Adaptive Lifting Network with Center Loss for Bearing Fault Diagnosis
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-6613-2_85
Reference16 articles.
1. Liu, R., Yang, B., Zio, E., et al.: Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech. Syst. Signal Proc. 108, 33–47 (2018)
2. Jin, X., Cheng, F., Peng, Y., et al.: Drivetrain gearbox fault diagnosis: vibration-and current-based approaches. IEEE Ind. Appl. Mag. 24(6), 56–66 (2018)
3. Wang, H., Wei, J., Li, P.: Research on fault diagnosis technology based on deep learning. J. Phy. Conf. Ser. 2187(1), 012041 (2022)
4. Li, Y., Yang, Y., Li, G., et al.: A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and MRMR feature selection. Mech. Syst. Signal Proc. 91, 295–312 (2017)
5. Cerrada, M., Sánchez, R.V., Li, C., et al.: A review on data-driven fault severity assessment in rolling bearings. Mech. Syst. Signal Proc. 99, 169–196 (2018)
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