Application of Kernel GDA to Performance Monitoring and Fault Diagnosis for Rotating Machinery  

作　　者：马思乐 张曦 邵惠鹤 

机构地区：[1]School of Control Science and Engineering,Shandong University [2]Guangdong Electric Power Research Institute [3]School of Electronic,Information and Electrical Engineering,Shanghai Jiaotong University

出　　处：《Journal of Donghua University(English Edition)》2010年第5期709-714,共6页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.60504033)

摘　　要：Faults in rotating machine are difficult to detect and identify,especially when the system is complex and nonlinear.In order to solve this problem,a novel performance monitoring and fault diagnosis method based on kernel generalized discriminant analysis(kernel GDA,KGDA)was proposed.Through KGDA,the data were mapped from the original space to the high-dimensional feature space.Then the statistic distance between normal data and test data was constructed to detect whether a fault was occurring.If a fault had occurred,similar analysis was used to identify the type of faults.The effectiveness of the proposed method was evaluated by simulation results of vibration signal fault dataset in the rotating machinery,which was scalable to different rotating machinery.Faults in rotating machine are difficult to detect and identify,especially when the system is complex and nonlinear.In order to solve this problem,a novel performance monitoring and fault diagnosis method based on kernel generalized discriminant analysis（kernel GDA,KGDA）was proposed.Through KGDA,the data were mapped from the original space to the high-dimensional feature space.Then the statistic distance between normal data and test data was constructed to detect whether a fault was occurring.If a fault had occurred,similar analysis was used to identify the type of faults.The effectiveness of the proposed method was evaluated by simulation results of vibration signal fault dataset in the rotating machinery,which was scalable to different rotating machinery.

关 键 词：kernel generalized discriminant analysis（KGDA） performance monitoring fault diagnosis rotating machinery 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]