用于损伤状态识别的极值延拓EMD和LS-SVM算法  

Damage state identification algorithm based on extreme value extension empirical mode decomposition and least squares support vector machine

作　　者：刘聪[1] 钱坤[1] 焦准 丁奇 LIU Cong;QIAN Kun;JIAO Zhun;DING Qi(School of Aeronautics Maintenance Non-Commissioned Officers,Air Force Engineering University,Xinyang Henan 464000,China)

机构地区：[1]空军工程大学航空机务士官学校,河南信阳464000

出　　处：《计算机应用》2023年第S02期256-260,共5页journal of Computer Applications

摘　　要：针对机电系统损伤状态识别问题,提出一种基于极值延拓经验模态分解(EMD)和最小二乘支持向量机(LS-SVM)算法。首先,分析EMD算法的基本原理,针对端点效应利用多项式拟合极值延拓的算法改进设计方案,并利用标准化处理的特征向量设计程式;其次,考虑到机电系统损伤状态数据归属小样本特征,利用LS-SVM算法给出了状态识别的设计程式;最后,开展仿真验证实验。实验结果表明,采用所提算法的损伤状态识别方案,可以确保损伤状态识别的正确率超过96%,满足机电系统工程应用要求。An algorithm based on Empirical Mode Decomposition(EMD)and Least Squares Support Vector Machines(LS-SVM)was proposed for the damage state identification of electromechanical systems.Firstly,the basic principle of EMD was analyzed,the algorithm of fitting extreme value extension was utilized to to tackle the end effect,and the design program of feature vectors was utilized for standardization.Then,considering the damage state data of the electromechanical system was attributed to the characteristics of the small samples,the design program for the state recognition was given by LS-SVM algorithm.Finally,the simulation experiment was carried out.The experimental results show that by using the proposed algorithm,the damage state identification probability is more than 96%,which can meet the application requirements of electromechanical system engineering.

关 键 词：极值延拓 经验模态分解 最小二乘支持向量机 损伤状态 状态识别 

分 类 号：TN911.7[电子电信—通信与信息系统]