Demagnetization fault diagnosis of controllable hybrid magnetic couplers based on deep neural networks

doi:10.3785/j.issn.1008-973X.2026.02.006

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (2): 279-286 DOI: 10.3785/j.issn.1008-973X.2026.02.006

Demagnetization fault diagnosis of controllable hybrid magnetic couplers based on deep neural networks

Shuang WANG1,2,3(

),Xitai ZHANG2,3,Yongcun GUO1,2,3,Shousuo SUN2,3

1. State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology, Huainan 232001, China
2. Collaborative Innovation Center for Mining Intelligent Technology and Equipment, Anhui University of Science and Technology, Huainan 232001, China
3. School of Mechatronics Engineering, Anhui University of Science and Technology, Huainan 232001, China

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Abstract

The demagnetization degree of a controllable hybrid magnetic coupler has a significant influence on its transmission performance, and accurate diagnosis is essential for stable operation. A demagnetization fault diagnosis method combining the Gramian angular summation field (GASF) and an improved residual network (ResNet) was proposed. Current density data at different positions in the air gap were obtained through finite element simulation. One-dimensional time-series data were converted into two-dimensional images using the GASF method to enhance feature representation. An attention mechanism was embedded into the ResNet structure to improve the network’s ability to learn fault features and perform classification. Experimental results show that the proposed method achieves an average diagnostic accuracy of 97.67%, which is higher than those of EfficientNet, RepVGG, ViT, and AlexNet by 15.04, 14.73, 10.66, and 9.37 percentage points, respectively. Furthermore, an experimental platform was established to verify the effectiveness of the proposed method in demagnetization fault diagnosis of controllable hybrid magnetic couplers, and an identification accuracy of 96.5% was achieved for actual demagnetization faults.

Key words： magnetic coupler Gramian angular summation field (GASF) residual network (ResNet) attention mechanism fault diagnosis

Received: 08 February 2025 Published: 03 February 2026

CLC:

TH 133

Fund: 安徽省高校杰出青年科研项目（2022AH020056）；国家自然科学基金资助项目（52274152）；安徽省自然科学优秀青年科研基金资助项目（2308085Y37）.

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	Shuang WANG
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Cite this article:

Shuang WANG,Xitai ZHANG,Yongcun GUO,Shousuo SUN. Demagnetization fault diagnosis of controllable hybrid magnetic couplers based on deep neural networks. Journal of ZheJiang University (Engineering Science), 2026, 60(2): 279-286.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.02.006 OR https://www.zjujournals.com/eng/Y2026/V60/I2/279

基于深度网络的可控混合式磁力耦合器退磁诊断

可控混合式磁力耦合器的退磁程度直接影响耦合器的传动性能，准确诊断是保障稳定运行的关键. 为此，将格拉姆角和场（GASF）与改进残差网络（ResNet）结合提出退磁故障诊断方法. 通过有限元仿真提取耦合器气隙内不同位置的电流密度数据，利用GASF方法将一维时序数据转换为二维图像，以增强特征表达能力. 在此基础上，将注意力机制嵌入ResNet结构，提升网络对故障细节特征的学习与分类能力. 实验表明，所提方法在测试集上的平均诊断准确率为97.67%，较EfficientNet、RepVGG、ViT、AlexNet分别提升15.04、14.73、10.66和 9.37个百分点. 通过实验平台验证所提方法在可控混合式磁力耦合器退磁故障诊断中的有效性和优越性，该方法对真实退磁故障的识别准确率为96.5%.

关键词： 磁力耦合器, 格拉姆角和场（GASF）, 残差网络（ResNet）, 注意力机制, 故障诊断

Fig.1 Structural model of controllable hybrid magnetic coupler

Fig.2 Equivalent magnetic circuit diagram of main magnetic flux for controllable hybrid magnetic coupler

Tab.1 Model parameters of controllable hybrid magnetic coupler

Fig.3 Magnetic curves of sintered Nd-Fe-B (grade N35) under various demagnetization levels

Fig.4 Schematic diagram of magnetic density distribution and magnetic field strength of permanent magnets under various demagnetization levels

Fig.5 Gramian angular summation field encoding process

Tab.2 Current density dataset content

Fig.6 Schematic diagrams of different residual-block architectures

Fig.7 Schematic diagram of improved residual network architecture based on attention mechanism

Tab.3 Demagnetization fault diagnosis model parameters

Tab.4 Spatial residual module parameters

Fig.8 Accuracy of ten experiments for demagnetization fault diagnosis model

Tab.5 Fault diagnosis results for demagnetization fault diagnosis model %

Tab.6 Module ablation experiment results of demagnetization fault diagnosis model %

Fig.9 Performance-parameter comparison across modules of demagnetization fault diagnosis model

Fig.10 Experimental platform for controllable hybrid magnetic coupler

Tab.7 Actual measurement data of residual magnetic strength for permanent magnets

Tab.8 Analysis of current signal characteristics for magnetic coupler

Fig.11 Classification results of demagnetization fault diagnosis model on real data


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