1. B. Li, B. Tang, L. Deng et al., Self-attention ConvLSTM and its application in RUL prediction of rolling bearings. IEEE Trans. Instrum. Meas. 70, 1–11 (2021)
2. X. Ding, A. Deng et al., Rolling bearing fault diagnosis based on multi-scale and attention mechanism. J. Southeast Univ. (Nat. Sci. Ed.). 52(01), 172–178 (2022)
3. Z. Zheng, Z. Wang, Y. Zhu, S. Tang, B. Wang et al., Feature extraction method for hydraulic pump fault signal based on improved empirical wavelet transform. Processes. 7(11), 824–835 (2019)
4. J. Zhang, J. Wu, B. Hu et al., Intelligent fault diagnosis of rolling bearings using variational mode decomposition and self-organizing feature map. J. Vib. Control. 26(21–22), 1886–1897 (2020)
5. M. Deng, A. Deng et al., Intelligent fault diagnosis of wind turbine rolling bearings based on BFD and MSCNN. J. Southeast Univ. (Nat. Sci. Ed.). 51(03), 521–528 (2021)