基于TVF-EMD和TEO的滚动轴承微弱故障特征提取

针对旋转机械转子振动信号通常伴随着强噪声,难以提取其有效信息的问题,提出一种基于时变滤波经验模态分解(Time varying filtering based empirical mode decomposition,TVF-EMD)和Teager能量算子(Teager energy operator,TEO)相结合的故障特征提取方法。首先,用TVF-EMD方法自适应地分解轴承振动信号,以获得一组本征模态函数(Intrinsic mode functions,IMFs);然后,对分解结果进行峭度计算,并根据峭度最大准则选出峰度值最高的敏感分量;进而,利用Teager能量算子对选定的敏感分量进行解调处理,通过观察明显的周期性故障特征频率来实现轴承微弱故障特征提取。进行了仿真和实验,结果证明,该方法能有效实现轴承微弱故障的诊断。

Feature Extraction of Weak Fault of Rolling Bearing based on TVF-EMD and TEO

Aiming at the problem that the vibration signals for rotating machinery rotors are usually accompanied by strong noise,it is difficult to extract its effective information.A method of fault feature extraction based on time-varying filter empirical mode decomposition(TVF-EMD)and Teager energy operator(TEO)is proposed.Firstly,the TVF-EMD method is used to adaptively decompose the bearing vibration signal to obtain a set of intrinsic modal functions.Then,the kurtosis calculation is performed on the decomposition result,and the sensitive component is selected with the highest kurtosis value according to the maximum kurtosis criterion.Furthermore,the Teager energy operator is used to demodulate the selected sensitive components,and the weak fault feature extraction of the bearing is realized by observing the obvious periodic fault feature frequency.Simulations and experiments are carried out,and the results prove that this method can effectively diagnose the weak faults of bearings.