基于提高变分模态分解的齿轮箱复合故障特征提取

Compound Fault Feature Extraction of Gearbox based on Improve Variational Mode Decomposition

在实际工况下,齿轮箱故障经常包含多个故障信息,而弱故障信号相比强故障信号和噪声属于微弱信号,故复合故障中的弱故障信号提取一直是旋转机械故障诊断的一大难点。基于上述问题,考虑到MED(Minimum Entropy Deconvolution)具有强降噪性能、VMD(Variational Mode Decomposition)分解出的本征模态函数在强噪声环境中会失真、VMD分解精度由惩罚因子α和分解次数k决定,提出了一种基于MED-VMD的滚动轴承微弱故障提取方法。首先对原信号用MED降噪;进一步设置初始参数α和k,对降噪后的信号通过VMD分解,计算相邻本征模态函数的相关系数,确定最佳惩罚因子α和分解次数k;最后对本征模态函数进行包络谱分析,提取了齿轮箱中轴承的微弱故障信息。通过仿真信号和实测数据均验证了所提方法的有效性,给强噪声环境的复合故障的微弱故障特征提取提供一种新思路。

In the practical condition,the fault signal of gearbox often contain multiple fault information,but the weak fault signal belongs to weak signal compared with strong fault signal and noise,therefore,the extraction of weak fault signal in complex fault is always a difficult point in rotating machinery fault diagnosis.Based on the problems above,considering that MED（ Minimum Entropy Deconvolution） has strong reduction performance of noise,the intrinsic mode function decomposed by VMD（ Variational Mode Decomposition） is distorted in strong noise environment. The VMD decomposition accuracy is determined by penalty factor α and decomposition times k. A weak fault extraction method based on MED-VMD is proposed. Firstly,the noise reduction of original signal is carried out by MED,further,the initial parameters α and k are set,the noise reduction signal is decomposed through the VMD,the correlation coefficient of the adjacent intrinsic mode function are calculated to determine the best penalty factor α and decomposition times k. Finally,the weak fault information of bearing in gearbox is extracted by the envelope spectrum analysis of the intrinsic modal function.The validity of the proposed method is verified by the simulation signal and the measured data. A new idea for weak fault feature extraction in complex fault in strong noise environment is presented.