The scarcity of labeled fault data in rotating machinery diagnosis poses significant challenges for model reliability, particularly when encountering novel operational scenarios or cross-domain applications. While unsupervised domain adaptation (UDA) has emerged as a promising solution, conventional UDA methods often exhibit static architectures that fail to dynamically adjust to target-domain distribution shifts, thereby limiting diagnostic accuracy and practical utility. To address these limitations, we propose an adaptive dynamic cooperative multisource domain adaptation network (ADCMDAN) for rotating machinery fault diagnosis. Firstly, a multi-perspective dynamic cooperative feature extractor is constructed to enable the model to dynamically adjust its architecture, thereby enhancing its ability to extract domain-invariant features across different domains. Secondly, a dynamic cooperative training mechanism is designed to adaptively adjust the training strategy that conducts targeted training for the model’s bi-module in different training phases, so as to strengthen the domain adaptation performance. Finally, a dual-object dynamic optimization loss is introduced to treat distinctively different source domains, thereby improving the utilization of existing diagnostic knowledge. Extensive experimental evaluations are conducted based on two different cases to rigorously assess the diagnostic performance of ADCMDAN. The results conclusively demonstrate that the proposed method achieves exceptional fault identification accuracy even in the absence of labeled target-domain data.
JiaZLiuZYaoG, et al. An incremental learning framework for mechanical fault diagnosis with bi-level multiscale convolutional attention. IEEE Trans Instrum Meas2025; 74: 3548013.
2.
RenXWangSZhaoW, et al. Universal federated domain adaptation for gearbox fault diagnosis: a robust framework for credible pseudo-label generation. Adv Eng Inform2025; 65: 103233.
3.
DongYJiangHMuM, et al. Multi-sensor data fusion-enabled lightweight convolutional double regularization contrast transformer for aerospace bearing small samples fault diagnosis. Adv Eng Inform2024; 62: 102573.
4.
LiaoJHeCLiJ, et al. Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under noisy conditions. Mech Syst Signal Process2025; 222: 111750.
5.
LiGWeiMWuD, et al. Zero-Sample fault diagnosis of rolling bearings via fault spectrum knowledge and autonomous contrastive learning. Expert Syst Appl2025; 275: 127080.
6.
YamadaKDLinFNakamuraT.Developing a novel recurrent neural network architecture with fewer parameters and good learning performance. Interdiscip Inf Sci2021; 27(1): 25–40.
7.
GaoCCaiGJiangX, et al. Conditional feature learning based transformer for text-based person search. IEEE Trans Image Process2022; 31: 6097–6108.
8.
ZhangCTianYXieY, et al. Prediction of thrust bearing parameters in shafting-shell coupling system from frequency response function with artificial neural networks. Appl Ocean Res2025; 158: 104528.
9.
SongBLiuYFangJ, et al. An optimized CNN-BiLSTM network for bearing fault diagnosis under multiple working conditions with limited training samples. Neurocomputing2024; 574: 127284.
10.
BharatheedasanKMaityTKumaraswamidhasL, et al. Enhanced fault diagnosis and remaining useful life prediction of rolling bearings using a hybrid multilayer perceptron and LSTM network model. Alex Eng J2025; 115: 355–369.
11.
GaoSXuLZhangY, et al. Rolling bearing fault diagnosis based on SSA optimized self-adaptive DBN. ISA Trans2022; 128: 485–502.
12.
WuZJiangHLiuS, et al. A Gaussian-guided adversarial adaptation transfer network for rolling bearing fault diagnosis. Adv Eng Inform2022; 53: 101651.
13.
XuZLeeCWongC.A novel fault diagnosis method based on deep stable learning for bearings with imbalanced data samples, Expert Syst Appl2025; 281: 127634.
14.
YangBLeiYJiaF, et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mech Syst Signal Process2019; 122: 692–706.
15.
QianQWenQTangR, et al. DG-Softmax: a new domain generalization intelligent fault diagnosis method for planetary gearboxes. Reliab Eng Syst Saf2025; 260: 111057.
16.
JiaNHuangWDingC, et al. Physics-informed unsupervised domain adaptation framework for cross-machine bearing fault diagnosis. Adv Eng Inform2024; 62: 102774.
17.
ZhongJLinCGaoY, et al. Fault diagnosis of rolling bearings under variable conditions based on unsupervised domain adaptation method. Mech Syst Signal Process2024; 215: 111430.
