基于EMD和BCS的振动信号数据修复方法

Vibration signal repairing method based on EMD and BCS

为改善振动信号修复效果,引入贝叶斯压缩感知(BCS)理论,并提出一种基于经验模态分解(EMD)的贝叶斯压缩感知修复方法,以解决连续缺失信号修复问题。针对随机缺失信号,根据压缩感知修复原理,利用贝叶斯压缩感知算法进行修复;针对连续缺失信号,先对其进行经验模态分解,对分解得到的所有基本模式分量利用多任务贝叶斯压缩感知算法进行修复,最终将所有修复的基本模式分量累加得到整体信号。利用西储大学公开轴承数据进行修复实验,发现所提方法在时频域指标、误差、信噪比、峰值信噪比等方面均优于正交匹配追踪和正则化正交匹配追踪算法。从修复效果角度验证,发现该方法成功还原了外圈故障信号基本模式分量中的故障特征频率,达到了修复的目的。

In order to improve the repairing effect of vibration signal,Bayesian compressed sensing(BCS)theory is introduced,and a Bayesian compressed sensing-based repairing method with empirical mode decomposition(EMD)is proposed to solve the problem of continuously missing signal restoration.For the randomly missing signals,Bayesian compressed sensing algorithm is designed to repair them based on the principle of compressed sensing-based repairing.While for the continuously missing signals,empirical mode decomposition is firstly performed on them,and then all the basic mode components obtained by decomposition are repaired by multi-task Bayesian compressed sensing algorithm.Finally,all the repaired mode components are accumulated to get the whole signal.Experiments on open bearing data from Case Western Reserve University show that the proposed method is superior to orthogonal matching pursuit and regularized orthogonal matching pursuit in time-frequency domain,error,signal-to-noise ratio and peak signal-to-noise ratio.From the perspective of repairing effect,it is found that this method successfully restores the fault feature frequency in the basic mode components of the outer ring fault signal,and achieves the purpose of repairing.