基于参数辨识法的异步电动机故障诊断

Fault Diagnosis of Asynchronous Motor Based on Parameter Identification

参数辨识法是异步电动机在线故障诊断的有效方法。根据定子电压、电流数据,采用递推最小二乘算法,计算电动机参数,在线辨识电动机故障,并讨论渐消记忆递推最小二乘法中遗忘因子对参数辨识的影响。鉴于模型的复杂性,用灰色系统观点线性化异步电动机模型,使参数快速收敛,但参数缺乏明确的物理含义。从电动机内部电磁关系入手,建立鼠笼异步电动机旋转坐标系电磁模型,消除磁链变量,避免磁链观测,达到故障诊断的目的。

The parameter identification is an effective method of on-line fault diagnosis for asynchronous motor. Using recursive least square （RLS） algorithm, the motor parameters are calculated from measuring data of stator voltage and current, and used for on-line fault diagnosis. The influence of the forgetting factor in fading memory RLS on the parameter identification has been discussed. Given the complexity of the model, the model of asynchronous motor is linear in the view of gray system. The simplified model makes the motor parameter converging fastly, but they are lacking for clear physical meanings. Then the electromagnetic model of asynchronous cage motor in revolving coordinate is established based on its internal electromagnetic relations to avoid the complexity of magnetic observation by eliminating the magnetic variations and to attain the goal of fault diagnosis.