DPMSLM Demagnetization Fault Detection Based on Texture Feature Analysis of Grayscale Fusion Image

• Published in: IEEE transactions on instrumentation and measurement 2023-01, Vol.72, p.1-1
• Main Authors: Song, Juncai, Liu, Shuo, Duan, Zhangling, Wu, Xianhong, Ding, Wei, Wang, Xiaoxian, Lu, Siliang
• Format: Article
• Language: English
• Subjects: Air gaps; Algorithms; Demagnetization; Demagnetization fault location and severity detection; double-sided permanent magnet synchronous linear motor (DPMSLM); Electric motors; Fault detection; Fault location; Feature extraction; Finite element method; GFI; Gray scale; Ground fault interruptions; Image enhancement; image morphology texture feature extraction; Inspection; Magnetic resonance imaging; Mathematical morphology; Morphology; MVO-SVM; Optimization; Permanent magnet motors; Permanent magnets; Support vector machines; Synchronous motors; Texture; Topology
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3259035

This study investigates a novel image morphology texture feature extraction method to realize the demagnetization fault location and severity detection of double-sided permanent magnet synchronous linear motor (DPMSLM). Initially, according to the constraints of DPMSLMs topology structure, the three lines magnetic density signal in motor air gap is extracted by finite element analysis as effective fault signal. Then, the grayscale fusion image (GFI) method is introduced to transform 1D data signal to 2D fused grayscale image which can better describe the demagnetization fault information. The unique features are visualized using the image enhancement techniques, and the image morphology texture features such as the area, Euler number, perimeter operator, correlation of binary image and so on can be extracted to constitute the demagnetization fault indexes. In addition, fisher score (FS) is used for feature optimization which can reduce the feature dimension. Furthermore, the two-level multiverse optimization support vector machine (MVO-SVM) algorithm is established to conduct demagnetization fault classification. Comparison experiments with other classifiers show that the MVO-SVM has a high fault identification accuracy of more than 98.3% and low running time less than 2.57s. Finally, the motor prototype experiment results show that the proposed method can accurately identify the location and severity of DPMSLM demagnetization faults, and it is an effective and feasible method which can be applied in DPMLM batch demagnetization inspection before delivery.