VMD和奇异差分谱在齿轮早期故障诊断中的应用

Application of the VMD and Singular Differential Spectrum in Incipient Fault Diagnosis of Gear

针对齿轮故障振动信号的多分量、多频率调制特性且早期故障振动信号信噪比低,故障特征微弱难以提取的问题,提出了基于变分模态分解(Variational Mode Decomposition,VMD)和奇异值差分谱的故障诊断方法。首先对采集到的齿轮故障振动信号进行VMD分解,得到一系列窄带本征模态分量(band-limited intrinsic mode functions,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;然后根据相关系数准则,选取与原始信号相关系数较大的分量构建Hankel矩阵并进行奇异值分解,求取奇异值差分谱,从差分谱中确定重构信号的有效阶次对信号进行降噪处理;最后对降噪处理后的信号进行Hilbert包络谱分析,即可从中准确地识别出齿轮的故障特征频率。仿真信号和齿轮箱齿轮故障模拟实验结果表明,该方法能够有效地降低噪声的影响,准确地提取到齿轮微弱的故障特征信息。

Aiming at the multi component and multi frequency modulation characteristics of the gear fault vibration signal and the low signal to noise ratio of the early fault vibration signal,the fault feature is weak and difficult to extract,a gear fault feature extraction method based on Variational Mode Decomposition and singular value difference spectrum is proposed.Firstly,the vibration signal of the gear fault was decomposed by VMD to get a series of intrinsic mode functions band-limited.Due to the interference of the noise,it is difficult to make the correct judgment of fault in the spectrum of each mode component;Then,according to the correlation coefficient criterion,the singular value difference spectrum was obtained by the correlation coefficient between the original signal and the original signal,and the effective order of the reconstructed signal was determined to reduce the 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 bearing fault feature information.