基于EMD改进算法的爆破振动信号去噪

为了解决振动信号经验模态分解(empirical mode decomposition, EMD)滤波去噪效果不佳的问题，提出一种自适应性正交经验模态分解(principal empirical mode decomposition, PEMD)的信号去噪方法。该算法融合了EMD分解的自适应性和主成分分析(principal component analysis，PCA)的完全正交性特点，对信号EMD分解过程中产生的模态混叠现象进行消除，得到了最佳的去噪效果。分析表明:PEMD在仿真模拟试验中相比于传统EMD算法和集总经验模态分解(ensemble empirical mode decomposition, EEMD) 算法，信噪比分别提高了1.15 dB和0.38 dB，且均方根误差最小；频域上PEMD对仿真信号频率(30 Hz)识别的灵敏度最高，30 Hz之外的噪声滤除效果最好。在爆破振动试验中，PEMD和EEMD去除噪声毛刺的效果较为理想，且PEMD在0～300 Hz的中低频振动信号保存效果最好，300 Hz以上的高频噪声滤除效果最好。

Vibration signal de-noising based on improved EMD algorithm

In order to solve the problem of poor performance of EMD (empirical mode decomposition） filter de-noising for vibration signal, an adaptive orthogonal decomposition signal de-noising method PEMD (principal empirical mode decomposition) is proposed. This algorithm combines the self-adaptability of EMD decomposition and the complete orthogonality of principal component analysis (PCA), eliminates the phenomenon of mode aliasing in the process of signal EMD decomposition, and obtains the best de-noising effect. The results showed that compared with EMD and EEMD (ensemble empirical mode decomposition）, PEMD (principal component analysis) improved 1.15 dB and 0.38 dB respectively in the simulation test, and the root-mean-square error was the smallest. In frequency domain, PEMD has the highest sensitivity to the frequency of simulation signal (30 Hz), and the noise filtering effect is the best outside 30 Hz. In the blasting vibration test, PEMD and EEMD had better performance in removing burrs, and PEMD had the best performance in preserving medium and low frequency vibration signals at 0?300 Hz, and the best performance in filtering high frequency noises above 300 Hz.