Innovative integration of multi-scale residual networks and MK-MMD for enhanced feature representation in fault diagnosis

Abstract

This article proposes a method based on multi-scale expansion of residual neural networks to address challenges in the operation of rotating components, such as bearings and gears, under complex conditions where they are often affected by environmental noise. This interference leads to weaker fault characteristics, making feature selection difficult and increasing the presence of extraneous information features. To tackle this issue, the proposed method first employs a multi-scale feature residual neural network to extract features from vibration signals of rotating machinery. The method decomposes the signal into multiple sub-signals of different scales, extracting local features at each scale. It then uses residual connections to combine these local features to obtain a global feature representation. Furthermore, the article introduces a construction of the maximum mean discrepancy and minimization of entropy boundaries to adapt to the differences between two domains. The method utilizes multiple kernel functions to calculate distances between data at different scales and combines these distances to obtain a comprehensive measure. By employing the maximum mean discrepancy and minimization of entropy boundary approach, the method can more accurately determine whether signals at different scales belong to the same category, thereby improving diagnostic accuracy and robustness. Experimental results demonstrate the effectiveness of the proposed method in unsupervised cross-domain fault diagnosis tasks. Future work will focus on further optimizing the architecture of residual neural networks, enhancing feature extraction capabilities, and exploring advanced data augmentation methods to further improve the model's generalization performance.