基于模糊支持向量机的感应电机故障诊断

Induction Motor Fault Diagnosis Based on Fuzzy Support Vector Machine

为了解决感应电机故障诊断复杂多样难于辨识的问题,有效提高分类的准确度,提出了一种采用模糊支持向量机构造感应电机多故障分类模型的方法。训练基于本文采用一种基于可控因子的隶属度设计新方法的模糊支持向量机作为故障模式分类器,对故障样本区别对待,有效消除噪声和野点对诊断结果的影响。将采集的电机定子电流信号进行小波分析,提取故障特征向量,作为支持相量机的输入参数,然后利用1对1策略构造转子多故障分类器,利用混合矩阵耦合策略对子分类器输出结果进行耦合,对电机故障进行识别,实验结果表明,该方法能够有效解决电机故障诊断中小样本集、非线性、高维数时的故障分类问题,提高电机故障诊断的准确性。

In order to solve the problem of correctly identifying fault classes in induction motor fault diagnosis and improve the accuracy of the classification, a novel fault diagnosis method of the classification model based on fuzzy support vector machine （FSVM）was proposed in this paper. The fault pattern classifier was trained, which the fuzzy membership of the feature vectors was computed by a controllable factors algorithm membership function to overcome the sensitivity to noise and outliers. After the stator current being sam pled, the fault feature was extracted from the sampling data through wavelet analysis and used as the input of the FSVM. A multi-class fault classifier was constructed to identify different faults, which was based on one to one strategy and mixed matrix combination. Experiment results show that fuzzy support vector machine （FSVM） has good performance for classification over non-linear and high dimension and small sample set. This method improves the accuracy in rotor fault diagnosis.