REFERENCES
1. Xu X, Cao D, Zhou Y, Gao J. Application of neural network algorithm in fault diagnosis of mechanical intelligence. Mech Syst Signal Pr 2020;141:106625.
2. Qiao W, Lu D. A survey on wind turbine condition monitoring and fault diagnosis—part Ⅰ: components and subsystems. IEEE Trans Ind Electron 2015;62:6536-45.
3. Hoang DT, Kang HJ. A survey on Deep Learning based bearing fault diagnosis. Neurocomputing 2019;335:327-35.
4. Lei Y, Yang B, Jiang X, et al. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Pr 2020;138:106587.
5. Tran MQ, Amer M, Dababat A, Abdelaziz AY, Dai HJ, et al. Robust fault recognition and correction scheme for induction motors using an effective IoT with deep learning approach. Measurement 2023;207:112398.
6. Gong W, Chen H, Zhang Z, et al. A novel Deep Learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion. Sensors 2019;19:1693.
7. Pandey SK, Janghel RR. Recent Deep Learning techniques, challenges and its applications for medical healthcare system: a review. Neural Process Lett 2019;50:1907-35.
8. Liu R, Yang B, Zio E, Chen X. Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Signal Pr 2018;108:33-47.
9. Zhuang F, Qi Z, Duan K, et al. A comprehensive survey on transfer learning. Proceedings of the IEEE 2021;109:43-76.
10. Qian C, Zhu J, Shen Y, Jiang Q, Zhang Q. Deep transfer learning in mechanical intelligent fault diagnosis: application and challenge. Neural Process Lett 2022;54:2509-31.
11. Yang X, Chi F, Shao S, Zhang Q. Bearing fault diagnosis under variable working conditions based on deep residual shrinkage networks and transfer learning. J Sensors 2021;2021:1-13.
12. Kouw WM, Loog M. A review of domain adaptation without target labels. IEEE Trans Pattern Anal Mach Intell 2021;43:766-85.
13. Hershey JR, Olsen PA. Approximating the kullback leibler divergence between gaussian mixture models. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07. IEEE; 2007.
14. Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv: 14123474 2014.
15. Shen J, Qu Y, Zhang W, Yu Y. Wasserstein Distance Guided Representation Learning for Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence 2018; doi: 10.1609/aaai.v32i1.11784.
16. Sun B, Saenko K. Deep CORAL: correlation alignment for deep domain adaptation. In: Lecture Notes in Computer Science. Springer International Publishing; 2016. pp. 443–50.
17. Qian C, Jiang Q, Shen Y, Huo C, Zhang Q. An intelligent fault diagnosis method for rolling bearings based on feature transfer with improved DenseNet and joint distribution adaptation. Meas Sci Technol 2021;33:025101.
18. Li X, Zhang W, Ding Q, Sun JQ. Multi-Layer domain adaptation method for rolling bearing fault diagnosis. Signal Process 2019;157:180-97.
19. Wang Y, Ning D, Lu J. A novel transfer capsule network based on domain-adversarial training for fault diagnosis. Neural Process Lett 2022;54:4171-88.
20. Li W, Huang R, Li J, et al. A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges. Mech Syst Signal Process 2022;167:108487.
21. Yao S, Kang Q, Zhou M, Rawa MJ, Abusorrah A. A survey of transfer learning for machinery diagnostics and prognostics. Artificia Intell Rev 2022;56:2871-922.
22. Long M, CAO Z, Wang J, Jordan MI. Conditional adversarial domain adaptation. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, et al., editors. Advances in Neural Information Processing Systems. vol. 31. Curran Associates, Inc.; 2018. Available from:
23. Borgwardt KM, Gretton A, Rasch MJ, et al. Integrating structured biological data by Kernel Maximum Mean Discrepancy. Bioinformatics 2006;22:e49-57.
24. Long M, Zhu H, Wang J, Jordan MI. Deep transfer learning with joint adaptation networks. In: International conference on machine learning. PMLR; 2017. pp. 2208–17.
25. “Case Western Reserve University Bearing Data Center Website”; .
25. Case WesLi K, Ping X, Wang H, Chen P, Cao Y. Sequential fuzzy diagnosis method for motor roller bearing in variable operating conditions based on vibration analysis. Sensors 2013;13:8013-41.