中文摘要：

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。

英文摘要：

With the use of wavelet multiple-dimension decomposition,a new method to extract fault features from rotor-shaft-bearing systems is developed.First of all,the levels of wavelet decomposition are determined according to the fault＇s characteristic frequencies of the bearings,and the time domain characteristic parameters of the high-frequency signals in each level,which can reflect the fault characteristics,are calculated.Then,these parameters are compared with those in the normal operation conditions,and the fault and its cause can be identified.Finally,the multi-dimension decomposition is employed to improve the signal-to-noise ratio（SNR） of the fault signals.Since the characteristics in both the time domain and frequency domain of the fault are taken into account,the vectors made up of the multi-dimension characteristic parameters can be employed to identify different causes of the faults of the rotor,shaft and bearings simultaneously.The validity of this method is proved by several examples for fault diagnoses of bearings.