REFERENCES

1. He, F.; Ye, Q. A bearing fault diagnosis method based on wavelet packet transform and convolutional neural network optimized by simulated annealing algorithm. Sensors 2022, 22, 1410.

2. Guo, L.; Han, B.; Huang, Q. Bearing fault diagnosis based on improved morlet wavelet transform and shallow residual neural network. Appl. Sci. 2024, 14, 4542.

3. Zhai, Z.; Luo, L.; Chen, Y.; Zhang, X. Rolling bearing fault diagnosis based on a synchrosqueezing wavelet transform and a transfer residual convolutional neural network. Sensors 2025, 25, 325.

4. Tan, Y.; Wu, G.; Qiu, Y.; Fan, H.; Wan, J. Fault diagnosis of a mixed-flow pump under cavitation condition based on deep learning techniques. Front. Energy. Res. 2023, 10, 1109214.

5. Gao, Y.; Piltan, F.; Kim, J. A novel image-based diagnosis method using improved DCGAN for rotating machinery. Sensors 2022, 22, 7534.

6. Deng, C.; Deng, Z.; Lu, S.; He, M.; Miao, J.; Peng, Y. Fault diagnosis method for imbalanced data based on multi-signal fusion and improved deep convolution generative adversarial network. Sensors 2023, 23, 2542.

7. Lai, S.; Cheung, T.; Zhao, J.; Xue, K.; Fung, K.; Lam, K. Residual attention single-head vision transformer network for rolling bearing fault diagnosis in noisy environments. In Proceedings of the 2024 6th International Conference on Video, Signal and Image Processing; Ningbo Hainan, China. New York, NY, USA: ACM; 2024. pp. 136-50.

8. Liu, D.; Cui, L.; Wang, G.; Cheng, W. Interpretable domain adaptation transformer: a transfer learning method for fault diagnosis of rotating machinery. Struct. Health. Monit. 2024, 24, 1187-200.

9. Hassannejad, R.; Ettefagh, M. M.; Bahrami Mossayebi, Y. Adaptive wavelet-based physics-informed CNN for bearing fault diagnosis. Int. J. Progn. Health. Manag. 2025, 16, 4234.

10. Deng, R.; Chen, D.; Yao, C.; Shao, M.; Hu, D. A multi-scale sensor importance-aware attention fusion network and its applications in fault diagnosis of centrifugal pumps and axial piston pumps. Measurement 2026, 258, 119315.

11. Kim, A. R.; Seon, Kim. H.; Young, Kim. S. Transformer-based fault detection using pressure signals for hydraulic pumps. IEEE. Access. 2024, 12, 145795-808.

12. Zhao, L.; He, Y.; Zheng, H.; Dai, D. A novel multistep wavelet convolutional transfer diagnostic framework for cross-machine bearing fault diagnosis. Sensors 2025, 25, 3141.

13. Yu, S.; Song, L.; Pang, S.; Wang, M.; He, X.; Xie, P. M-net: a novel unsupervised domain adaptation framework based on multi-kernel maximum mean discrepancy for fault diagnosis of rotating machinery. Complex. Intell. Syst. 2024, 10, 3259-72.

14. Sun, K.; Xu, X.; Lu, N.; Xia, H.; Han, M. Joint discriminative adversarial domain adaptation for cross-domain fault diagnosis. IEEE. Trans. Instrum. Meas. 2023, 72, 1-11.

15. Li, G.; Wu, J.; Deng, C.; Wei, M.; Xu, X. Self-supervised learning for intelligent fault diagnosis of rotating machinery with limited labeled data. Appl. Acoust. 2022, 191, 108663.

16. Zhu, P.; Ma, S.; Han, Q.; Chu, F. Deep contrastive transfer learning for rotating machinery fault diagnosis. IEEE. Trans. Instrum. Meas. 2025, 74, 1-10.

17. Zhou, F.; Zhang, Z.; Li, S. Research on federated learning method for fault diagnosis in multiple working conditions. Complex. Eng. Syst. 2021, 1, 7.

