False monitoring information is a major problem in process production system and several ineffective methods have been proposed to identify false monitoring information in the production system. In this paper, a new m...False monitoring information is a major problem in process production system and several ineffective methods have been proposed to identify false monitoring information in the production system. In this paper, a new method is proposed to identify false monitoring information based on system coupling analysis and collision detection from the perspective of data analysis. Coupling multifractal features are extracted to reflect the changes in coupling relationship by utilizing the multifractal detrended cross-correlation analysis (MF-DXA). Each monitoring variable in process production system has more than one coupled variable, which can be regarded as multi-source. To achieve low redundancy in features and uniform description of coupling relationship, the feature level information fusion is studied based on modified Mahalanobis Taguchi system (MTS). False alarms are identified when the coupling relationships among the coupled monitoring variables collide. Analysis results of coupled R?ssler and Henon datasets indicate the feasibility of this method for selecting the effective coupling feature and uniform description of coupling relationship. The compressor system case of Coal Chemical Ltd. Group is studied and false monitoring information is identified.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 51375375)
文摘False monitoring information is a major problem in process production system and several ineffective methods have been proposed to identify false monitoring information in the production system. In this paper, a new method is proposed to identify false monitoring information based on system coupling analysis and collision detection from the perspective of data analysis. Coupling multifractal features are extracted to reflect the changes in coupling relationship by utilizing the multifractal detrended cross-correlation analysis (MF-DXA). Each monitoring variable in process production system has more than one coupled variable, which can be regarded as multi-source. To achieve low redundancy in features and uniform description of coupling relationship, the feature level information fusion is studied based on modified Mahalanobis Taguchi system (MTS). False alarms are identified when the coupling relationships among the coupled monitoring variables collide. Analysis results of coupled R?ssler and Henon datasets indicate the feasibility of this method for selecting the effective coupling feature and uniform description of coupling relationship. The compressor system case of Coal Chemical Ltd. Group is studied and false monitoring information is identified.
