C., Sanchez, R.: Gearbox fault identification and classification with convolutional neural networks. Shock Vibr. 2015, 1–10 (2015)
Li, C., Sanchez, R., Zurita, G., et al.: Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 168, 119–127 (2015)
Li, P., Kong, F., He, Q., et al.: Multiscale slope feature extraction for rotating machinery fault diagnosis using wavelet analysis. Meas. J. Int. Meas. Confederation 46, 497–505 (2013)
Ye, Z., Yang, C.G., Zhang, J., et al.: Fault diagnosis of railway rolling bearing based on wavelet analysis and FCM. Int. J. Digit. Content Technol Appl. 5, 47–58 (2011)
Eslamloueyan, R.: Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee-Eastman process. Appl. Soft Comput. J. 11, 1407–1415 (2011)
Zhu, K., Song, X., Xue, D.: A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm. Measurement 47, 669–675 (2014)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Jia, F., Lei, Y., Lin, J., et al.: Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech. Syst. Signal Process. 72–73, 303–315 (2016)
Gan, M., Wang, C., Zhu, C.: Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech. Syst. Signal Process. 72–73, 92–104 (2016)
Guo, X., Chen, L., Shen, C.: Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement 93, 490–502 (2016)
Barua, A., Khorasani, K.: Hierarchical fault diagnosis and fuzzy rule-based reasoning for satellites formation flight. IEEE Trans. Aerosp. Electron. Syst. 47, 2435–2456 (2011)
Zhou, S., Lin, L., Xu, J.M.: Conditional fault diagnosis of hierarchical hypercubes. Int. J. Comput. Math. 89, 2152–2164 (2012)
Gu, Z.J., Wang, C.: A hierarchical model of network fault diagnosis. In: International Conference on Convergence Computer Technology, pp. 128–131. IEEE Computer Society, Washington (2012)
Hu, B., She, J., Yokoyama, R.: Hierarchical fault diagnosis for power systems based on equivalent-input-disturbance approach. IEEE Trans. Industr. Electron. 60, 3529–3538 (2013)
Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., et al.: Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chen, L., Zhuang, Y., Zhang, J., Wang, J. (2017). An End-to-End Approach for Bearing Fault Diagnosis Based on a Deep Convolution Neural Network. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-70096-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70095-3
Online ISBN: 978-3-319-70096-0
eBook Packages: Computer ScienceComputer Science (R0)