18.
HuaZShiJDumondP.Domain-invariant feature exploration for intelligent fault diagnosis under unseen and time-varying working conditions. Mech Syst Signal Process2025; 224: 112193.
19.
WuZJiangHLuT, et al. A deep transfer maximum classifier discrepancy method for rolling bearing fault diagnosis under few labeled data. Knowl Based Syst2020; 196: 105814.
20.
LiXChenHLiS, et al. Multi-kernel weighted joint domain adaptation network for cross-condition fault diagnosis of rolling bearings. Reliab Eng Syst Saf2025; 261: 111109.
21.
PanXChenHWangW, et al. Adversarial domain adaptation based on contrastive learning for bearings fault diagnosis. Sim Model Pract Theory2025; 139: 103058.
22.
DuZLiuDCuiL.Dynamic model-driven dictionary learning-inspired domain adaptation strategy for cross-domain bearing fault diagnosis. Reliab Eng Syst Saf2025; 258: 110905.
23.
WuZJiangHLiuS, et al. Conditional distribution-guided adversarial transfer learning network with multi-source domains for rolling bearing fault diagnosis. Adv Eng Inform2023; 56: 101993.
24.
LiQQinLXuH, et al. Transparent information fusion network: an explainable network for multi-source bearing fault diagnosis via self-organized neural-symbolic nodes. Adv Eng Inform2025; 65: 103156.
25.
XuXYangXQiaoZ, et al. Multi-source domain adaptation using diffusion denoising for bearing fault diagnosis under variable working conditions. Knowl Based Syst2024; 302: 112396.
26.
ZhaoKJiangHLiX, et al. Ensemble adaptive convolutional neural networks with parameter transfer for rotating machinery fault diagnosis. Int J Mach Learn Cyb2021; 12(5): 1483–1499.
27.
RezaeianjouybariBShangY.A novel deep multi-source domain adaptation framework for bearing fault diagnosis based on feature-level and task-specific distribution alignment. Measurement2021; 178: 109359.
28.
WuZJiangHZhuH, et al. A knowledge dynamic matching unit-guided multi-source domain adaptation network with attention mechanism for rolling bearing fault diagnosis. Mech Syst Signal Process2023; 189: 110098.
29.
JinYSongXYangY, et al. An improved multi-channel and multi-scale domain adversarial neural network for fault diagnosis of the rolling bearing. Control Eng Pract2025; 154: 106120.
30.
WangJChenY.Introduction to transfer learning: algorithms and practice. Singapore: Springer, 2023.
31.
GoodfellowIPougetJMirzaM, et al. Bengio, generative adversarial nets. Adv Neural Inform Process Syst2014; 27: 2672–2680.
32.
YaoQQianQQinY, et al. Adversarial domain adaptation network with pseudo siamese feature extractors for cross-bearing fault transfer diagnosis. Eng Appl Artif Intell2022; 113: 104932.
33.
WangJZhangZLiuZ, et al. Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis. Reliab Eng Syst Saf2023; 234: 109152.
34.
YuXZhaoXZhangX.Conditional adversarial domain adaptation with discrimination embedding for locomotive fault diagnosis. IEEE Trans Instrum Meas2021; 70: 3503812.
35.
ZhaoCZioEShenW.Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study. Reliab Eng Syst Saf2024; 245: 109964.
36.
SunBSaenkoK. Deep CORAL: correlation alignment for deep domain adaptation. In: 14th European conference on computer vision (ECCV), Amsterdam, Netherlands, 2016, pp. 443–450. Springer.
37.
ZhaoKJiangHWangK, et al. Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis. Knowl Based Syst2021; 222: 106974.
38.
QianQQinYLuoJ, et al. Deep discriminative transfer learning network for cross machine fault diagnosis. Mech Syst Signal Process2023; 186: 109884.
39.
PengXBaiQXiaX, et al. Moment matching for multi-source domain adaptation. In: IEEE/CVF international conference on computer vision (ICCV), 2019, pp. 1406–1415. New York: IEEE.
40.
ZhuYZhuangFWangD.Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources. In: 33rd AAAI conference on artificial intelligence, Honolulu, HI, 2019, 5989–5996. AAAI.
41.
HouLYiHJinY, et al. Inter-shaft bearing fault diagnosis based on aero-engine system: a benchmarking dataset study. JDMD2023; 2: 228–242.
