DPMSLM Demagnetization Fault Diagnosis Based on Deep Feature Fusion of External Stray Flux Signal

Abstract

To detect the demagnetization fault (DF) of a dual-sided permanent magnet synchronous linear motor, a new method based on deep feature fusion of external stray flux signal (ESFS) is proposed. First, finite element models under ideal materials and assembly conditions are established to extract ESFS to reflect DF information. Second, Markov transition field and recurrence plot transform 1-D signals into 2-D images, to realize DF feature visual enhancement. Low-rank representation networks can merge the advantages of both methods by image fusion. Then, an accurate diagnosis framework, as efficient channel attention-MobileNetV3, is proposed to conduct deep feature extraction and realize diagnosis in both qualitative fault type classification and fault degree evaluation aspects. The classification accuracy reaches 98.50%, and the evaluation index <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R²</i> reaches 0.96, superior to other frameworks. Finally, a tunnel magnetoresistance sensor is applied to realize ESFS noninvasive online measurement, and an experimental platform is built to certify the superiority.