Experiments show that the proposed adaptive variable-scale lightweight convolutional neural network (AVS-LCNN) achieves a diagnostic accuracy rate of over 99% with only 0.17 M parameters, demonstrating a favorable balance among computational accuracy, robustness and inference efficiency.
A diagnostic framework that combines generative-adversarial data augmentation with dual-branch time–frequency representation learning to improve feature quality and fault classification and achieves better performance than baseline models and maintains robustness under data imbalance and noisy conditions is proposed.
The results show that SAMACNN outperforms both classical and advanced methods on the two datasets, demonstrating strong robustness and generalization capability in complex variable-condition measurement environments.
Conventional single-network models struggle to simultaneously capture local transient impacts, temporal dependencies, and contextual correlations from rolling bearing vibration signals. Moreover, traditional serial hybrid architectures suffer from feature dilution, wherein weak fault features extracted by upstream laye...
A novel diagnosis method integrating FFT-VMD feature extraction with a Bi-TCN-Bi-GRU neural network, which maintains an exceptional diagnostic accuracy even under severe background noise.
Vision Transformer with multi-channel and multiscale adaptive feature fusion (MCMSAF-ViT), a lightweight acoustic-vibration bimodal ViT achieves consistently high accuracy, outperforming baselines like ResNet and EfficientNet under noise and complex working conditions.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
