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- W2393068999 abstract "When kernel principal component analysis(KPCA)method is applied to the on-line monitoring of complex chemical industrial process,primary fault sources are usually difficult to identify.So an integrated fault identification method based on KPCA and least squares support vector machine(LS-SVM) is proposed.First the data are analyzed by using KPCA,T2 and SPE are constructed in the feature space for detecting faults.If T2 and SPE exceed the predefined control limits,a fault may have occurred.Then nonlinear principal component score vectors of the samples are calculated and inputted into least squares support vector machine to identify the faults through least squares support vector machine classification.The proposed method is applied to the Tennessee Eastman(TE) chemical industry process.Simulation results of multiple fault modes demonstrate that the proposed method can not only effectively identify various types of fault sources,but also improve speed of fault detection and diagnosis." @default.
- W2393068999 created "2016-06-24" @default.
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- W2393068999 date "2012-01-01" @default.
- W2393068999 modified "2023-09-23" @default.
- W2393068999 title "A fault diagnosis algorithm of chemical industry process based on KPCA and LS-SVM" @default.
- W2393068999 hasPublicationYear "2012" @default.
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