TL;DR
A global-to-local domain adaptation framework based on hybrid discrepancy measures and neighbor-aware pseudo-label refinement is proposed for fault diagnosis of rotating machinery, resulting in higher diagnostic accuracy, more stable convergence, and stronger domain alignment.
Validation on multiple public bearing datasets demonstrates that IGDATL achieves higher accuracy and superior domain generalization performance, providing an innovative and practical solution for cross-domain intelligent diagnosis of complex industrial systems.
An explainable domain-adaptive CNN–Transformer framework for unsupervised bidirectional cross-domain bearing fault diagnosis using the Case Western Reserve University (CWRU) and Paderborn University (PU) datasets, which confirms the complementary contributions of convolutional feature extraction, Transformer-based temp...
DDDG, a dendrite-network-assisted multi-source domain generalization framework for fault diagnosis in rotating machinery, is proposed, and the results demonstrate that DDDG achieves high accuracy under unseen operating conditions, exhibiting strong generalizability.
MAML is enhanced by incorporating a Sample Relationship Exploration module that learns intra-class similarity to improve class separability and replaces the fixed inner-loop update scheme in MAML with a trapezoidal gradient descent scheduler that adapts the number of inner-loop update steps across training.
UCTL, an end-to-end unsupervised cross-sensor transfer learning network that enables the transfer and reuse of fault features from labeled source monitoring points to unlabeled target monitoring points with disparate data distributions, thereby achieving cross-sensor domain adaptation in multi-point monitoring systems...
PDAPN first employs episodic prototypical learning to construct stable and discriminative class representations from scarce labeled source-domain samples, and a maximum of cosine similarity-based local partial alignment mechanism is introduced to identify target-compatible source-domain anchors and reduce negative tran...
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