Electric Motor Compound Fault Diagnosis Using Adaptive Feature Weighting Fusion of Multisource Information With Deep Convolutional Neural Networks

An electric motor is a paramount part of rotating machinery, which plays an important role in industrial applications. The motor is an electromechanical coupling system, prone to electromagnetic mechanical coupling failure. It is difficult for traditional fault diagnosis methods based on a single signal source to comprehensively extract features of composite faults and ensure reliable results, as composite fault signals are coupled and interfere with each other. Recently, fault diagnosis methods leveraging multi-source heterogeneous information fusion have gained significant attention. Traditional information fusion techniques employ fixed-weight fusion strategies but suffer from a lack of dynamic adaptability. Although attention mechanism-based fusion enables dynamic feature weighting to overcome this limitation, its opacity and lack of physical interpretability remain challenges. To address these challenges, this paper proposes a novel model for electric motor compound fault diagnosis, termed the multi-source heterogeneous data adaptive feature fusion deep convolutional neural network. This model incorporates a multi-channel architecture that effectively preventing the mixing of feature information. The feature weights of different signals during data fusion are dynamically adjusted through an interpretable adaptive fusion strategy to capture more comprehensive fault features. The dropout method is employed to reduce redundancy and enhance the extraction of quality features from different signal sources. Finally, experimental results on fault detection on the experimental platform show that the accuracy of this method has reached 99.42%, demonstrating superior fault diagnosis capability, proving the feasibility and effectiveness of the method.