高阶统计量在故障诊断中的应用研究

The Study of the Application of the High-order Statistics to Bearings Fault Diagnosis

近年来,国内外有关轴承的研究表明,轴承的振动与噪声对机电设备的寿命有着重要的影响。其中,故障信号的提取是一个关键问题。在轴承、齿轮等零件的故障诊断中,常常应用包络分析提取冲击信号,但当信噪比很低及故障信息十分微弱时,包络分析往往也很难获得满意的结果。本文以轴承为对象,在提取包络信号的基础上,进一步计算包络信号的高阶统计量估计值,成功地将滚子故障和外圈故障等信号进行了分离,效果十分明显。

In the paper, The application of higher - order statistics to fault diagnosis of bearing is studied. The analytical signals are acquired by means of Hilbert transformation of the original signals, the enveloped signals are calculated from the analytical signals. The higher-order cumulates and moments of the enveloped signals are estimated. The results of the research show that the normal bearing signals, rolling element defect signals and outer race defect signals can be easily separated by using higher-order statistics as the signal features.