电机故障的小波MRA诊断技术

本文论述了电机运行过程中产生的故障现象，以及对电机故障信号的诊断方法。利用小波变换的多分辨率分析特性对电机故障信号进行多尺度分解，将信号分解到不同的频带上。通过对高频带分解系数的分析，可以提取出故障信号的特征，从而可以有针对性地对故障进行定位和诊断。仿真结果表明，从小波分解的高频细节系数中能够提取出精确的故障信号发生时刻以及频率，为故障的诊断提供了可靠的依据，表明了小波分析在电机故障诊断方面的广泛应用前景。

Technique of wavelet's MRA diagnose to fault of motor

The text introduced the fault phenomenon during the course of motor operation and its di agnosemethod. In the text, the MRA characteristic of wavelet transform was used to do multi-scale decomposition to fault signal of motor, and decomposed it into different frequency bands. From the analysis of decomposition coefficient in high frequency band, the feature of fault signal can be extracted. Accordingly, we can make pertinent diagnose and position fixing. It made clear from the result of simulation that the happening time and frequency of fault signal can be accurately extracted from high frequency detail coefficient decomposed by wavelet, which provide reliable proof to fault diagnose. The simulation result indicate that wavelet analysis has extensive application future in fault diagnose of motor.