Abstract
In a manufacturing industry, there is a lot of rotating machinery used and rolling bearing is an essential part of rotating machinery. Fault occurring probability in rolling bearing is very high, so this is necessary to predict fault to reduce various losses. Fault diagnosis methods using the concept of deep learning (DL) require less human expertise than traditional fault diagnosis, and it also has automatic feature extracting capability. If we want to train our DL model from scratch, it requires numerous fault signals and long training time for better results. But in practical condition, available fault data is small. Therefore, the limitation DL for small dataset has been solved using pre-trained DL models. The pre-trained AlexNet and analytic Morlet wavelet function based scalogram have been used in the proposed algorithm. The performance of the algorithm has been tested on the different size dataset. Also, its performance has been compared with other existing 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
References
Li H, Lian X, Guo C, Zhao P (2015) Investigation on early fault classification for rolling element bearing based on the optimal frequency band determination. J Intel Manuf 261(1):189–98
Hu Q, Si X-S, Qin A-S, Lv Y-R, Zhang Q-H (2020) Machinery fault diagnosis scheme using redefined dimensionless indicators and mRMR feature selection. IEEE Access 8:40313–40326
Qin X, Li Q, Dong X, Lv S (2017) The fault diagnosis of rolling bearing based on ensemble empirical mode decomposition and random forest. Shock Vib 2017:1–9
Chen X, Zhang B, Gao D (2021) Bearing fault diagnosis based on multi-scale CNN and LSTM model. J Intell Manuf 32:971–987
Hoang D-T, Kang H-J (2019) Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cognit Syst Res 53:42–50
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359
Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
Case Western Reserve University Bearing Data Center [Online]. Available: http://csegroups.case.edu/bearingdatacenter/pages/download-data-file. Accessed 20 Jul 2012
Zang Y (2015) A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement 69:164–179
Li Y, Wang X, Wu J (2016) Fault diagnosis of rolling bearing based on permutation entropy and extream learning machine. In: Proceedings of Chinese control and decision conference, pp 2966–2971
Haidong S, Hongkai J, Xingqiu L, Shuaipeng W (2018) Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowl-Based Syst 140:1–14
Wang S, Xiang J, Zhong Y, Zhou Y (2018) Convolutional neural network-based hidden Markov models for rolling element bearing fault identification. Knowl-Based Syst 144:65–76
Yuan L, Lian D, Kang X, Chen Y, Zhai K (2020) Rolling bearing fault diagnosis based on convolutional neural network and support vector machine. IEEE Access 8:137395–137406
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, P., Amhia, H., Sharma, S.D. (2022). Transfer Learning-Based Model for Rolling Bearing Fault Classification Using CWT-Based Scalograms. In: Pandian, A.P., Palanisamy, R., Narayanan, M., Senjyu, T. (eds) Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-7330-6_43
Download citation
DOI: https://doi.org/10.1007/978-981-16-7330-6_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7329-0
Online ISBN: 978-981-16-7330-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)