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Explainable Unsupervised Multi-Sensor Industrial Anomaly Detection and Categorization

Mina Ameli; Philipp Aaron Becker; Katharina Lankers; Markus van Ackeren; Holger Bähring; Wolfgang Maaß
In: International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications (ICMLA-2022), located at ICMLA, December 12-14, Paradise Island, Bahamas, IEEE, 12/2022.


Real-time Anomaly Detection is of great importance in industrial applications in order to have high-quality production and avoid downtime or failure of the system. In this paper, we study the application of anomaly detection over the multivariate data collected from Glass Production Industry. Our experiments utilize and compare different Unsupervised multivariate time series Anomaly Detection and Localization algorithms that have already demonstrated significant results on the state-of-the-art data sets. We propose a two-level multivariate anomaly detection approach that not only detects anomalous events in the production line but also categorize the different type of anomalies based on statistical pattern recognition. Furthermore, we localize the anomalous sensors by utilizing Explainable-AI approaches to help better decision-making in glass production monitoring. In this work, we propose an efficient pipeline for Anomaly Detection, Categorization and Localization which the experiments show promising results.