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- W2810699813 abstract "Abstract The Concept Drift (CD) effect is often involved in most of the chemical manufacturing processes, and could easily lead to the decay of the Fault Detection and Diagnosis (FDD) system performance. This paper proposes a methodology for the online quantification of the CD amount. The method is based on the combination of an incremental learned classifier (Hyperplane Distance- Support Vector Machines (HD-SVM)) and a non-automatic clustering method (K-means technique). The incremental learned classifier is employed to examine and characterized the classes (i.e. normal, fault-type 1, fault-type 2, etc.) of the measured online datasets. Then, the CD amount is tracked through the monitoring of the matching extent between the latent pattern of the online dataset (regarding each class) and the latent pattern extracted from a certain reference dataset (for the corresponding class). The results of the method application to a benchmark case study involving a Continuous Stirred Tank Reactor (CSTR) indicate promising capabilities for tracking the CD amount along the operating time." @default.
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- W2810699813 date "2018-01-01" @default.
- W2810699813 modified "2023-10-15" @default.
- W2810699813 title "Online Quantification of the Concept Drift Using Incremental Learned Classifier and Non-automatic Clustering" @default.
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- W2810699813 doi "https://doi.org/10.1016/b978-0-444-64235-6.50187-x" @default.
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