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Air-Gap Asymmetry Fault Diagnosis of PMSLM Based on External Magnetic Field Data and Few-Shot Learning Framework
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AI
宋
J
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B
X
Z
S
DOI：10.1109/tmech.2025.3649913.png)
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Abstract
En 中文
Air-gap asymmetry faults (AAFs) in permanent magnet synchronous linear motors (PMSLMs) increase thrust fluctuation, reduce positioning accuracy, and impair reliable operation. Existing methods exhibit insufficient feature extraction capability and heavy reliance on data. To address these drawbacks, this article presents an efficient approach for detecting AAFs in PMSLMs. First, external stray magnetic field signals corresponding to different AAFs are extracted through a noninvasive signal acquisition scheme that utilizes tunnel magnetoresistance sensors. Second, an optimized recurrence plot signal processing method is proposed to enable visual representation and feature enhancement. Next, a few-shot learning framework named efficient multiscale faster prototype network is proposed to realize precise diagnosis of AAF types, achieving a higher classification accuracy of 97.75% and a shorter inference time of 11.15 ms compared with other models. Notably, in contrast to traditional deep learning-based diagnostic methods, the proposed approach yields substantial improvements in two key aspects: namely the required training dataset size and the time incurred in its construction. Furthermore, the method has been rigorously validated via both simulations and physical motor prototype experiments.
Keywords:
Air-gap asymmetry fault (AAF)
efficient multiscale faster prototype network
few-shot learning (FSL)
optimized recurrence plot (ORP)
permanent magnet synchronous linear motor (PMSLM)
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