基于线调频小波路径追踪的滚动轴承故障特征提取

发布时间：2018-05-11 17:27

本文选题：齿轮噪源 + 变转速　；参考：《振动与冲击》2017年13期

【摘要】：针对齿轮噪源以及变转速的工作条件双重干扰下的滚动轴承的故障诊断,提出了一种基于线调频小波路径追踪的滚动轴承故障特征提取方法。对混合信号进行Hilbert变换得到包络信号,并在满足采样定理的条件下对包络信号进行降采样;对降采样包络信号应用线调频小波路径追踪算法提取轴承的瞬时故障特征频率趋势线,再对轴承的瞬时故障特征频率趋势线和转速曲线进行等时间重采样,并求各时间点的计算瞬时故障特征系数;根据计算瞬时故障特征系数与轴承的外圈、内圈和滚动体的故障特征系数进行比较,完成故障诊断。通过处理仿真信号和实测信号证明了该方法在不对混合信号进行滤波和使用阶比分析的情况下,不仅能检测滚动轴承是否出现故障,而且能确定故障的发生位置。
[Abstract]:A fault feature extraction method for rolling bearing based on linear frequency modulation wavelet path tracing is proposed for the fault diagnosis of rolling bearing under double interference of gear noise source and variable rotational speed. The envelope signal is obtained by Hilbert transform to the mixed signal, and the envelope signal is de-sampled under the condition that the sampling theorem is satisfied. Linear frequency modulation wavelet path tracing algorithm is used to extract the instantaneous fault characteristic frequency trend line of bearing, and then the instantaneous fault characteristic frequency trend line and rotational speed curve of bearing are re-sampled in equal time. The instantaneous fault characteristic coefficient of each time point is calculated, and the fault diagnosis is completed by comparing the instantaneous fault characteristic coefficient with the fault characteristic coefficient of the outer ring, inner ring and rolling body of the bearing. It is proved that this method can not only detect the fault of rolling bearing but also determine the location of fault without filtering and using order ratio analysis of mixed signals by processing simulated and measured signals.
【作者单位】：北京交通大学机械与电子控制工程学院;
【基金】：国家自然科学基金资助项目(51275030)
【分类号】：TH133.33

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