基于谱峭度和AR模型的滚动轴承故障诊断

提出基于自回归(Autoregressive,简称AR)预测滤波的谱峭度分析方法,将其应用于滚动轴承的早期故障诊断。通过结合AR预测滤波器提取轴承故障信号共振衰减成分的特性,利用谱峭度方法对AR预测滤波器滤波后的信号进行处理,实现了滚动轴承早期微弱故障的识别。通过滚动轴承的疲劳全寿命加速实验获取滚动轴承的自然故障信号,克服了传统轴承故障诊断人工加工故障的不足。通过试验数据的分析表明,基于AR预测滤波的谱峭度方法不仅能够消除干扰成分提取故障特征,还能增加谱峭度方法的稳定性。

Spectral Kurtosis and AR Model Based Method for Fault Diagnosis of Rolling Bearings

AR predict filer based Spectral kurtosis method is proposed to research the early fault diagnosis of rolling bearings. Combining the characteristic of extracting the resonance damping component of bearing fault signal which an Autoregressive (AR) predict filer holds, the early weak fault of rolling bearings are successfully detected after that the filtered signal is processed by spectrum kurtosis method. An accelerated life test of rolling bearing is established to obtain the nature fault signal of rolling bearings which overcomes the insufficient of traditional artificial fault process. The result of experiment data analysis shows that the proposed method can not only extract the fault characteristic by eliminating the interference component, but also can rise the stability of spectral kurtosis analysis method.