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• W2954923624 abstract "Abstract This work proposes a classification model for fault diagnosis. In a first stage, an unsupervised clustering algorithm discovers groups of potential fault classes. Subsequently, a specialized classifier is trained for each of the clusters, thus diminishing complexity and augmenting performance. The quality of the diagnosis is further improved by combining the classifiers in an ensemble. As a benchmark, data provided by the Tennessee Eastman chemical plant simulator was used, with promising results." @default.
• W2954923624 created "2019-07-12" @default.
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• W2954923624 date "2019-01-01" @default.
• W2954923624 modified "2023-09-24" @default.
• W2954923624 title "Feature extraction in an ensemble of multiple local classifiers for fault diagnosis in industrial processes" @default.
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• W2954923624 doi "https://doi.org/10.1016/j.ifacol.2019.06.077" @default.
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