An integrated approach for mechanical fault diagnosis using maximum mean square discrepancy representation and CNN-based mixed information fusion

Abstract

Bearings are an essential component of modern industry and cross-domain diagnosis of bearings holds significant importance. However, in practical applications, issues such as insufficient training data and differences between equipment present challenges. Transfer learning has become one of the effective methods to address these problems. This article proposes a fault diagnosis method that combines maximum mean square discrepancy (MMSD) measurement with the convolutional neural network (CNN) with mixed information (MIXCNN). MIXCNN enhances spatial position discrimination through deep convolution and achieves cross-channel information interaction using traditional convolution. The introduction of residual connections reduces information loss, while increasing network depth focuses on highly distinguishable features. MMSD constructs a metric that comprehensively reflects the mean and variance information of data samples in the reproducing kernel Hilbert space, thereby enhancing domain confusion. Experimental results show that this method achieves high diagnostic accuracy in various transfer tasks, with a maximum accuracy of 99.29%, providing reliable support for bearing fault diagnosis.