Deployment of transfer learning methods for intelligent fault diagnosis has been limited by the scarcity of labeled data and label-space mismatch across domains. Here, a simulation-driven partial domain adaptation (SPDA) framework was presented that couples a validated fault-dynamics model (label-rich simulation source) with a physical test rig (unlabeled measurement target) for cylindrical roller bearings. Within SPDA, a scale-aware entropy-minimizing ambiguity domain adaptation network was developed that combines adversarial alignment with class- and instance-scale weighting and entropy minimization to mitigate negative transfer from source-only classes under partial label shift. Experiments on a NU216 benchmark covering 28 sim-to-real transfer tasks show consistent improvements over recent closed-set and partial domain adaptation baselines across diverse load–speed conditions. These results suggest that physics-informed simulation, paired with a domain adaptation algorithm, provides a practical route to reliable diagnosis when exhaustive labeled field data are unavailable.
ZhongHHeDSunH, et al. A novel meta-learning method based on relation network for train bearing fault diagnosis. Nonlinear Dyn2025; 113: 27001–27029.
2.
JiangXWangJShenC, et al. An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis. Struct Health Monit2021; 20(5): 2708–2725.
3.
ZhangCZhangL. Wind turbine pitch bearing fault detection with Bayesian augmented temporal convolutional networks. Struct Health Monit2024, 23(2): 1089–1106.
4.
LiYWuJ. Extended dispersion entropy-based Lempel–Ziv complexity: a novel metric for rolling bearing fault diagnosis. Nonlinear Dyn2025; 113(10): 11423–11437.
5.
HuangRLiJLiaoY, et al. Deep adversarial capsule network for compound fault diagnosis of machinery toward multidomain generalization task. IEEE Trans Instrum Meas2020; 70: 1–11.
6.
LiWLanHChenJ, et al. WavCapsNet: an interpretable intelligent compound fault diagnosis method by backward tracking. IEEE Trans Instrum Meas2023; 72: 1–11.
7.
LiDChenJHuangR, et al. Sensor-aware CapsNet: towards trustworthy multisensory fusion for remaining useful life prediction. J Manuf Syst2024; 72: 26–37.
8.
YaoQQianQQinY, et al. Adversarial domain adaptation network with pseudo-siamese feature extractors for cross-bearing fault transfer diagnosis. Eng Appl Artif Intell2022; 113: 104932.
9.
WangYQZhaoYPLiangBY, et al. Weighted class-aware matching adaptation network for aero-engine imbalanced multi-source cross-domain fault diagnosis under class shift. Eng Appl Artif Intell2025; 149: 110510.
10.
LiYBaoTGaoZ, et al. A new dam structural response estimation paradigm powered by deep learning and transfer learning techniques. Struct Health Monit2022; 21(3): 770–787.
11.
MingZTangBDengL, et al. Simulation data-driven adaptive frequency filtering focal network for rolling bearing fault diagnosis. Eng Appl Artif Intell2024; 138: 109371.
12.
QianQWangYZhangT, et al. Maximum mean square discrepancy: a new discrepancy representation metric for mechanical fault transfer diagnosis. Knowl Based Syst2023; 276: 110748.
13.
WangRHuangWShiJ, et al. A higher-order moment matching based fine-grained adversarial domain adaptation method for intelligent bearing fault diagnosis. Meas Sci Technol2022; 33(5): 055022.
14.
ChangQFangCZhouW, et al. A multi-order moment matching-based unsupervised domain adaptation with application to cross-working condition fault diagnosis of rolling bearings. Struct Health Monit2024; 24(3): 1438–1455.
15.
JiaoJLinJZhaoM, et al. Double-level adversarial domain adaptation network for intelligent fault diagnosis. Knowl Based Syst2020; 205: 106236.
16.
WanLLiYChenK, et al. A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis. Measurement2022; 191: 110752.
17.
LiangPWangBJiangG, et al. Unsupervised fault diagnosis of wind turbine bearing via a deep residual deformable convolution network based on subdomain adaptation under time-varying speeds. Eng Appl Artif Intell2023; 118: 105656.
