DPMSLM Eccentricity Fault Detection Based on Multi-view of Mystery Curve Transformation and Deep Feature Extraction

To detect the eccentricity fault of dual-sided permanent magnet synchronous linear motor (DPMSLM) and ensure the stable operation of the equipment, a new method based on multi-view of mystery curve transformation (MCT) and deep feature extraction is proposed to detect complex eccentricity faults of DPMSLM in this work. First, finite element analysis (FEA) calculation models of DPMSLM under different static eccentricity and dynamic eccentricity fault conditions are established to extract the external magnetic leakage signal (EMLS) as the efficient fault diagnostic signals. Second, a MCT signal processing method is proposed to convert a 1D EMLS into 3D curve and obtain 2D projections of multiple views (top, front, and side). This method achieves eccentricity fault signal visual display in 2D multi-view fusion image and realize complementary enhancement of fault characteristics. Thereafter, a novel classification deep learning framework, named SA-ConvNeXt, is proposed to conduct deep fault feature extraction and realize eccentricity faults accurate classification in fault types and severity levels. The diagnostic accuracy of SA-ConvNeXt is as high as 99.5%, which is better than those of comparison models, such as CNN, ResNet-34, ShuffleNet, and ConvNeXt. Finally, tunnel magnetoresistance (TMR) sensor circuit hardware is integrated designation with motor mover module to realize EMLS data non-invasive online measurement, and the DPMSLM experimental platform under several eccentricity faults is built to verify the superiority and robustness of the proposed method.