动态称量经验模态分解数据处理方法

为消除动态称量信号中的各种噪声,研究动态称量经验模态分解数据处理方法.针对经验模态分解筛分过程中数据序列的两端常处于非极值状态,而导致边缘效应,以及现有抑制边缘效应方法效率低、对数据量要求大的不足,提出一种新的数据延拓方法次端点镜像延拓法来抑制边缘效应.通过对定量加料动态称量经验模态分解试验,结果表明新的抑制边缘效应方法可以获得较高的称量精度和效率,对称量信号分解的最大误差在±0.8%以内.

Data processing method of empirical mode decomposition on dynamic weighting

To eliminate noises involved in dynamic weighting signal,empirical mode decomposition(EMD) is applied.For the end points of data sequence being usually not the extremes,the upper and lower envelopes swing sharply there,which cause end effect and result in low precision of the sifting results.A new method called the second extremes mirror method is proposed,considering the given anti-end-effect method with low precision and strict requirement for data volume.The improved EMD method is adopted in dynamic weighting trial.The results indicate that the precision of this new method is very high,the new anti-end-effect method is the most effective one among the given methods,and the error of weighting can be controlled below 0.8%.