18.
WangHWangPWangS, et al. Rolling bearing fault diagnosis method based on dynamic simulated model from source domain to target domain with improved alternating transfer learning. Nonlinear Dyn2025; 113(5): 4485–4510.
19.
LiGLiuSHeJ, et al. A multi-domain adversarial transfer network for cross domain fault diagnosis under imbalanced data. Eng Appl Artif Intell2024; 136: 108948.
20.
ZhaoKJiangHWangK, et al. Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis. Knowl Based Syst2021; 222: 106974.
21.
WangXYanJXuX. A novel dynamic spatio-temporal graph based condition monitoring framework for consistency retention of digital twin. J Manuf Syst2025; 79: 455–465.
22.
LiZDingXSongZ, et al. Digital twin-assisted dual transfer: a novel information-model adaptation method for rolling bearing fault diagnosis. Inform Fusion2024; 106: 102271.
23.
ZhangYJiJCRenZ, et al. Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing. Reliab Eng Syst Saf2023; 234: 109186.
24.
YuJWangSWangL, et al. Gearbox fault diagnosis based on a fusion model of virtual physical model and data-driven method. Mech Syst Signal Proc2023; 188: 109980.
25.
XiaPHuangYTaoZ, et al. A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern. Reliab Eng Syst Saf2023; 235: 109256.
26.
FengKJiJCZhangY, et al. Digital twin-driven intelligent assessment of gear surface degradation. Mech Syst Signal Proc2023; 186: 109896.
27.
YanSZhongXShaoH, et al. Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization. Reliab Eng Syst Saf2023; 239: 109522.
28.
FangCPengYGuanY, et al. A new numerical method for the tribo-dynamic analysis of cylindrical roller bearings. Nonlinear Dyn2023; 111(12): 11275–11295.
29.
FangCMengXXieY. A piston tribodynamic model with deterministic consideration of skirt surface grooves. Tribol Int2017; 110: 232–251.
30.
LiuJCaoHSuS, et al. Simulation-driven subdomain adaptation network for bearing fault diagnosis with missing samples. Eng Appl Artif Intell2023; 123: 106201.
31.
LiSJiangQXuY, et al. Digital twin-driven focal modulation-based convolutional network for intelligent fault diagnosis. Reliab Eng Syst Saf2023; 240: 109590.
32.
LiXLiuJDingS, et al. Dynamic modeling and vibration analysis of double row cylindrical roller bearings with irregular-shaped defects. Nonlinear Dyn2024; 112(4): 2501–2521.
33.
RegisAArroyave-TobónSLinaresJM, et al. Physic-based vs data-based digital twins for bush bearing wear diagnostic. Wear2023; 526: 204888.
34.
MihaiSYaqoobMHungDV, et al. Digital twins: a survey on enabling technologies, challenges, trends and future prospects. IEEE Commun Surv Tutor2022; 24(4): 2255–2291.
35.
MaALiJLuK, et al. Adversarial entropy optimization for unsupervised domain adaptation. IEEE Trans Neural Netw Learn Syst2021; 33(11): 6263–6274.
36.
ChenWLiJWangQ, et al. Fault feature extraction and diagnosis of rolling bearings based on wavelet thresholding denoising with CEEMDAN energy entropy and PSO-LSSVM. Measurement2021; 172: 108901.
37.
GuoLYuYLiuY, et al. Reconstruction domain adaptation transfer network for partial transfer learning of machinery fault diagnostics. IEEE Trans Instrum Meas2021; 71: 1–10.
38.
KuangJXuGTaoT, et al. Dual-weight consistency-induced partial domain adaptation network for intelligent fault diagnosis of machinery. IEEE Trans Instrum Meas2022; 71: 1–12.
39.
ZhangXWangJJiaS, et al. Partial domain adaptation method based on class-weighted alignment for fault diagnosis of rotating machinery. IEEE Trans Instrum Meas2022; 71: 1–14.
