基于自适应共振解调的齿轮故障诊断方法

Fault Diagnosis Method of Gear based on Adaptive Demodulated Resonance

针对传统共振解调技术难以确定带通滤波器参数及受噪声影响较大,诊断效果不佳的问题,提出一种基于EEMD、信息熵和快速峭度图的自适应共振解调方法。该方法通过EEMD方法将故障信号分解成若干个固有模态分量,利用互相关系数方法自适应重构信号以突出故障特征信号,对信息熵最小的固有模态分量进行谱峭度分析,自适应确定带通滤波器的中心频率和带宽,最后对通过滤波的重构信号进行Hilbert包络谱分析。数字仿真实验和实测齿轮故障数据分析结果表明,该方法可以有效突出故障特征信号,证明了该方法的有效性。

In order to solve problems that the traditional demodulated resonance technology is difficult to determine the parameter of the band-pass filter and greatly influenced by noise,an adaptive demodulated resonance based on EEMD,information entropy and fast kurtogram is presented. Firstly,the original signal is decomposed into several IMFs,and the signal is reconstructed by using the correlation coefficient method to highlight the fault characteristic signal. Then,select the minimum information entropy of IMF to spectral kurtosis analysis and the central frequency and bandwidth of the band-pass filter is determined. Lastly,the filtered reconstructive signal is analyzed by using the Hilbert envelope spectrum analysis. The numerical simulation signal and measured gear fault signal test show that the fault characteristic is highlighted by using the proposed method and the validity is proved.