小波分析在转子系统多故障诊断中的应用研究

提出小波多尺度分解的时域参数向量用于转子、滚动轴承系统多类故障同时产生情况下特征提取的新方法,该方法首先根据轴承的故障特征频率确定小波分解的层数,对分解后的各层高频信号计算其能反映故障特征的时域特征参数,再将包含故障特征频率的各尺度时域参数与转子、轴承正常运转时的时域参数相对比,从而判断转子、轴承故障及其产生故障的原因。通过多尺度分解可明显地提高故障信号所在尺度的信噪比,由于既考虑了故障的频域特征也参照了故障的时域特征,通过多尺度特征参数构成的向量可同时诊断出转子、轴承的不同故障原因,通过仿真和故障轴承的实例分析验证该方法的有效性。

Application of Wavelet Analysis to Multiple Faults Diagnosis in Rotor-Shaft-Bearing Systems

This paper developed a new method to extract fault feature from the system, where multiple faults may occur simultaneously in the rotor shaft and bearing, with wavelet multiple-dimensioned decomposition. First, the levels of wavelet decomposition are determined by the fault feature frequency, and the time domain characteristic parameters of HF detail signals, which could show the fault characteristic, are calculated. Then, these parameters are compared to the normal ones, by which the fault and its source can be identified. Multiple-dimensioned decomposition can improve the signal-to-noise ratio (SNR) of fault signals obviously. Because both of the time and frequency domain information of the fault are taken into account, with the distribution of vectors made up of characteristic parameter, different source of fault can be diagnosed at the same time. The validity of this method was proved by massive simulations and fault bearing examples.