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• W2519357899 abstract "This paper introduces a graph signal processing (GSP) framework for industrial process monitoring. Through a graph built to represent the variables of an industrial plant and their relationships, we show how to transform each sample vector observation using the graph Fourier transform (GFT) in order to infer the status of the plant. To validate the proposed method we compare it to the state of the art PCA T2 and Q methods for the Tennessee Eastman Process (TEP), a widely used benchmark for validation of methods in control and process monitoring. The results show our method outperforms PCA in most cases." @default.
• W2519357899 created "2016-09-23" @default.
• W2519357899 creator A5040001106 @default.
• W2519357899 creator A5054042935 @default.
• W2519357899 date "2016-07-01" @default.
• W2519357899 modified "2023-09-26" @default.
• W2519357899 title "Plant-wide fault detection using graph signal processing" @default.
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• W2519357899 doi "https://doi.org/10.1109/sam.2016.7569744" @default.
• W2519357899 hasPublicationYear "2016" @default.
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