基于多通道信号二维递归融合和ECA-ConvNeXt的永磁同步电机高阻接触故障诊断  

High-Resistance Connection Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Two-Dimensional Recursive Fusion of Multi-Channel Signals and ECA-ConvNeXt

作　　者：丁伟 宋俊材 陆思良[1] 王骁贤 Ding Wei;Song Juncai;Lu Siliang;Wang Xiaoxian(School of Electrical Engineering and Automation,Anhui University,Hefei 230601,China;School of Internet,Anhui University,Hefei 230039,China;School of Electronic and Information Engineering,Anhui University,Hefei 230601,China)

机构地区：[1]安徽大学电气工程与自动化学院,合肥230601 [2]安徽大学互联网学院,合肥230039 [3]安徽大学电子信息工程学院,合肥230601

出　　处：《电工技术学报》2024年第20期6397-6408,共12页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金项目(52207036,62203010,52075002);安徽省自然科学基金项目(22080850E167);安徽省教育厅自然科学重点项目(KJ2021A0018)资助。

摘　　要：该文提出一种基于多通道信号二维递归融合和高效通道注意力机制新一代卷积神经网络(ECA-ConvNeXt)相结合的方法,以解决永磁同步电机高阻接触故障精细定量化诊断识别的问题。首先,建立永磁同步电机仿真模型获取三相电流信号作为有效故障信号;其次,引入递归图,将三相电流信号分别映射为二维图像并进行多通道融合,以提高故障特征信息的丰富性并消除人工特征提取的影响,实现故障特征的增强显示;然后,通过在ConvNeXt中引入高效通道注意力模块,提升了网络在通道维度上的适应性,得到ECA-ConvNeXt以实现永磁同步电机故障位置类型和严重程度的精确诊断分类,分类精度达到99.18%,并通过带噪声数据验证了该方法的鲁棒性;最后,搭建了样机实验平台,验证所提方法识别精度高达97.35%,能够准确识别永磁同步电机高阻接触故障位置和严重程度。The measurement error of traditional high-resistance connection(HRC)fault diagnosis method for permanent magnet synchronous motor(PMSM)is typically high,and it is difficult to evaluate HRC fault comprehensively and quantitatively.Modern diagnosticmethods of HRC fault are realized by the symmetry monitoring system of injection signal method and zero-sequence component detection method.However,these methods based on signal injection strategy are invasive diagnostics,which may have an impact on the original drive system.The method based on zero sequence component must have neutral points and additional resistance network,and the application scenario is limited.To solve the above problems,this paper proposes a data-driven method to realize the HRC fault diagnosis of PMSM,and establishes the fault mode of the system through a large number of existing historical data,without the need for a prior known model or signal mode.Firstly,a simulated PMSM model is established to obtain the three-phase current signal as an effective fault signal.Secondly,the recursive graph method is innovatively introduced to map the three-phase current signals into two-dimensional recursive images and perform multi-channel fusion.The fused images contain more feature information and avoid the influence of artificial feature extraction during fault signal processing.Then,ConvNeXt is introduced to effectively solve the gradient dispersion problem of the existing classification model,and a new ECA-ConvNeXt classification model is obtained by integrating the attention mechanism,which improves the adaptability of the network in the channel dimension and enhances the generalization ability of the model.The simulation results show that compared with the basic ConvNeXt,the accuracy of ECA-ConvNeXt on the test set sample is improved from 98.05%to 99.18%.Compared with models such as GoogLeNet,ResNet and Swin Transformer,the ECA-ConvNeXt proposed in this paper also has higher performance indexes and more advantages in identifying early HRC faults.To veri

关 键 词：永磁同步电机 高阻接触故障 递归图 卷积神经网络 注意力机制 

分 类 号：TM351[电气工程—电机]