Abstract
Bearings are vital part of rotary machines. A failure of bearing has a negative impact on schedules, production operation and even human casualties. Therefore, in prior achieving fault detection and diagnosis (FDD) of bearing is ensuring the safety and reliable operation of rotating machinery systems. However, there are some challenges of the industrial FDD problems. Since according to a literature review, more than half of the broken machines are caused by bearing fault. Therefore, one of the important thing is time delay should be reduced for FDD. However, due to many learnable parameters in model and data of long sequence, both lead to time delay for FDD. Therefore, this paper proposes a deep Light Convolutional Neural Network (LCNN) using one dimensional convolution neural network for FDD.
Supported by organization x.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nandi, S., Toliyat, H.A., Li, X.: Condition monitoring and fault diagnosis of electrical motors–a review. IEEE Trans. Energy Convers. 20(4), 719–729 (2005)
Liu, X., Choo, K.K.R., Deng, R.H., Lu, R., Weng, J.: Efficient and privacy-preserving outsourced calculation of rational numbers. IEEE Trans. Dependable Secure Comput. 15(1), 27–39 (2016)
Qiao, W., Lu, D.: A survey on wind turbine condition monitoring and fault diagnosis-Part I: components and subsystems. IEEE Trans. Ind. Electron. 62(10), 6536–6545 (2015). https://doi.org/10.1109/TIE.2015.2422112
Kim, J., Caire, G., Molisch, A.F.: Quality-aware streaming and scheduling for device-to-device video delivery. IEEE/ACM Trans. Netw. 24(4), 2319–2331 (2015)
Ranjan, C., Reddy, M., Mustonen, M., Paynabar, K., Pourak, K.: Dataset: rare event classification in multivariate time series. arXiv preprint arXiv:1809.10717 (2018)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: Efficient object localization using convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 648–656 (2015)
Gao, L., Guo, Z., Zhang, H., Xu, X., Shen, H.T.: Video captioning with attention-based LSTM and semantic consistency. IEEE Trans. Multimedia 19(9), 2045–2055 (2017)
Takahashi, N., et al.: Deep convolutional neural networks and data augmentation for acoustic event detection. arXiv preprint arXiv:1604.07160 (2016)
Zhang, H., et al.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)
Zhao, R., Yan, R., Wang, J., Mao, K.: Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors 17(2), 273 (2017)
Zhang, W., Peng, G., Li, C.: Bearings fault diagnosis based on convolutional neural networks with 2-D representation of vibration signals as input. In MATEC web of conferences, vol. 95, p. 13001. EDP Sciences (2017)
Basha, S.H.S., et al.: Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing 378, 112–119 (2020)
Zilong, Z., Wei, Q.: Intelligent fault diagnosis of rolling bearing using one-dimensional multi-scale deep convolutional neural network based health state classification. In: 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). IEEE (2018)
Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03933828). This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2018-0-01417) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Oh, J.W., Jeong, J. (2020). Bearing Fault Detection with a Deep Light Weight CNN. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12250. Springer, Cham. https://doi.org/10.1007/978-3-030-58802-1_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-58802-1_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58801-4
Online ISBN: 978-3-030-58802-1
eBook Packages: Computer ScienceComputer Science (R0)