Fault Pattern Recognition using Dynamic Independent Component based Sparse Kernel Classifier
In order to diagnose fault source effectively, this paper proposed a novel fault pattern recognition method called dynamic independent component based sparse kernel classifier (DICSKC). In the proposed method, fault pattern recognition is viewed as a classification problem and kernel trick is applied to construct nonlinear classifier for each fault scene. To improve classification performance, dynamic independent component analysis is used to extract data features which substitute for original measured variables as the input of classifier. For obtaining a sparse classifier to reduce the computation complexity, an orthogonal forward subset selection procedure is utilized to minimize the leave one out classification error. Simulation on the Tennessee Eastman benchmark process shows that the proposed method has a good fault pattern recognition performance.
fault pattern recognition dynamic independent component analysis sparse kernel classifier
DENG Xiaogang TIAN Xuemin
College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266555
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
5322-5327
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）