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• W4283205711 endingPage "103714" @default.
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• W4283205711 abstract "Detecting anomalies in industrial processes is a critical task. Prior fault detection can reduce company costs, and most importantly, may prevent accidents and environmental damage. Anomaly detection can be treated as drift detection, as both aims at identifying changes in data that happen unexpectedly over time. In this paper, a graph-based approach to anomaly detection is presented. Based on graph theory and set coverage, the method brings results similar to deep autoencoders, without the need to extensively set parameters." @default.
• W4283205711 created "2022-06-22" @default.
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• W4283205711 date "2022-11-01" @default.
• W4283205711 modified "2023-10-10" @default.
• W4283205711 title "Time domain graph-based anomaly detection approach applied to a real industrial problem" @default.
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• W4283205711 doi "https://doi.org/10.1016/j.compind.2022.103714" @default.
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