BOOSTING ALGORITHM APPLICATION IN AERO-ENGINE FAULT DIAGNOSIS

摘要：

This study presents Support Vector Machine (SVM) classifier for aero-engine fault diagnosis, and establishes an efficient integrated SVM classifier by Boosting algorithm. It conducts fault feature compression and extraction on real-time aero-engine data with principal component analysis (PCA) and rough set theory (RST), and carries out classification and identification of engine failure data after dimension degradation by the established efficient classifier. Experimental results verify the effective performance of the proposed model for aero-engine fault diagnosis.

关键词： support vector machine (SVM) boosting algorithm principal component analysis (PCA) rough set theory (RST)

作者: Chaoying Sun Lu Liu Chuanwu Liu Bo Liu

作者单位: School of Economics and Management, Beihang University, Beijing 100191, China, Beijing Aeronautical School of Economics and Management, Beihang University, Beijing 100191, China Beijing Aeronautical Technology Research Center, Beijing 100085, China

会议类型: 国际会议

会议名称: The Tneth International Conference on Industrial Management(第十届工业管理国际会议 ICIM 2010)

会议地点: 北京

会议语种:英文

页码: 583-586

在线出版日期: 2010-09-16（万方平台首次上网日期，不代表论文的发表时间）