基于小波变换和聚类的BLDCM故障检测与识别

为了满足航空用机电作动器(EMA,Electro-Mechanical Actuator)高可靠性和大范围调速的要求,充分利用具有双通道容错结构的无刷直流电动机(BLDCM,Brushless DC Motor)系统特殊的结构和换相特点,通过分析两个通道中功率电路直流母线电流波形的突变特征,提出一种采用小波变换(WT,Wavelet Transform)与层次聚类算法(HCA,Hierarchical Clustering Algorithm)相结合的故障检测与诊断方法.并通过实际电机系统试验验证了方法的可行性与正确性.试验结果表明,这种方法对电机断相故障、逆变器功率管断路故障具有明显的检测与识别效果,而且不受转速、负载和噪声的影响.信号特征提取算法简单,故障识别方法可靠性高,无需额外设备,易于应用,具有很强的实际操作性.

Fault detection and identification for a dual-redundant brushless DC motor system using wavelet transform and hierarchical clustering algorithm

In order to implement the high reliability and wide-ranged speed control of an electro-mechanical actuator (EMA), a novel scheme was proposed to detect and identify faults of a dual-redundant brushless DC motor (BLDCM) system, which combines wavelet transform (WT) technique with hierarchical clustering algorithm (HCA) by testing the existing bus-current signals in each channel and using the features of the specific system structure and commutation process. Experimental results reveal that the motor open-phase faults and all kinds of inverter-transistor open-circuit faults of this BLDCM system can be correctly detected and identified with high robustness for the impact of wide-ranged motor speed, operating load, and even unexpected noise. This method is so sensitive to the sudden changes of the bus currents that it is very powerful for detecting those faults which lead to abnormal commutations. It needs no more extra devices, and is of low complexity, well fault-distinguish ability, high reliability and practical simplicity.