Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps

Modern Cyber-Physical Production Systems provide large amounts of data such as sensor and control signals or configuration parameters. The available data enables unsupervised, data-driven solutions for model-based anomaly detection and anomaly localization: models which represent the normal behavior of the system are learned from data. Then, live data from the system can be compared to the predictions of the model to detect anomalies and perform anomaly localization. In this paper we use self-organizing maps for the aforementioned tasks and evaluate the presented methods on real-world systems.

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