Differential-Augmented Current Feature Learning Network With Multi-Information Interaction for Fault Diagnosis in Electromechanical Drive System

Current-based fault diagnosis has become a promising solution for electromechanical systems due to the low cost and easy access. However, most of the existing studies require additional signal processing or diagnostic expertise to preprocess the signals due to the effects of the fundamental component and electrical noise. It is challenging to extract effective fault-related features from the raw current signals. To this end, this article proposes a differential-augmented current feature learning network named CurrentNet for drive system fault diagnosis, which is an end-to-end model. First, a differential-augmented strategy based on the raw current signal is introduced to generate complementary current representations. Furthermore, a multi-information interaction module (MIIM) is designed to adaptively capture complementary shared information between the original and enhanced signals through a parallel mechanism. Also, efficient channel attention is used to reduce the complexity of the model. Our proposed method is experimentally evaluated on two datasets and presents obvious superiority over existing fault diagnosis methods.