A cross-domain multi-scale feature fusion network based on graph convolution for intelligent fault diagnosis

Fault diagnosis of mechanical equipment plays a critical role in enhancing system stability and ensuring operational safety. Multi-modal monitoring data provides a more comprehensive view of the equipment’s condition, enabling more precise state diagnosis through the processing of such data. A major challenge in contemporary multi-modal information fusion lies in the effective integration of cross-modal information. To overcome the inherent limitations of single-view approaches often found in existing multi-modal methods, this paper presents a multi-view, multi-modal information fusion approach based on graph convolutional networks. This method not only extracts key features from signals efficiently but also uncovers the interrelationships between different modal signals through a multi-view interaction mechanism, achieving more robust information fusion and enhanced fault diagnosis performance. This method consists of three core modules: feature extraction, information fusion, and classification. The feature extraction module utilizes a multi-level residual architecture, Mini-Long Short Term Memory, and self-attention mechanisms to capture both local and global signal features, model temporal dependencies, and refine feature selection. The information fusion module combines data and feature-level signal correlations using graph convolutional networks for cross-modal interaction. The classification module employs a two-layer fully connected network for fault diagnosis. This method quantitatively assesses the contribution of each modality, providing a theoretical basis for understanding their physical significance. Experimental results and ablation studies demonstrate its superior performance and enhanced accuracy in fault diagnosis, offering a novel approach for condition monitoring of complex equipment.