This study addresses the challenge of low recognition precision in single fault signal isolation for induction motors by proposing a novel fault diagnosis strategy that integrates multi-source information and an enhanced Convolutional Neural Network (CNN). This approach aims to mitigate the effects of strong nonlinear correlations inherent in fault characteristics. Initially, vibration and stator current signals are preprocessed using denoising autoencoders to improve signal quality. Subsequently, multi-source homogeneous data are fused at the data layer using a correlation variance contribution rate method, effectively integrating information from disparate sources. The fused signals are then transformed into two-dimensional images, serving as input for a refined CNN architecture designed to handle heterogeneous data integration and feature extraction. Finally, the proposed adaptive CNN fault diagnostic model is evaluated using induction motor test datasets. Empirical results demonstrate the method’s ability to effectively utilize both redundant and complementary information from multiple sources and to model the nonlinear dynamics of feature datasets. Specifically, the proposed method achieves an average diagnostic accuracy of 99.0% at 1800 r/min and 94.8% at 2400 r/min, significantly outperforming traditional methods under the same conditions. Furthermore, when compared to other advanced multi-source fusion techniques, the proposed method demonstrates superior performance. These results highlight highlights its potential as a robust tool for induction motor fault diagnosis.
Bo et al. (Fri,) studied this question.