Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivate...Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight approach.The proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.展开更多
Dynamic control of reservoir limited water level is important to reservoir flood control operation.A reasonable limited water level can best utilize flood water resources in addition to flood control.This paper is a t...Dynamic control of reservoir limited water level is important to reservoir flood control operation.A reasonable limited water level can best utilize flood water resources in addition to flood control.This paper is a trial application of the fuzzy information entropy matter-element evaluation method(FIEMEM) as an optimal selection of dynamic control of limited water level.In this method,compound matter elements are established first,followed by establishment of an evaluation model and choice of the optimal scheme on the basis of fuzzy information entropy.In determining weights,a combined weighting method in game theory is adopted to combine experiential weights and mathematical weights so as to eliminate one-sidedness of the single weighting method.Finally,the feasibility of this optimization method is verified by citing dynamic control of Biliuhe reservoir limited water level as an example.展开更多
基金Supported by the National Natural Science Foundation of China(61174114)the Research Fund for the Doctoral Program of Higher Education in China(20120101130016)+1 种基金the Natural Science Foundation of Zhejiang Province(LQ15F030006)and the Science and Technology Program Project of Zhejiang Province(2015C33033)
文摘Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight approach.The proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.
基金supported by the Nonprofit Sector Specific Research of Ministry of Water Resources (Grant No. 200701015)
文摘Dynamic control of reservoir limited water level is important to reservoir flood control operation.A reasonable limited water level can best utilize flood water resources in addition to flood control.This paper is a trial application of the fuzzy information entropy matter-element evaluation method(FIEMEM) as an optimal selection of dynamic control of limited water level.In this method,compound matter elements are established first,followed by establishment of an evaluation model and choice of the optimal scheme on the basis of fuzzy information entropy.In determining weights,a combined weighting method in game theory is adopted to combine experiential weights and mathematical weights so as to eliminate one-sidedness of the single weighting method.Finally,the feasibility of this optimization method is verified by citing dynamic control of Biliuhe reservoir limited water level as an example.
