Accurate Detection and Evaluation of the Airgap Asymmetry Fault in DS-PMSLM Based on OSVT and ECA-ENet

Abstract

A new method based on magnetic signal analysis and artificial intelligence framework is proposed to detect airgap asymmetry fault (AAF) of double-sided permanent magnet synchronous linear motor (DS-PMSLM). First, a 3-D finite element model (3D-FEM) is established to extract external stray magnetic field signals (ESMFSs) under different AAFs as efficient fault signals. Second, an optimized snowflake view transformer (OSVT) signal processing method is proposed to convert 1-D ESMFS into 2-D feature-enhanced images, which can realize signal visual display and characteristics enhancement. Then, a novel deep learning framework named efficient channel attention-EfficientNet (ECA-ENet) is proposed to conduct AAF feature extraction and realize precise diagnosis of AAF types and severity degrees. The AAF type classification accuracy and F1 score are as high as 99.50% and 99.74%, respectively. Furthermore, the AAF degree diagnostic index MAE and RMSE are remarkably low at 0.0720 and 0.0842 mm, respectively. These results indicate the proposed method is better than the compared methods, such as ResNet50, ShuffleNet, EfficientNetV1, and EfficientNetV2. Finally, tunnel magnetoresistance (TMR) sensor circuit hardware is integrated into the design of the motor mover module to realize ESMFS data noninvasive online measurement, and the DS-PMSLM experimental platform is built to validate the superiority and robustness of the proposed method.