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

Fault Diagnosis of Rolling Bearings Based on Multi-Layer Autocorrelation and Envelope Analysis

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。

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 pres- ence of early bearing faults because the modulation signal is weak and polluted by noise. The multi-layer au- tocorrelation 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 deter- mine 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 en- gineering application value.