盲信号处理在感应电动机复合故障诊断中的应用研究

A composite fault diagnosis method of induction motor based on blind signal processing

感应电动机定子短路和轴承故障一起发生时,由于故障机理不同,通常需要同时采集定子电流和振动信号才能进行有效诊断。研究了一种通过振动信号同时诊断两种故障的方法,以两种故障一起发生时的低频振动信号为研究对象,采用最小均方盲提取算法对故障信号进行提取,得到了信噪比提高的故障信号,做频谱分析后两种故障的特征频谱能够很好地分辨。通过实验平台模拟了定子相间短路和轴承外滚道缺损的复合故障,诊断结果表明该方法能够提取故障信号,减少噪声干扰,实现了利用振动信号对感应电动机复合故障的诊断。

When the stator winding fault and the roller bearing fault occur simultaneously, only the simultaneous acquisition of the stator current signal and the vibration signal is effective for the diagnosis of the composite faults because the mechanism of fault is different. A composite fault diagnosis method based on blind Least Mean Square is proposed to extract the fault signal from the low-frequency vibration signal of the induction motor. Utilizing this method the fault signal is denoised and the characteristic components for fault detection can be identified effectively. The stator phase short circuit fault and the outer raceway fault are simulated in experiment platform. Experimental results show that the proposed method is effective to extract the fault signal and improve the signal-to-noise ratio, and the composite fault diagnosis by using the vibration signal is feasible. This work is supported by National Natural Science Foundation of China（No.50677069）.