Zhunan Shen

Accurate fault diagnosis is essential for the safe operation of rotating machinery. Recently, traditional deep learning-based fault diagnosis have achieved promising results. However, most of these methods focus only on supervised learning and tend to use small convolution kernels non-effectively to extract features that are not controllable and have poor interpretability. To this end, this study proposes an innovative semi-supervised learning method for bearing fault diagnosis. Firstly, multi-scale dilated convolution squeeze-and-excitation residual blocks are designed to exact local and global features. Secondly, a classifier generative adversarial network is employed to achieve multi-task learning. Both unsupervised and supervised learning are performed simultaneously to improve the generalization ability. Finally, supervised learning is applied to fine-tune the final model, which can extract multi-scale features and be further improved by implicit data augmentation. Experiments on two datasets were carried out, and the results verified the superiority of the proposed method.

With continuous advancements in sensor technology and computer vision, automated surface defect detection has become an important problem in the modern manufacturing industry. Although deep-learning-based defect detection methods have proven successful in various inspection tasks, they often require extensive high-quality pixel-level annotations for model training. Industrial defects, characterized by diverse shapes and intricate textures, require manual labelling by skilled inspectors. This study introduces a weakly supervised defect segmentation framework called the progressively learning dynamic level set (PLDL) to overcome these constraints. This framework incorporates two parallel learning modules and a differentiable level set module interconnected through a progressive learning strategy facilitated by an innovative loss function. Using only image-level labels as inputs, the PLDL framework iteratively refines object boundaries, dynamically optimizes the training process, and generates pixel-level outputs. Our method is efficient and consistent, requiring no manual supervision or post-processing steps. Experimental results on four benchmark datasets representing diverse industrial scenarios reveal that PLDL outperforms recent weakly supervised models. It achieved 76.52%, 85.23%, 85.71%, 84.43%, and 83.17% in mIoU, mPA, Precision, Recall, and F-measure, respectively, indicating its superior effectiveness in weakly supervised defect segmentation.