Abstract
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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Factories of the Future: MultiAnnual Roadmap for the contractual PPP under HORIZON 2020. European Union, Luxembourg (2013)
Alhoniemi, E., Hollmn, J., Simula, O., Vesanto, J.: Process monitoring and modeling using the self-organizing map. Integrated Computer Aided Engineering 6, 3–14 (1999), http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.573
Frey, C.: Monitoring of complex industrial processes based on self-organizing maps and watershed transformations. In: Industrial Technology (ICIT), 2012 IEEE International Conference on (Mar 2012)
Henzinger, T.A.: The theory of hybrid automata. In: Proceedings 11th Annual IEEE Symposium on Logic in Computer Science. pp. 278–292 (Jul 1996)
Kohonen, T.: The self-organizing map. In: Proceedings of the IEEE. vol. 78 (Sep 1990)
Kumpulainen, P., Hätönen, K.: Local anomaly detection for mobile network monitoring. Inf. Sci. 178(20), 3840–3859 (2008)
Liukkonen, M., Hiltunen, Y., Laakso, I.: Advanced monitoring and diagnosis of industrial processes. In: 2013 8th EUROSIM Congress on Modelling and Simulation. pp. 112–117 (Sept 2013)
Maier, A.: Identification of timed behavior models for diagnosis in production systems. Ph.D. thesis, Paderborn, Univ. (2015)
Meyer, F.: Topographic distance and watershed lines. Signal Processing 38(1), 113–125 (jul 1994), http://dx.doi.org/10.1016/0165-1684(94)90060-4
Niggemann, O., Lohweg, V.: On the diagnosis of cyber-physical production systems: State-of-the-art and research agenda. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
OPAK-Consortium: Offene engineering-plattform fr autonome, mechatronische automatisierungskomponenten in funktionsorientierter architektur (2017), http://www.opak-projekt.de/
Simula, O., Kangas, J.: Process monitoring and visualisation using self-organizing maps (1995)
Tian, J., Azarian, M.H., Pecht, M.: Anomaly detection using self-organizing mapsbased k-nearest neighbor algorithm. Second European Conference of the Prognostics and Health Management Society 2014 (2014)
Ultsch, A., Siemon, H.P.: Kohonen’s self-organizing feature maps for exploratory data analysis. In: Proceedings of the International Neural Network Conference (INNC’90 (1990), http://www.uni-marburg.de/fb12/datenbionik/pdf/pubs/1990/UltschSiemon90
Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (Jun 1991)
von Birgelen, A., Niggemann, O.: Using self-organizing maps to learn hybrid timed automata in absence of discrete events. In: 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2017) (Sep 2017)
Yin, H.: The Self-Organizing Maps: Background, Theories, Extensions and Applications, pp. 715–762. Springer Berlin Heidelberg, Berlin, Heidelberg (2008)
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von Birgelen, A., Niggemann, O. (2018). Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps. In: Niggemann, O., Schüller, P. (eds) IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency. Technologien für die intelligente Automation, vol 8. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57805-6_4
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DOI: https://doi.org/10.1007/978-3-662-57805-6_4
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