Multivariate statistical process control (MSPC) has been successfully applied to performance monitoring and fault diagnosis for chemical processes However, traditional MSPC are based upon the assumption that the separ...Multivariate statistical process control (MSPC) has been successfully applied to performance monitoring and fault diagnosis for chemical processes However, traditional MSPC are based upon the assumption that the separated latent variables must be subject to normal probability distribution, which sometimes can not be satisfied In this paper, a novel method combining principal component analysis (PCA) and independent component analysis (ICA) is proposed to model non Gaussian data from industry and improve the monitoring performance of process In order to deal with the uncertainty of probability distribution within the independent component, a kind of classifier referred to as support vector classifier is used for classifying the abnormal modes Simulation result for a nonisothermal continuous stirred tank reactor (CSTR) by the presented method verifies the effectiveness of ICA based展开更多
文摘Multivariate statistical process control (MSPC) has been successfully applied to performance monitoring and fault diagnosis for chemical processes However, traditional MSPC are based upon the assumption that the separated latent variables must be subject to normal probability distribution, which sometimes can not be satisfied In this paper, a novel method combining principal component analysis (PCA) and independent component analysis (ICA) is proposed to model non Gaussian data from industry and improve the monitoring performance of process In order to deal with the uncertainty of probability distribution within the independent component, a kind of classifier referred to as support vector classifier is used for classifying the abnormal modes Simulation result for a nonisothermal continuous stirred tank reactor (CSTR) by the presented method verifies the effectiveness of ICA based
