针对现有旋转机械故障诊断模式的缺点,提出通过拟合故障振动信号模型实现滚动轴承故障诊断的方法.首先建立了滚动轴承故障振动信号模型,对原始振动信号做EMD(empirical mode decomposition)分解,并对包含有故障调制信息的IMF(intrinsic mode function)分量做信号重构,最后采用遗传算法对重构信号和故障信号模型做数据拟合,根据拟合结果可知损伤点所在部位和损伤程度.通过在风力发电机组齿轮箱高速端滚动轴承故障诊断中的应用,验证了方法的有效性和实用性.%Aiming at the shortcomings of the existing fault diagnosis modes for rotary machine,a method for fault diagnosis of rolling bearings based on the fault vibration signal model fitting is proposed.The fault vibration signal model is presented firstly,and the original vibration signals are decomposed by EMD (Empirical Mode Decomposition),then the signals of IMFs containing fault modulation information are reconstructed.Finally,the data fitting is carried out in term of both the reconstructed signals and the model by Genetic Algorithm.According to the fitting results,the location and damage level are obtained.The effectiveness and availability of the method are proved by the application of fault diagnosis of the rolling bearings at high speed side of wind turbine gearbox.
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