1. Luo, P., Yin, Z., Yuan, D., Gao, F., Liu, J.: An intelligent method for early motor bearing fault diagnosis based on Wasserstein distance generative adversarial networks meta learning. IEEE Trans. Instrum. Meas. 72, 1–11 (2023)
2. Luo, P., Yin, Z., Zhang, Y., Bai, C., Liu, J.: A novel whale optimization algorithm based on music theory knowledge for RUL prediction of motor bearing. IEEE Trans. Instrum. Meas. 72, 1–11 (2023)
3. Zhu, W., Shi, B., Feng, Z.: A transfer learning method using high-quality pseudo labels for bearing fault diagnosis. IEEE Trans. Instrum. Meas. 72, 1–11 (2023)
4. Hu, Q., Si, X., Qin, A., Lv, Y., Liu, M.: Balanced adaptation regularization based transfer learning for unsupervised cross-domain fault diagnosis. IEEE Sens J. 22(12), 12139–12151 (2022)
5. Hou, Y., et al.: Bearing fault diagnosis under small data set condition: a bayesian network method with transfer learning for parameter estimation. IEEE Access 10, 35768–35783 (2022)