A new prospective topology-guided prototype network for exemplar-free continual fault diagnosis of rolling bearing
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OpenAlex topics: Machine Fault Diagnosis Techniques · Anomaly Detection Techniques and Applications · Structural Health Monitoring Techniques
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References (31)
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1. Zhou P, Yu X, Yang Y, et al. Fault-induced gear meshing modulation sideband extraction and evaluation for fault diagnosis of planetary gearboxes. Sci China Tech Sci, 2025, 68: 112030510.1007/s11431-024-2802-y ↗
2. Xiao Y, Shao H, Yan S, et al. Domain generalization for rotating machinery fault diagnosis: A survey. Adv Eng Inf, 2025, 64: 10306310.1016/j.aei.2024.103063 ↗
3. Xia P C, Huang Y X, Wang Y X, et al. Augmentation-based discriminative meta-learning for cross-machine few-shot fault diagnosis. Sci China Tech Sci, 2023, 66: 1698–171610.1007/s11431-022-2380-0 ↗
4. Wang J, Jin J, Zhang L, et al. Towards spatial-temporal meta-hypergraph learning for multimodal few-shot fault diagnosis. J Ind Inf Integr, 2025, 48: 100924
5. Zhong T, Qin C J, Shi G, et al. A residual denoising and multiscale attention-based weighted domain adaptation network for tunnel boring machine main bearing fault diagnosis. Sci China Tech Sci, 2024, 67: 2594–261810.1007/s11431-024-2734-x ↗
6. Chen Y, Zhang D, Yan R. A trend domain adaptation approach with dynamic decision for fault diagnosis of rotating machinery equipment. IEEE Trans Ind Inf, 2024, 21: 2084–209310.1109/TII.2024.3488780 ↗
7. Li Q, Li H, Hu W, et al. Transparent operator network: A fully interpretable network incorporating learnable wavelet operator for intelligent fault diagnosis. IEEE Trans Ind Inf, 2024, 20: 8628–863810.1109/TII.2024.3366993 ↗
8. Xiao X, Li C, He H, et al. Rotating machinery fault diagnosis method based on multi-level fusion framework of multi-sensor information. Inf Fusion, 2025, 113: 10262110.1016/j.inffus.2024.102621 ↗
9. Liu Y, Chen Y, Li X, et al. MPNet: A lightweight fault diagnosis network for rotating machinery. Measurement, 2025, 239: 11549810.1016/j.measurement.2024.115498 ↗
10. Guo P, Huang W, Jia N, et al. A novel adaptive gating neurons model with physical features weighted for bearing fault diagnosis under strong noise. Eng Appl Artif Intell, 2025, 149: 11053210.1016/j.engappai.2025.110532 ↗
11. Wang X, Jiang H, Mu M, et al. A trackable multi-domain collaborative generative adversarial network for rotating machinery fault diagnosis. Mech Syst Signal Process, 2025, 224: 11195010.1016/j.ymssp.2024.111950 ↗
12. Zhang X, Huang W, Wang J, et al. Adaptive variational sampling-embedded domain generalization network for fault diagnosis with intra-inter-domain class imbalance. Reliab Eng Syst Saf, 2025, 256: 11070710.1016/j.ress.2024.110707 ↗
13. Qian Q, Luo J, Qin Y. Adaptive intermediate class-wise distribution alignment: A universal domain adaptation and generalization method for machine fault diagnosis. IEEE Trans Neural Netw Learn Syst, 2024, 36: 4296–431010.1109/TNNLS.2024.3376449 ↗
14. Sun S, Huang H, Peng T, et al. A data privacy protection diagnosis framework for multiple machines vibration signals based on a swarm learning algorithm. IEEE Trans Instrum Meas, 2023, 72: 1–9
15. Jin Y R, Qin C J, Zhang Z N, et al. A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions. Sci China Tech Sci, 2022, 65: 2551–256310.1007/s11431-022-2109-4 ↗
16. Shen C, He Z, Chen B, et al. Dynamic branch layer fusion: A new continual learning method for rotating machinery fault diagnosis. Knowl Based Syst, 2025, 313: 11317710.1016/j.knosys.2025.113177 ↗
17. He Z, Shen C, Chen B, et al. A new feature boosting based continual learning method for bearing fault diagnosis with incremental fault types. Adv Eng Inf, 2024, 61: 10246910.1016/j.aei.2024.102469 ↗
18. Hu K, He Q, Cheng C, et al. Adaptive incremental diagnosis model for intelligent fault diagnosis with dynamic weight correction. Reliab Eng Syst Saf, 2024, 241: 10970510.1016/j.ress.2023.109705 ↗
19. Li J, Yue K, Chen Z, et al. An uncertainty-aware continual learning framework for fault diagnosis of rotating machinery with homogeneous-heterogeneous faults. IEEE Trans Automat Sci Eng, 2026, 23: 3284–329810.1109/TASE.2024.3519608 ↗
20. Zhang Y, Shen C, Zhong X, et al. Adaptive feature consolidation residual network for exemplar-free continuous diagnosis of rotating machinery with fault-type increments. Adv Eng Inf, 2024, 62: 10271510.1016/j.aei.2024.102715 ↗
21. Chen B, Zhang X, Shen C, et al. CoUDA: Continual unsupervised domain adaptation for industrial fault diagnosis under dynamic working conditions. IEEE Trans Ind Inf, 2025, 21: 4072–408210.1109/TII.2025.3538135 ↗
22. Wang L, Zhang X, Su H, et al. A comprehensive survey of continual learning: Theory, method and application. IEEE Trans Pattern Anal Mach Intell, 2024, 46: 5362–538310.1109/TPAMI.2024.3367329 ↗
23. Rolnick D, Ahuja A, Schwarz J, et al. Experience replay for continual learning. Adv Neural Inf Process Syst, 2019, 32: 350–360
24. Liu Y, Chen B, Wang D, et al. A lifelong learning method based on generative feature replay for bearing diagnosis with incremental fault types. IEEE Trans Instrum Meas, 2023, 72: 1–11
25. Yan S, Shao H, Wang X, et al. Few-shot class-incremental learning for system-level fault diagnosis of wind turbine. IEEE ASME Trans Mechatron, 2024, 30: 4493–450210.1109/TMECH.2024.3490733 ↗
26. Wan X, Cen L, Chen X, et al. Convertible shapelet learning with incremental learning capability for industrial fault diagnosis under shape shift samples. IEEE Trans Ind Inf, 2025, 21: 3356–336510.1109/TII.2024.3523595 ↗
27. Wang R, Huang W, Zhang X, et al. Federated contrastive prototype learning: An efficient collaborative fault diagnosis method with data privacy. Knowl Based Syst, 2023, 281: 11109310.1016/j.knosys.2023.111093 ↗
28. Kirkpatrick J, Pascanu R, Rabinowitz N, et al. Overcoming catastrophic forgetting in neural networks. Proc Natl Acad Sci USA, 2017, 114: 3521–352610.1073/pnas.1611835114 ↗
29. Li Z, Hoiem D. Learning without forgetting. IEEE Trans Pattern Anal Mach Intell, 2017, 40: 2935–294710.1109/TPAMI.2017.2773081 ↗
30. Gomez-Villa A, Goswami D, Wang K, et al. Exemplar-free continual representation learning via learnable drift compensation. In: Leonardis A, Ricci E, Roth S, et al., eds. ECCV 2024. Lecture Notes in Computer Science. Cham: Springer, 2024. 473–490
31. Goswami D, Soutif-Cormerais A, Liu Y, et al. Resurrecting old classes with new data for exemplar-free continual learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2024. 28525–28534