Fault diagnosis methods based on domain generalization (DG) have gained significant attention for their ability to improve model generalization and perform fault diagnosis in unknown target domains. However, in practical applications, unlabeled data is more common, making it crucial to enhance model generalization using unlabeled data. In this paper, we propose a credible granular feature contrastive learning network (CGFCLN) for semi-supervised DG (SSDG) in fault diagnosis of rotating machinery, which further enhances the practicality of DG methods. CGFCLN leverages both deep features and credible labels to fully utilize unlabeled data, thereby improving model performance. First, CGFCLN contrasts deep features from different source domains, enabling the model to extract domain-invariant features from granular features. Additionally, CGFCLN contrasts deep features and final outputs of both original and augmented data, which helps improve the robustness of the model. Second, we introduce credible pseudo-labels, which minimize the negative impact of incorrect pseudo-labels on the model and enhance classification and generalization performance. To validate the effectiveness of CGFCLN, extensive experiments were conducted on two rotating machinery datasets, achieving average accuracies of 93.85% and 92.70%, respectively. Compared to existing SSDG methods, CGFCLN demonstrates superior diagnostic and generalization performance.
ZhuZLeiYQiG, et al. A review of the application of deep learning in intelligent fault diagnosis of rotating machinery. Measurement2023; 206: 112346.
2.
QianQQinYLuoJ, et al. Deep discriminative transfer learning network for cross-machine fault diagnosis. Mech Syst Signal Process2023; 186: 109884.
3.
PanZPanXJiangF, et al. Remaining useful life prediction of rolling-element bearings based on cumulative transformation and EResNet-KSLSTM network. Mech Syst Signal Process2025; 238: 113251.
4.
ShiYDengADengM, et al. Domain augmentation generalization network for real-time fault diagnosis under unseen working conditions. Reliab Eng Syst Saf2023; 235: 109188.
5.
DingCHuangWShenC, et al. Synchroextracting frequency synchronous chirplet transform for fault diagnosis of rotating machinery under varying speed conditions. Struct Health Monit2024; 23(3): 1403–1422.
6.
ZhaoJHeDJinZ, et al. A new method for bearing remaining useful life prediction based on dynamic wavelet and physical information constraints. Expert Syst Appl2025; 296: 129023.
7.
HeDZhaoJJinZ, et al. DCAGGCN: a novel method for remaining useful life prediction of bearings. Reliab Eng Syst Saf2025; 260: 110978.
8.
ZhangYJiJ CRenZ, et al. Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing. Reliab Eng Syst Saf2023; 234: 109186.
9.
LiuYWangYChowT W S, et al. Deep adversarial subdomain adaptation network for intelligent fault diagnosis. IEEE Trans Ind Inform2022; 18(9): 6038-6046.
10.
WangJJiSHanB, et al. Deep adaptive adversarial network-based method for mechanical fault diagnosis under different working conditions. Complexity2020; 2020(1): 6946702.
11.
LiaoWYangKFuW, et al. A review: the application of generative adversarial network for mechanical fault diagnosis. Meas Sci Technol2024; 35: 062002.
12.
ZhaoCShenW.Dual adversarial network for cross-domain open set fault diagnosis. Reliab Eng Syst Saf2022; 221: 108358.
13.
ChenXChenZGuoL, et al. Pseudo-label assisted semi-supervised adversarial enhancement learning for fault diagnosis of gearbox degradation with limited data. Mech Syst Signal Process2025; 224: 112108.
14.
ZhengJWangJYeL, et al. Semi-supervised convolutional generative adversarial networks for dynamic fault classification with manifold regularization. Process Safety and Environmental Protection2024; 193: 550–557.
15.
ZhangYYuKLeiZ, et al. Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning. Expert Syst Appl2023; 232: 120854.
16.
YuKLinT RMaH, et al. A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning. Mech Syst Signal Process2021; 146: 107043.
17.
HanTXieWPeiZ.Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine. Inform Sci2023; 648: 119496.
18.
DengCDengZMiaoJ.Semi-supervised ensemble fault diagnosis method based on adversarial decoupled auto-encoder with extremely limited labels. Reliab Eng Syst Saf2024; 242: 109740.
