电子测量与仪器学报

夏焰坤,王宛婷,黄鹏.基于PSO-VMD的永磁同步电机匝间短路振动信号故障特征提取研究[J].电子测量与仪器学报,2024,38(7):196-207

基于PSO-VMD的永磁同步电机匝间短路振动信号故障特征提取研究

Fault feature extraction of inter-turn short circuit vibration signalsin PMSM based on PSO-VMD

DOI：

英文关键词:permanent magnet synchronous motor vibration signal particle swarm optimization variational mode decomposition feature extraction

基金项目:四川省科技计划课题(2020YFG0184)项目资助

作者	单位
夏焰坤	西华大学电气与电子信息学院成都610039
王宛婷	西华大学电气与电子信息学院成都610039
黄鹏	西华大学电气与电子信息学院成都610039

Author	Institution
Xia Yankun	School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039,China
Wang Wanting	School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039,China
Huang Peng	School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039,China

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中文摘要:

在永磁同步电机（PMSM）的故障类型中，匝间短路（ITSC）故障较为常见，准确提取其故障特征具有重要意义。然而，在故障特征提取时容易出现模态混叠现象。为了准确提取出永磁同步电机（PMSM）出现匝间短路（ITSC）时振动信号的故障特征，提出了一种基于粒子群优化变分模态分解（PSO-VMD）的自适应非线性信号处理方法。首先，利用粒子群优化（PSO）寻找变分模态分解（VMD）的最优分解层数与二次惩罚因子，得到最优分解模型。其次，利用得到的最优分解模型对电机振动信号进行分解，得到一系列固有模态函数（IMF）。在此之后，计算分解得到的各IMF的方差贡献率（VCR），进一步计算累计方差贡献率（C-VCR），以筛选出包含故障特征信息的IMF。最后，应用希尔伯特变换（HT）对筛选出的IMF进行分析，并以三维时频图输出时间、瞬时频率与幅值，完成故障特征提取。为了验证所提方法的有效性和准确性，搭建了PMSM的ITSC实验平台，使用所提方法处理实测信号，结果表明，所提PSO-VMD方法有效改善了模态混叠现象，能更准确的提取故障特征，具有更好的工程适用性。

英文摘要:

In the fault types of permanent magnet synchronous motors (PMSM), inter-turn short circuit (ITSC) faults are relatively common, making the accurate extraction of fault features particularly significant. However, during fault feature extraction, modal mixing often occurs. In order to accurately extract the fault features of vibration signals in permanent magnet synchronous motor (PMSM) when inter-turn short circuit (ITSC) occurs, proposes an adaptive nonlinear signal processing method based on particle swarm optimized variational mode decomposition (PSO-VMD). Firstly, particle swarm optimization (PSO) is used to find the optimal number of decomposition layers and quadratic penalty factor for variational modal decomposition (VMD) to obtain the optimal decomposition model. Secondly, the optimal decomposition model is used to decompose the motor vibration signals to obtain a series of intrinsic mode functions (IMF). After that, the variance contribution rate (VCR) of each IMF is calculated, and the cumulative variance contribution rate (C-VCR) is further calculated to filter out the IMF that contain fault signature information. Finally, the filtered IMF are analyzed by applying the Hilbert transform (HT), and the three-dimensional time-frequency diagrams are used to output the time, the instantaneous frequency and the amplitude to complete the fault feature extraction. In order to verify the validity and accuracy of the proposed method, an experimental platform of the ITSC in PMSM was built, and the proposed method was used to process the measured signals. The experimental results show that the proposed PSO-VMD method effectively improves the phenomenon of modal mixing, can more accurately extract fault features, and has better engineering applicability.

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