Anomaly Analytics and Structural Assessment in Process Industries
نویسنده
چکیده
Detecting anomalous behavior can be of critical importance in an industrial application context: While modern production sites feature sophisticated alarm management systems, they mostly react to single events. In the context of process industries and heterogeneous data sources, we model sequential alarm data for anomaly detection and analysis, based on first-order Markov chain models. We outline hypothesis-driven and description-oriented modeling and provide an interactive dashboard for exploration and visualization.
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تاریخ انتشار 2017