基于EEMD-KNN的机车牵引座状态识别方法  

EEMD-KNN State Recognition Method for Locomotive Traction Seat

作　　者：谢锋云 姜永奇 冯春雨 王二化[2] 刘翊 XIE Fengyun;JIANG Yongqi;FENG Chunyu;WANG Erhua;LIU Yi(School of Mechatronics&Vehicle Engineering,East China Jiaotong University,Nanchang Jiangxi 330013,China;Changzhou City Lab of Intelligent Technology for Advanced Manufacturing Equipment,Changzhou College of Information Technology,Changzhou Jiangsu 213164,China;National Innovation Center of Advanced Rail Transit Equipment,Zhuzhou Hunan 412000,China)

机构地区：[1]华东交通大学机电与车辆工程学院,江西南昌330013 [2]常州信息职业技术学院常州市高端制造装备智能化技术重点实验室,江苏常州213164 [3]国家先进轨道交通装备创新中心,湖南株洲412000

出　　处：《机床与液压》2022年第13期32-36,共5页Machine Tool & Hydraulics

基　　金：国家自然科学基金地区科学基金项目(51565015);江西省教育厅资助项目(GJJ180301)。

摘　　要：机车牵引座的安全可靠性对机车运行的安全性起到重要作用,因此对机车牵引座状态识别研究具有重要的经济价值和社会意义。为了开展牵引座的正常、小裂纹、大裂纹等3种状态识别研究,搭建机车牵引座的模拟实验台,用加速度传感器采集不同状态的振动信号,对原始数据进行时域特征提取,并进行集合经验模态分解(EEMD)提取时频域特征,采用K邻近算法(KNN)进行牵引座状态识别。实验结果表明:基于EEMD-KNN模式识别方法能对机车牵引座状态进行有效识别,识别率达到83.3%;而且添加时域特征之后的识别率更高一些,识别率达到90.5%。The safety and reliability of the traction seat plays an important role in the safety of locomotive operation,the research on the status identification of locomotive traction seat has important economic value and social significance.In order to carry out the research on the identification of the normal,small crack and large crack states of the traction seat,a simulation experimental platform of the locomotive traction seat was built.The acceleration sensor was used to collect the vibration signals of different states,and the time-domain features of the original data were extracted.The time-frequency features were extracted by ensemble empirical mode decomposition(EEMD),and the K-nearest neighbor algorithm(KNN) was used for the status identification of the traction seat.The experimental results show that the EEMD-KNN pattern recognition method can effectively recognize the locomotive traction seat state,and the rate of recognitionreaches 83.3%.After adding time domain features the recognition rate is higher,which reaches 90.5%.

关 键 词：牵引座 特征提取 集合经验模态分解 K邻近算法 状态识别 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程] TH133[自动化与计算机技术—控制科学与工程]