Research on Fault Diagnosis Method Based on Deep Canonical Correlation Analysis for Multi-View Feature Learning

Due to the increasing complexity of industrial equipment failure signals and the subtle nature of failure characteristics, fault diagnosis of equipment presents a significant challenge. To effectively address the issue of feature extraction and selection for complex fault signals collected by different sensors, this paper first designs a feature fusion network based on raw sensor data, which is used to extract and integrate fault information from the same type of sensor signals. Then, a multi-view feature learning network is employed to further enrich the fault feature space. Through deep fusion of the feature data, the fault recognition performance is enhanced. Finally, the fused features are input into a classifier for training, achieving intelligent identification and diagnosis of different fault types. Experimental results show that this method can automatically extract effective fault features from multi-sensor signals and significantly outperforms traditional feature extraction methods in terms of diagnostic accuracy and efficiency.