Fault diagnosis results of 5 methods (1800r/min).

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.

本研究针对感应电动机单一故障信号隔离任务中识别精度偏低的挑战，提出了一种融合多源信息与增强型卷积神经网络（Convolutional Neural Network, CNN）的新型故障诊断策略，旨在缓解故障特性固有的强非线性关联带来的不利影响。首先，采用去噪自编码器对振动信号与定子电流信号进行预处理，以提升信号质量。随后，通过相关方差贡献率法在数据层完成多源同质数据融合，有效整合来自不同采集源的信息。将融合后的信号转换为二维图像，作为优化后的CNN架构的输入，该架构可适配异构数据集成与特征提取任务。最后，借助感应电动机测试数据集对所提出的自适应CNN故障诊断模型进行评估。实验结果表明，所提方法能够有效利用多源信息中的冗余与互补信息，并实现特征数据集非线性动力学特性的建模。具体而言，所提方法在1800转/分钟（r/min）下的平均诊断准确率达99.0%，在2400转/分钟下达94.8%，在相同条件下显著优于传统故障诊断方法。此外，与其他先进的多源融合技术相比，所提方法展现出更优异的性能。上述结果充分彰显了该方法作为感应电动机故障诊断可靠工具的应用潜力。