In recent years, deep learning-based fault diagnosis methods have drawn lots of attention. However, for most cases, the success of machine learning-based models relies on the circumstance that training data and testing data are under the same working condition, which is too strict for real implementation cases. Combined with the features of robustness of deep convolutional neural network and vibration signal characteristics, information fusion technology is introduced in this study to enhance the feature representation capability as well as the transferability of diagnosis models. With the basis of multi-sensors and narrow-band decomposition techniques, a convolutional architecture named fusion unit is proposed to extract multi-scale features from different sensors. The proposed method is tested on two data sets and has achieved relatively higher generalization ability when compared with several existing works, which demonstrates the effectiveness of our proposed fusion unit for feature extraction on both source task and target task.

Addressing the phenomenon of data sparsity in hostile working conditions, which leads to performance degradation in traditional machine learning-based fault diagnosis methods, a novel Wasserstein distance-based asymmetric adversarial domain adaptation is proposed for unsupervised domain adaptation in bearing fault diagnosis. A generative adversarial network-based loss and asymmetric mapping are integrated to alleviate the difficulty of the training process in adversarial transfer learning, especially when the domain shift is serious. Moreover, a simplified lightweight architecture is introduced to enhance the generalization and representation capability and reduce the computational cost. Experimental results show that our method not only achieves outstanding performance with sufficient data, but also outperforms these prominent adversarial methods with limited data (both source and target domain), which provides a promising approach to real industrial bearing fault diagnosis.

Current data-driven fault diagnosis methods are prone to overfitting and a decrease in accuracy when working with only a limited number of labeled samples. Additionally, existing graph neural network-based fault diagnosis methods often fail to comprehensively utilize both global and local features. To address these challenges, we propose a rolling bearing fault diagnosis method based on Multi-Scale Weighted Visibility Graph(MSWVG) and a Multi-Channel Graph Convolutional Network(MCGCN). Our approach converts vibration signals into multiple weighted graphs from the perspective of geometric meaning and extracts local node feature information and global topology information of graphs using MCGCN. Experimental results demonstrate that our method achieves excellent performance under both sufficient and limited data conditions, providing a promising approach for real-world industrial bearing fault diagnosis.

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Wire bonding is a crucial process in the micro-assembly, as its quality directly affects the reliability of microwave components and their operating characteristics. Therefore, it is important to detect defects in wire bonding. Due to the diversity of chips, connections, and circuit substrates, the wire bonding regions vary greatly. Using image processing methods exclusively requires expert knowledge, and the solution lacks versatility. Meanwhile, in highly complex industrial scenarios, relying on end-to-end deep learning method alone cannot accomplish the task constrained by data volume and task difficulty. Therefore, we propose a three-stage wire bonding defect detection method that integrates deep learning with traditional image processing methods for the detection of complex wire bonding defects. In order to address the defect detection of more types of complex bonding images, we divide them into four categories and complete the detection step by step. In the first two stages, semantic segmentation and image processing methods are used in turn to complete the extraction of the region of interest (ROI), and in the third stage, we propose a defect recognition model based on Siamese network with a new feature fusion structure to enhance feature learning. Experiments show that the proposed three-stage method, which combines deep learning and image processing, can effectively detect wire bonding defects and is suitable for handling highly complex engineering tasks with greater efficiency and intelligence.