基于多超球SVM的电机转子断条故障诊断  

Multiple hyper-spheres support vector machine for motor broken rotor bar fault diagnosis

作　　者：唐国锋[1] Tang Guofeng(Yantai Automobile Engineering Professional College,Linyi 264000,China)

机构地区：[1]烟台汽车工程职业学院,临沂264000

出　　处：《电子测量技术》2021年第5期1-5,共5页Electronic Measurement Technology

摘　　要：针对电机转子断条故障诊断问题,设计了一个故障诊断模型。将电机转子断条故障诊断视为多分类问题,提出了一种多超球支持向量机(MHSVM)故障诊断模型。MHSVM是通过利用支持向量数据描述(SVDD),结合二叉树结构的方法,构造的一种多分类模型。为验证所提出算法的有效性,将MHSVM与支持向量机(SVM)和神经网络算法(BP)进行了对比实验。结果为提出的诊断模型能够实现94.92%的诊断率,而SVM模型和BP模型分别实现92.06%和89.06%的诊断率。提出的诊断模型的诊断率是3个模型中最高的。实验结果表明,基于SVM的故障诊断模型的诊断效果优于基于BP算法的诊断效果。同时,提出的MHSVM对转子断条故障具有最好的诊断效果,这证明了所提出模型的适用性和有效性。Aiming at the problem of motor broken rotor bar fault diagnosis, a fault diagnosis model is designed. This paper transforms the rotor broken bar fault diagnosis problem into a classification problem, thus a fault diagnosis model based on multiple hyper-spheres support vector machine(MHSVM) is proposed. MHSVM is a multi-class classification model, which is constructed by using support vector data description(SVDD) and binary tree structure. To verify the effectiveness of the proposed algorithm, MHSVM is compared with support vector machine(SVM) and neural network algorithm(BP). The results show that the diagnosis rate of MHSVM is 94.92%, while the diagnosis rate of SVM model and BP model is 92.06% and 89.06% respectively. As can be seen, the diagnosis rate of the proposed model is the highest among the three models. The experimental results show that the effect of the fault diagnosis models based on SVM is better than that of BP. Meanwhile, MHSVM has the best effect for broken rotor bar fault diagnosis, which proves the applicability and effectiveness of the MHSVM.

关 键 词：电动机 转子断条 故障诊断 模式分类 支持向量机 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]