基于信号分析的异步电动机的转子断条与偏心故障诊断方法中，常用传统的电机电流信号特征分析（MCSA）方法。由于采样频率偏低、强大的基波旁瓣效应等因素的影响，会导致特征频率成分被淹没、难以量化故障程度等问题。因此，提出了一种基于自适应粒子群优化逐序支持向量机（APSO-SSVM）的异步电动机故障诊断方法。首先，利用经验小波变换（EWT）对原始信号进行滤波；然后，对滤波后的信号进行特征提取并输入到SSVM诊断模型中；最后，通过APSO算法确定各次序下SVM模型的最佳超参数，从而实现转子断条数量的精确故障诊断。

The traditional method of motor current signal characteristic analysis (MCSA) is commonly used in the fault diagnosis of rotor broken bar and eccentricity of asynchronous motor based on signal analysis. Because of low sampling frequency, strong base bourbon effect and other factors will lead to the drowning of characteristic frequency components, difficult to quantify the fault degree and other problems. Therefore, a fault diagnosis method of asynchronous motor based on adaptive particle swarm optimization sequential support vector machine (APSO-SSVM) is proposed. Firstly, empirical wavelet transform (EWT) is used to filter the original signal; then, the feature extraction of the filtered signal is carried out and input into the SSVM diagnosis model; finally, the APSO algorithm is used to determine the optimal hyperparameters of the SVM model in each order, so as to achieve accurate fault diagnosis of the number of broken rotor bars.

TM343

国家自然科学基金（62273264）；国家自然科学基金青年项目（51907144）