A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is：...A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is： First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator＇s fault data set prove that the proposed method is effective.展开更多
Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal com...Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal component analysis (NLPCA) using radial basis function (RBF) neural network is developed in this paper. The orthogonal least squares (OLS) algorithm is used to train the RBF neural network. This method improves the training speed and prevents it from being trapped in local optimization. Results of two experiments show that this NLPCA method can effectively capture nonlinear correlation of nonlinear complex data, and improve the precision of the classification and the prediction.展开更多
We compared nonlinear principal component analysis(NLPCA) with linear principal component analysis(LPCA) with the data of sea surface wind anomalies(SWA),surface height anomalies(SSHA),and sea surface temperature anom...We compared nonlinear principal component analysis(NLPCA) with linear principal component analysis(LPCA) with the data of sea surface wind anomalies(SWA),surface height anomalies(SSHA),and sea surface temperature anomalies(SSTA),taken in the South China Sea(SCS) between 1993 and 2003.The SCS monthly data for SWA,SSHA and SSTA(i.e.,the anomalies with climatological seasonal cycle removed) were pre-filtered by LPCA,with only three leading modes retained.The first three modes of SWA,SSHA,and SSTA of LPCA explained 86%,71%,and 94% of the total variance in the original data,respectively.Thus,the three associated time coefficient functions(TCFs) were used as the input data for NLPCA network.The NLPCA was made based on feed-forward neural network models.Compared with classical linear PCA,the first NLPCA mode could explain more variance than linear PCA for the above data.The nonlinearity of SWA and SSHA were stronger in most areas of the SCS.The first mode of the NLPCA on the SWA and SSHA accounted for 67.26% of the variance versus 54.7%,and 60.24% versus 50.43%,respectively for the first LPCA mode.Conversely,the nonlinear SSTA,localized in the northern SCS and southern continental shelf region,resulted in little improvement in the explanation of the variance for the first NLPCA.展开更多
State reconstruction approach is very useful for sensor fault isolation, reconstruction of faulty measurement and the determination of the number of components retained in the principal components analysis （PCA） mod...State reconstruction approach is very useful for sensor fault isolation, reconstruction of faulty measurement and the determination of the number of components retained in the principal components analysis （PCA） model. An extension of this approach based on a Nonlinear PCA （NLPCA） model is described in this paper. The NLPCA model is obtained using five layer neural network. A simulation example is given to show the performances of the proposed approach.展开更多
基金The National Natural Science Foundation of China(No60504033)
文摘A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is： First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator＇s fault data set prove that the proposed method is effective.
文摘Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal component analysis (NLPCA) using radial basis function (RBF) neural network is developed in this paper. The orthogonal least squares (OLS) algorithm is used to train the RBF neural network. This method improves the training speed and prevents it from being trapped in local optimization. Results of two experiments show that this NLPCA method can effectively capture nonlinear correlation of nonlinear complex data, and improve the precision of the classification and the prediction.
基金Supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (No.KZCX1-YW-12)the National Natural Science Foundation of China (No.40706011)the Open Foundation of Key Laboratory of Marine Science and Numerical Modeling (MASNUM)
文摘We compared nonlinear principal component analysis(NLPCA) with linear principal component analysis(LPCA) with the data of sea surface wind anomalies(SWA),surface height anomalies(SSHA),and sea surface temperature anomalies(SSTA),taken in the South China Sea(SCS) between 1993 and 2003.The SCS monthly data for SWA,SSHA and SSTA(i.e.,the anomalies with climatological seasonal cycle removed) were pre-filtered by LPCA,with only three leading modes retained.The first three modes of SWA,SSHA,and SSTA of LPCA explained 86%,71%,and 94% of the total variance in the original data,respectively.Thus,the three associated time coefficient functions(TCFs) were used as the input data for NLPCA network.The NLPCA was made based on feed-forward neural network models.Compared with classical linear PCA,the first NLPCA mode could explain more variance than linear PCA for the above data.The nonlinearity of SWA and SSHA were stronger in most areas of the SCS.The first mode of the NLPCA on the SWA and SSHA accounted for 67.26% of the variance versus 54.7%,and 60.24% versus 50.43%,respectively for the first LPCA mode.Conversely,the nonlinear SSTA,localized in the northern SCS and southern continental shelf region,resulted in little improvement in the explanation of the variance for the first NLPCA.
文摘State reconstruction approach is very useful for sensor fault isolation, reconstruction of faulty measurement and the determination of the number of components retained in the principal components analysis （PCA） model. An extension of this approach based on a Nonlinear PCA （NLPCA） model is described in this paper. The NLPCA model is obtained using five layer neural network. A simulation example is given to show the performances of the proposed approach.