18. Shao, H.; Xia, M.; Wan, J.; De Silva, C. W. Modified stacked autoencoder using adaptive morlet wavelet for intelligent fault diagnosis of rotating machinery. IEEE/ASME. Trans. Mechatron. 2022, 27, 24-33.

19. Wang, Y.; Liu, Y.; Chow, T. W. S.; Gu, J.; Zhang, M. A balanced adversarial domain adaptation method for partial transfer intelligent fault diagnosis. IEEE. Trans. Instrum. Meas. 2022, 71, 1-11.

20. Zhang, Y.; Liu, Z.; Huang, Q. A contrastive learning-based fault diagnosis method for rotating machinery with limited and imbalanced labels. IEEE. Sensors. J. 2023, 23, 16402-12.

21. Zhang, Y.; Ren, Z.; Zhou, S.; Feng, K.; Yu, K.; Liu, Z. Supervised contrastive learning-based domain adaptation network for intelligent unsupervised fault diagnosis of rolling bearing. IEEE/ASME. Trans. Mechatron. 2022, 27, 5371-80.

22. Zhu, J.; Chen, N.; Shen, C. A new multiple source domain adaptation fault diagnosis method between different rotating machines. IEEE. Trans. Ind. Inf. 2021, 17, 4788-97.

23. Yang, L.; Chen, Y.; Ma, X.; Qiu, Q.; Peng, R. A prognosis-centered intelligent maintenance optimization framework under uncertain failure threshold. IEEE. Trans. Rel. 2024, 73, 115-30.

24. Tan, L.; Wei, F.; Ma, X.; Peng, R.; Xiao, H.; Yang, L. Systemic condition-based maintenance optimization under inspection uncertainties: a customized multiagent reinforcement learning approach. IEEE. Trans. Rel. 2025, 74, 5848-62.

25. Yang, L.; Zhou, S.; Ma, X.; Chen, Y.; Jia, H.; Dai, W. Group machinery intelligent maintenance: Adaptive health prediction and global dynamic maintenance decision-making. Reliab. Eng. Syst. Saf. 2024, 252, 110426.

26. Wang, Q.; Wu, B.; Zhu, P.; Li, P.; Zuo, W.; Hu, Q. ECA-net: efficient channel attention for deep convolutional neural networks. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2020 Jun 13-19; Seattle, WA, USA. IEEE; 2020. pp. 11531-9.

27. Aburakhia, S. A.; Myers, R.; Shami, A. A hybrid method for condition monitoring and fault diagnosis of rolling bearings with low system delay. IEEE. Trans. Instrum. Meas. 2022, 71, 1-13.

28. Dubaish, A. A.; Jaber, A. A. Comparative analysis of SVM and ANN for machine condition monitoring and fault diagnosis in gearboxes. Math. Model. Eng. Probl. 2024, 11, 976-86.

29. Cui, Y.; Jia, M.; Lin, T.; Song, Y.; Belongie, S. Class-balanced loss based on effective number of samples. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2019 Jun 15-20; Long Beach, CA, USA. IEEE; 2019. pp. 9260-9.

30. Joloudari, J. H.; Marefat, A.; Nematollahi, M. A.; Oyelere, S. S.; Hussain, S. Effective class-imbalance learning based on SMOTE and convolutional neural networks. Appl. Sci. 2023, 13, 4006.

31. Pan, C.; Shang, Z.; Tang, L.; Cheng, H.; Li, W. Open-set domain adaptive fault diagnosis based on supervised contrastive learning and a complementary weighted dual adversarial network. Mech. Syst. Signal. Process. 2025, 222, 111780.

32. Xu, Z.; Lee, C. K. M.; Wong, C. A novel fault diagnosis method based on deep stable learning for bearings with imbalanced data samples. Expert. Syst. Appl. 2025, 281, 127634.

33. Liu, Z.; Zhang, J.; He, X.; Zhang, Q.; Sun, G.; Zhou, D. Fault diagnosis of rotating machinery with limited expert interaction: a multicriteria active learning approach based on broad learning system. IEEE. Trans. Contr. Syst. Technol. 2023, 31, 953-60.

Complex Engineering Systems
ISSN 2770-6249 (Online)

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/