基于SVM与D-S证据理论的异步电动机转子断条故障诊断方法
Fault Diagnosis Method of Rotor Broken Bar of Asynchronous Motor Based on SVM and D-S Evidence Theory
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摘要: 目前异步电动机转子断条故障诊断方法都是基于从定子电流中提取出特征频率来对转子状态作出诊断的方法,当异步电动机空载或轻载时,该特征频率易受基频泄露的影响而很难得到,同时该特征频率受转速波动影响很大,单纯根据该特征频率对转子状态作出判断缺乏准确性。针对上述问题,提出了一种运用SVM与D-S证据理论对异步电动机转子断条故障进行识别的诊断方法。该方法基于扩展Park法与FFT变换法,分别从定子电流信号和振动信号中提取转子断条故障的特征信息,利用SVM对异步电动机的状态进行模式识别,并将识别结果形成彼此独立的证据,而后根据D-S证据融合规则进行融合处理,从而实现对异步电动机转子断条故障的准确识别。实验结果表明,该方法可以对异步电动机转子断条故障作出准确判断。Abstract: At present, fault diagnosis method of rotor broken bar of asynchronous motor is based on the method of extracting characteristic frequency from stator current to make diagnosis for rotor state. When asynchronous motor runs with no-load or light load, the characteristic frequency is easy to be influenced by base-frequency leakage so as to be difficult to obtain, meanwhile speed fluctuation has great influence on the characteristic frequency, so it is lack of accuracy according to the characteristic frequency to judge rotor state. To solve above problem, the paper proposed a diagnosis method of using SVM and D-S evidence theory to identify fault of rotor broken bar of asynchronous motor. The method separately extracts characteristic informations of fault of rotor broken bar from current signal and vibration signal of stator on the basis of the extended Park method and the FFT transformation method, uses SVM to make pattern recognition to the status of asynchronous motor and form independent evidences for recognition results and then makes fusion process according to D-S evidence fusion rules, so as to realize accurate recognition of fault of rotor broken bar. The experiment result showed that the method can make accurate judgment for fault of rotor broken bar of asynchronous motor.