首页 | 本学科首页   官方微博 | 高级检索  
     检索      

基于SVM和广义粗糙度特征的航空发动机振动故障诊断方法
引用本文:吴娅辉,李新良,洪宝林,张大治.基于SVM和广义粗糙度特征的航空发动机振动故障诊断方法[J].航空动力学报,2011,26(11):2445-2449.
作者姓名:吴娅辉  李新良  洪宝林  张大治
作者单位:中国航空工业集团公司北京长城计量测试技术研究所计量与校准技术重点实验室,北京,100095
基金项目:航空科学基金(20105644004)
摘    要:通过对航空发动机振动信号进行小波分解,依据多尺度空间局部能量分布和粗糙性提取基于子带信号能量加权广义粗糙度特征实现对振动情况的描述.然后将上述特征送入支持向量机(support vectormachine,简称SVM)分类器进行训练,根据分类器的输出结果判断航空发动机的工作状态和故障类型.通过对实测航空发动机试车时得到...

关 键 词:航空发动机  振动分析  广义粗糙度  支持向量机(SVM)  小波  故障诊断
收稿时间:4/8/2011 12:00:00 AM
修稿时间:2011/9/20 0:00:00

Research on aeroengine vibration fault diagnosis based on support vector machine and generalized roughness feature
WU Ya-hui,LI Xin-liang,HONG Bao-lin and ZHANG Da-zhi.Research on aeroengine vibration fault diagnosis based on support vector machine and generalized roughness feature[J].Journal of Aerospace Power,2011,26(11):2445-2449.
Authors:WU Ya-hui  LI Xin-liang  HONG Bao-lin and ZHANG Da-zhi
Institution:WU Ya-hui,LI Xin-liang,HONG Bao-lin,ZHANG Da-zhi (Key Laboratory of Science and Technology on Metrology and Calibration,Changcheng Institute of Metrology and Measurement,Aviation Industry Corporation of China,Beijing 100095,China)
Abstract:The signal was decomposed based on wavelet transform and the generalized roughness vector of the signal was formed by making use of the local energy distributions and the roughness of the sub-band signal.Then the desired parameters serve as the fault characteristic vectors to be input to the support vector machine classifier and the work conditions and fault patterns were identified by the output of the classifier.The analysis results from the aeroengine vibration signals show that the fault diagnosis method can classify working conditions and fault patterns effectively.
Keywords:aeroengine  vibration analysis  generalized roughness vector
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《航空动力学报》浏览原始摘要信息
点击此处可从《航空动力学报》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号