TL;DR
An improved dynamic pseudo-label learning strategy is developed, improving model generalization by involving more unlabeled data in training, and a noise reduction learning strategy based on self-supervised learning is proposed, extracting invariant features under noise conditions by a new optimization objective.
This study proposes a robust bearing fault diagnosis method based on an adaptive weighted heterogeneous ensemble learning framework, capable of effectively reducing and eliminating noise interference in two-dimensional image data, thus enhancing fault diagnosis accuracy.
A novel intelligent fault diagnosis framework, termed PGDS-CLNet, is proposed in this study, which is an end-to-end trainable diagnostic network after standard signal normalization and segmentation.
A fault diagnosis framework integrating deep learning with signal processing, which consists of probabilistic principal component analysis for noise suppression, the autoregressive (AR) model for discrete interference elimination, spectral kurtosis (SK) for fault feature enhancement, and LSTM-based intelligent classifi...
A GSR-enhanced robust sparse feature learning framework that derives stability control conditions for the GSR system, integrates a lightweight dual-branch architecture to fuse global convolutional features and local sparse spectral peak features, and constructs a Wasserstein-regularized dual-loss function to optimize t...
A small sample transfer diagnosis method driven by a high-precision dynamic model for rolling bearings that not only effectively diagnoses rolling bearing faults but also exhibits excellent generalization ability on small sample datasets is proposed.
The analysis indicates that existing studies frequently investigate TL and SSL methods for bearing fault diagnosis independently, rely predominantly on controlled benchmark datasets, and provide limited validation of cross-machine and cross-condition generalization.
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