基于Autogram的齿轮断齿故障特征提取方法

针对复杂生产背景下产生的强噪声淹没齿轮有效故障特征信息的问题,利用Autogram方法对其进行特征提取。该方法利用最大重叠离散小波包变换,对齿轮断齿故障振动信号进行不同层数分解处理,每层得到若干个信号,被称为“node”。为了更加全面地描述故障特征信息,对每个node进行包络谱的3种无偏自相关谱峭度求取,以便选取合适node作为信号源进行下一步分析。最后,对该信号源引入阈值处理,以便加强频谱分析的全面性,实现对齿轮断齿故障特征信息的有效提取。通过对比分析仿真和实测齿轮故障振动信号,验证了该方法的有效性。

Feature Extraction of Gearbox Broken Tooth Fault Based on Method of Autogram

Fault feature information extraction of broken tooth is often contaminated out by strong production background noises.Aiming at the problem,Autogram method was applied to extract the feature information.In the method,maximum overlapping discrete wavelet packet transform was utilized to decompose a contaminated signal of broken tooth fault into several signals,and each signal was called“node”.In order to depict the feature information comprehensively,unbiased autocorrelation of squared envelope for each node was computed,then one node was selected as the data source to be analyzed.For the sake of effective feature information extraction,threshold processing was introduced,and spectrum analysis could be more comprehensive.The method is effective through comparing the simulation signal sand the test ones.