Abstract
Feature extraction is of great significance in the traditional bearing fault diagnosis methods which detect incipient defects by identifying peaks at characteristic defect frequencies in the envelope power spectrum or by developing discriminative models for features extracted from the signals through time and frequency domain analysis. This issue not only requires expert knowledge to design discriminative features but also requires appropriate feature selection algorithms to eliminate irrelevant and redundant features that degrade the performance of the methods. In this paper, we present a convolutional neural networks (CNNs) based method to automatically derive optimal features which not require any feature extraction or feature selection. The proposed method identifies bearing defects efficiently by processing feature extraction and classification into a single learning machine and automatically extracts optimal features from these energy distribution maps to diagnose various single and compound bearing defects and yields better diagnostic performance compared to state-of-the-art AE-based methods.
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
Thorsen, O.V., Dalva, M.: Failure identification and analysis for high-voltage induction motors in the petrochemical industry. IEEE Trans. Ind. Appl. 35(4), 810–818 (1999)
Jin, X., Zhao, M., Chow, T.W.S., Pecht, M.: Motor bearing fault diagnosis using trace ratio linear discriminant analysis. IEEE Trans. Ind. Electron. 61(5), 2441–2451 (2014)
Seshadrinath, J., Singh, B., Panigrahi, B.K.: Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets. IEEE Trans. Power Electron. 29(2), 936–945 (2014)
Lau, E.C.C., Ngan, H.W.: Detection of motor bearing outer raceway defect by wavelet packet transformed motor current signature analysis. IEEE Trans. Instrum. Meas. 59(10), 2683–2690 (2010)
Frosini, L., Bassi, E.: Stator current and motor efficiency as indicators for different types of bearing faults in induction motors. IEEE Trans. Ind. Electron. 57(1), 244–251 (2010)
Kang, M., Islam, R., Kim, J., Kim, J.-M., Pecht, M.: A hybrid feature selection scheme for reducing diagnostic performance deterioration caused by outliers in data-driven diagnostics. IEEE Trans. Ind. Electron. 63(5), 3299–3310 (2016)
Kang, M., Kim, J., Wills, L., Kim, J.-M.: Time-varying and multi-resolution envelope analysis and discriminative feature analysis for bearing fault diagnosis. IEEE Trans. Ind. Electron. 62(12), 7749–7761 (2015)
Nguyen, P., Kang, M., Kim, J.M., Ahn, B.H., Ha, J.M., Choi, B.K.: Robust condition monitoring of rolling element bearings using de-noising and envelope analysis with signal decomposition techniques. Expert Syst. Appl. 42(22), 9024–9032 (2015)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324 (1998)
Bouvrie, J.: Notes on convolutional neural networks (2006)
LeCun, Y., Bottou, L., Orr, G., Muller, K.: Efficient backprop. Neural Networks: Tricks of the Trade, vol. 1524, pp. 9–50 (1998)
Abu-Mahfouz, I.A.: A comparative study of three artificial neural networks for the detection and classification of gear faults. Int. J. Gen. Syst. 34(3), 261–277 (2005)
LeCun, Y., Cortes, C., Burges, C.J.: MNIST handwritten digit database. AT&T Labs (2010)
Haykin, S.: Neural Networks and Learning Machines. 3rd edn, pp. 122–229. Prentice Hall, New York (2009)
Becker, S., Le Cun, Y.: Improving the convergence of back-propagation learning with second order methods. In: Proceedings of the Connectionist Models Summer School, pp. 29–37 (1988)
LeCun, Y.: Generalization and network design strategies. In: Pfeifer, R., Schreter, Z., Fogelman, F., Steels, L. (eds.) Connectionism in Perspective Zurich. Elsevier, Switzerland (1989)
Tra, V., Kim, J., Khan, S.A., Kim, J.-M.: Incipient fault diagnosis in bearings under variable speed conditions using multiresolution analysis and a weighted committee machine. J. Acoust. Soc. Am. 142(1), EL35–EL41 (2017)
Acknowledgements
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (Nos. 20162220100050, 20161120100350, 20172510102130). It was also funded in part by The Leading Human Resource Training Program of Regional Neo industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2016H1D5A1910564), and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A3B03931927).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that there is no conflict of interest regarding the publication of this manuscript.
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tra, V., Kim, J., Kim, JM. (2019). Fault Diagnosis of Bearings with Variable Rotational Speeds Using Convolutional Neural Networks. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 759. Springer, Singapore. https://doi.org/10.1007/978-981-13-0341-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-13-0341-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0340-1
Online ISBN: 978-981-13-0341-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)