A visual method of fault diagnosis for a complicated process was developed based on self-organizing map (SOM).Due to the high dimensionality of the complicated process, principal component analysis (PCA) was introduce...A visual method of fault diagnosis for a complicated process was developed based on self-organizing map (SOM).Due to the high dimensionality of the complicated process, principal component analysis (PCA) was introduced to reduce the dimension of the process data.Then the self-organizing map was utilized to project the preprocessed data onto a 2D visualization space in which different process conditions were represented by different regions.Online monitoring could be achieved by the dynamic trajectory in the visualization space.The cause of certain fault could be deduced from the U-matrix of the derived SOM network and the loadings vector of the principal components.The application to the Tennessee Eastman process (TEP) demonstrated that fault detection and diagnosis could be carried out in a more intuitional and practical manner by using the proposed method.展开更多
The modeling and control of pH neutralization processes is a difficult problem in the field of process control.A multi-modeling method using an improved k-means clustering based on a new validity function is proposed ...The modeling and control of pH neutralization processes is a difficult problem in the field of process control.A multi-modeling method using an improved k-means clustering based on a new validity function is proposed in this paper.There are some common problems, including the number of clusters assumed as a priori knowledge and initial cluster centers selected randomly for classical k-means clustering.The proposed algorithm is used to compute initial cluster centers and a new validity function is added to determine the appropriate number of clusters, then partial least squares (PLS) is used to construct the regression equation for each local cluster.Simulation results showed that multiple models using the proposed algorithm gave good performance, and the feasibility and validity of the proposed algorithm was verified.展开更多
文摘A visual method of fault diagnosis for a complicated process was developed based on self-organizing map (SOM).Due to the high dimensionality of the complicated process, principal component analysis (PCA) was introduced to reduce the dimension of the process data.Then the self-organizing map was utilized to project the preprocessed data onto a 2D visualization space in which different process conditions were represented by different regions.Online monitoring could be achieved by the dynamic trajectory in the visualization space.The cause of certain fault could be deduced from the U-matrix of the derived SOM network and the loadings vector of the principal components.The application to the Tennessee Eastman process (TEP) demonstrated that fault detection and diagnosis could be carried out in a more intuitional and practical manner by using the proposed method.
文摘The modeling and control of pH neutralization processes is a difficult problem in the field of process control.A multi-modeling method using an improved k-means clustering based on a new validity function is proposed in this paper.There are some common problems, including the number of clusters assumed as a priori knowledge and initial cluster centers selected randomly for classical k-means clustering.The proposed algorithm is used to compute initial cluster centers and a new validity function is added to determine the appropriate number of clusters, then partial least squares (PLS) is used to construct the regression equation for each local cluster.Simulation results showed that multiple models using the proposed algorithm gave good performance, and the feasibility and validity of the proposed algorithm was verified.
