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Fault prediction technology is important to avoid serious process failure. This paper is concerned with the fault prediction of dynamic industrial process with incipient faults and proposes a canonical variable trend analysis (CVTA) based fault prediction method. In the proposed method, canonical variate analysis (CVA) algorithm is firstly applied to analyze the process dynamics and extract the uncorrelated...
A soft sensor modeling method is proposed by combining the kernel principal component analysis (KPCA) with the support vector machine (SVM). Via KPCA the method is able to capture the high-ordered principal components among the secondary variables, and use SVM to establish a correlated regression model between the featured principal components and the primary variable. The proposed KPCA-SVM method...
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