19.
MiaoJDengZDengC, et al. Boosting efficient attention assisted cyclic adversarial auto-encoder for rotating component fault diagnosis under low label rates. Eng Appl Artif Intell2024; 133: 108499.
20.
ZhaoZPangSWangL, et al. MedFedProto: a semi-supervised classification framework for medical images based on federated prototypical learning. Expert Syst Appl2025; 300: 130332.
21.
ZhengXZhouLXueA, et al. A unified open-world semi-supervised learning framework for industrial defect detection via contrastive embedding and dynamic attention. Eng Appl Artif Intell2026; 164: 113289.
22.
GuoCZhaoZRenJ, et al. Causal explaining guided domain generalization for rotating machinery intelligent fault diagnosis. Expert Syst Appl2024; 243: 122806.
23.
WangJRenHShenC, et al. Multi-scale style generative and adversarial contrastive networks for single domain generalization fault diagnosis. Reliab Eng Syst Saf2024; 243: 109879.
24.
LuBZhangYSunQ, et al. A novel multidomain contrastive-coding-based open-set domain generalization framework for machinery fault diagnosis. IEEE Trans Ind Inform2024; 20: 6369–6381.
25.
RenLMoTChengX.Meta-learning based domain generalization framework for fault diagnosis with gradient aligning and semantic matching. IEEE Trans Ind Inform2023; 20(1): 754–764.
26.
ZhangGKongXWangQ, et al. Single domain generalization method based on anti-causal learning for rotating machinery fault diagnosis. Reliab Eng Syst Saf2024; 250: 110252.
27.
QianQZhouJQinY.Relationship transfer domain generalization network for rotating machinery fault diagnosis under different working conditions. IEEE Trans Ind Inform2023; 19(9): 9898–9908.
28.
ShiMDingCChangS, et al. Cross-domain class incremental broad network for continuous diagnosis of rotating machinery faults under variable operating conditions. IEEE Trans Ind Inform2024; 20(4): 6356–6368.
29.
SongQJiangXLiuJ, et al. Contrast-assisted domain-specificity-removal network for semi-supervised generalization fault diagnosis. IEEE Trans Neural Netw Learn Syst2024; 36: 1–14.
30.
LiJShenCKongL, et al. A new adversarial domain generalization network based on class boundary feature detection for bearing fault diagnosis. IEEE Trans Instrum Meas2022; 71: 1–9.
31.
RenHWangJZhuZ, et al. Domain fuzzy generalization networks for semi-supervised intelligent fault diagnosis under unseen working conditions. Mech Syst Signal Process2023; 200: 110579.
32.
ZhuRPengWWangD, et al. Bayesian transfer learning with active querying for intelligent cross-machine fault prognosis under limited data. Mech Syst Signal Process2023; 183: 109628.
33.
LiaoYHuangRLiJ, et al. Deep semisupervised domain generalization network for rotary machinery fault diagnosis under variable speed. IEEE Trans Instrum Meas2020; 69(10): 8064–8075.
34.
RenHWangJHuangW, et al. Domain-invariant feature fusion networks for semi-supervised generalization fault diagnosis. Eng Appl Artif Intell2023; 126: 107117.
35.
ZhangXHuangWHuangfuY, et al. EnergyMatch: a long-tailed semi-supervised learning framework for cross-domain fault diagnosis. Reliab Eng System Saf2025; 269: 112013.
36.
LiuWWenYYuZ, et al. Large-margin softmax loss for convolutional neural networks. In: Proceedings of the 33rd International Conference on Machine Learning, New York, NY, 2016. JMLR: W&CPvolume 48.
37.
LiuWWenYYuZ, et al. Sphereface: deep hypersphere embedding for face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, 2017, pp. 212–220.
38.
WangFChengJLiuW, et al. Additive margin softmax for face verification. IEEE Signal Process Lett2018; 25(7): 926–930.
ChenLLiQShenC, et al. Adversarial domain-invariant generalization: a generic domain-regressive framework for bearing fault diagnosis under unseen conditions. IEEE Trans Ind Inform2021; 18(3): 1790–1800.
