基于LMD和平滑Teager能量算子解调的电机滚动轴承故障特征提取

作为电机转子的支撑元件,电机轴承故障在电机故障中占有很大比例。针对电机滚动轴承振动信号多分量调幅调频的特点,提出一种基于局域均值分解(LMD)和平滑Teager能量算子的电机轴承故障特征提取方法。该方法首先通过LMD将多分量调制信号分解为若干个单分量调制信号,再运用平滑Teager能量算子对包含主要故障信息的分量进行解调,从而准确地分析出轴承的故障特征。模拟和实例证明了该方法的有效性。

Fault Feature Extraction of Motor Rolling Bearing based on LMD and Smoothed Teager Energy Operator Demodulating

As the sustaining element of rotor in motor,bearing fault owns great proportion in motor common faults.In allusion to the character of multi-component and AF/FM in motor rolling bearing vibrating signal,a process of motor bearing fault feature extraction based on local mean decomposition(LMD) and smoothed Teager energy operator is proposed.Multi-component AF-FM signal is decomposed into some single component AF-FM signal at first,then the component including main fault character is demodulated by means of smooth Teager energy operator,the fault feature of motor is analyzed accurately thereby.The effectiveness of the means is proved through simulation and example.