电力系统保护与控制2026，Vol.54Issue(5)：176-187,12.DOI:10.19783/j.cnki.pspc.250817
基于多信号对称点模式和改进可变卷积残差网络的电机故障诊断
Motor fault diagnosis based on multi-signal symmetrical dot pattern and improved deformable convolutional residual network
摘要
Abstract
To address the difficulty in distinguishing between inter-turn short circuit faults and local demagnetization faults in permanent magnet synchronous motors(PMSMs),this paper proposes a method for fault diagnosis based on multi-signal symmetrical dot pattern(MSDP)and hybrid attention improved deformable convolutional residual network(HADRN).First,considering the different current fluctuation features of the two fault types,fault features are extracted from three-phase current signals using the improved two-dimensional symmetrical dot pattern analysis method.Second,an improved residual network model incorporating deformable convolution and hybrid attention modules is constructed to extract weak features and perform fault category mapping.Finally,the proposed algorithm is validated by collecting current signal data through simulation experiments.Comparative studies with various neural network algorithms demonstrate that the proposed method exhibits stronger feature extraction capability and higher diagnostic accuracy.关键词
永磁同步电机/匝间短路/局部退磁/多信号对称点模式/可变形卷积Key words
permanent magnet synchronous motor/inter-turn short circuit/local demagnetization/multi-signal symmetrical dot pattern/deformable convolutional引用本文复制引用
赵耀,赵彤彤,李东东,杨康..基于多信号对称点模式和改进可变卷积残差网络的电机故障诊断[J].电力系统保护与控制,2026,54(5):176-187,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.52377111). 国家自然科学基金项目资助(52377111) （No.52377111）
西藏自治区科技项目资助(XZ202401ZY0037) （XZ202401ZY0037）
教育部春晖计划合作科研项目资助(HZKY20220084) （HZKY20220084）
