基于VMD和最小熵反褶积的齿轮早期故障诊断

针对强噪声环境下齿轮早期故障特征信号微弱,故障特征信息难以提取的问题,提出了变分模态分解(Varia-tional Mode Decomposition,VMD)和最小熵反褶积(Minimum Entropy Deconvolution,MED)的诊断方法。首先,利用VMD对采集到的齿轮故障振动信号进行自适应分解,得到一系列窄带本征模态分量(band-limited intrinsic mode funct-ions,BLIMFS),由于噪声的干扰,从各个模态分量的频谱中很难对故障做出正确的判断;然后依据相关系数准则,选取包含故障特征信息较丰富的分量进行MED滤波处理以消除噪声影响,凸显故障特征信息。最后对降噪后的信号进行Hilbert包络解调分析,即可从包络谱中准确地识别齿轮故障特征频率。通过仿真信号和齿轮箱实验数据对所提方法进行了验证,结果表明,该方法能够有效地降低噪声的影响,准确地提取齿轮早期故障信号中微弱的特征信息。

Early Fault Diagnosis of Gear Based on VMD and Minimum Entropy Deconvolution

Aiming at the problem that the fault characteristic signal is weak and the fault feature information is difficult to extract in the strong noise environment,a fault diagnosis method based on variational mode decomposition and minimum entropy deconvolution was proposed.Firstly,the vibration signal of the bearing fault was decomposed by VMD to get a series of intrinsic mode functions band-limited.Due to the interference of the noise,it was difficult to make the correct judgment of fault in the spectrum of each mode component;Then,according to the correlation coefficient criterion,the minimum entropy deconvolution filter was selected to eliminate the influence of noise.Finally,the processed signal was analyzed by Hilbert envelope.The fault characteristic frequency can be extracted accurately from the envelope spectrum.Through the analysis of Simulation signal and experimental data of gear fault,the results show that the method can effectively reduce the influence of the noise,and accurately realize the extraction of gear fault feature information.