小波包频带-能量重构的异步电动机转子断条故障诊断

Fault Diagnosis of Asynchronous Motor Rotor Broken-Bar of Wavelet Packets Frequency Band-Energy Reconstruction

基于定子电流信号对异步电动机进行故障诊断时,其转子断条故障特征频率分量常被电流的基频分量淹没。尽管小波变换具有频率越高,相应的时间分辨率也越高的特点,但频率域上的分辨率却降低成了它的弱点。而小波包技术能同时对上一层的低频部分和高频部分进行细分,并根据被分析信号的特征,自适应的选择相应的频段,使之与信号频谱相匹配,从而提高时域分辨率。用小波包分析法对所采集信号处理,可作为电动机故障诊断输入特征向量的分解后的频段能量特征值。实验表明,采用小波包技术可快速、准确地诊断出电动机故障,其效果良好,也为电动机故障在线实时诊断提供了理论依据。

When doing the motor fault diagnosis, the rotor broken-bar fault characteristic frequency is often flooded by the fundamental component. Although the Wavelet transform has the strength of high resolution, its low resolution in the frequency domain has become its weakness. The wavelet packet can both study the low and high frequency component and it can choose the proper frequency stage according to the real situation. Uses the wavelet packet to deal with the collected current data and gets the motor fault characteristics quantity successfully. The wavelet packet analysis method processes the collected signals, which can be the frequency energy feature value that be separated after the motor fault diagnosis input vector. The experiments show that by using this way, gets the motor fault characteristic quantity much better than the FFT.