Author:
Bai Yongliang,Xue Hai,Meng Jiadong,Chen Jiangtao
Publisher
Springer Nature Singapore
Reference10 articles.
1. Cao, H.R., Fan, F., Zhou, K., He, Z.J.: Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement 82, 439–449 (2016). (in English)
2. Bai, Y.L., Yang, J.W., Wang, J.H., Li, Q.: Intelligent diagnosis for railway wheel flat using frequency-domain Gramian angular field and transfer learning network. Ieee Access 8, 105118–105126 (2020). (in English)
3. Fu, W.L., Shao, K.X., Tan, J.W., Wang, K.: Fault diagnosis for rolling bearings based on composite multiscale fine-sorted dispersion entropy and SVM with hybrid mutation SCA-HHO algorithm optimization. Ieee Access 8, 13086–13104 (2020). (in English)
4. Fan, P.: Research on Characteristic Signal Extraction and Diagnosis Method of Bearing Fault of High-speed Train. Master's Thesis, Southwest Jiaotong University (2018)
5. Xianglong, L.: Failure analysis and preventive measures of rollers of newly manufactured 197726 bearings. Roll. Stock 04, 41–42 (2002)