基于多重自相关与包络谱分析的滚动轴承故障诊断方法研究

摘要

The vibration signal measured by the sensor is non-stationary and multi-component modulation signal when rolling bearings run in a fault condition. It is difficult to identify the characteristic in the presence of early bearing faults because the modulation signal is weak and polluted by noise. The multi-layer autocorrelation is used to eliminate noise while extracting signal cycle modulation component,and the envelope demodulation method based on Hilbert transform is used to obtain fault characteristic frequency and determine the type of bearing failure.The results show that the method of multi-layer autocorrelation and envelope demodulation can extract the characteristic frequency of rolling bearing more accurately, and has certain engineering application value.%滚动轴承在故障状态运行时,传感器测得的振动信号为非平稳、多分量的调制信号.在故障出现早期,由于调制信号微弱且含有噪声,导致故障特征难以识别,采用多重自相关消除噪声干扰,提取信号中的周期调制成分,然后利用Hilbert 变换的包络解调方法获取故障特征频率,从而判断出轴承故障类型.实验结果表明,采用多重自相关与包络谱解调相结合的方法,能较准确的提取滚动轴承故障特征频率,具有一定的工程应用价值.