基于FFT和专家系统的BLDCM系统故障检测与识别

针对具有双通道结构的无刷直流电动机(BLDCM, Brushless DC Motor)容错系统的高可靠性要求和大范围调速特点,在不增加额外设备的条件下,通过分析两个通道中功率电路直流母线电流波形的特点,提出一种采用归一化快速傅里叶变换(FFT, Fast Fourier Transform)方法提取频率特征,再结合基于规则的专家系统进行故障检测与识别的方法,并通过实际电动机系统的试验验证了方法的正确性.试验结果表明:规一化FFT方法可以消除不同转速和不同负载对判断结果的影响;专家系统中阈值的选取可以有效避免实际应用中出现的噪声等因素的影响.算法复杂度低,可靠性高,易于应用,具有很强的实际操作性.

Fault detection and identification for dual-redundant BLDCM system using FFT algorithm and expert system approach

A scheme was proposed to detect and identify faults of a dual-redundant brushless DC motor (BLDCM) system, in which high reliability and wide speed range control were required. In order to eliminate the effects of different speeds and different loads from the frequency spectrum, both of the existing current signals in each channel were processed and normalized by fast Fourier transform (FFT) algorithm. And then, a rule-based expert system provided an effective identification approach for various motor, sensor and circuit faults. Experimental results show that main faults of this BLDCM system can be correctly identified even with unexpected noise. This approach needs no more extra devices, and is proved as fast, and easy to use for the proposed BLDCM system.