Time domain graph-based anomaly detection approach applied to a real industrial problem
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Universidade Federal de Minas Gerais
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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.
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Anomaly detection, Gabriel graph, Dominating set, Deep autoenconders, Machine learning, A graph-based approach to anomaly detection is presented. This work shows that dominating set incorporates statistical properties of a data set. The concept of a space coverage to encapsulate a data set, using dominating sets, is presented. The new approach brings results similar to deep autoencoders, without the need to extensively set parameters.
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https://www.sciencedirect.com/science/article/pii/S0166361522001117