Abstract
Model-based diagnosis is a commonly used approach to identify anomalies and root causes within cyber-physical production systems (CPPS) through the use of models, which are often times manually created by experts. However, manual modelling takes a lot of effort and is not suitable for today’s fast-changing systems. Today, the large amount of sensor data provided by modern plants enables data-driven solutions where models are learned from the systems data, significantly reducing the manual modelling efforts. This enables tasks such as condition monitoring where anomalies are detected automatically, giving operators the chance to restore the plant to a working state before production losses occur. The choice of the model depends on a couple of factors, one of which is the type of the available signals. Modern CPPS are usually hybrid systems containing both binary and real-valued signals. Hybrid timed automata are one type of model which separate the systems behaviour into different modes through discrete events which are for example created from binary signals of the plant or through real-valued signal thresholds, defined by experts. However, binary signals or expert knowledge to generate the much needed discrete events are not always available from the plant and automata cannot be learned. The unsupervised, non-parametric approach presented and evaluated in this paper uses self-organizing maps and watershed transformations to allow the use of hybrid timed automata on data where learning of automata was not possible before. Furthermore, the results of the algorithm are tested on several data sets.
Chapter PDF
Similar content being viewed by others
References
Factories of the Future: MultiAnnual Roadmap for the contractual PPP under HORIZON 2020. European Union, Luxembourg (2013)
3S-Smart Software Solutions GmbH: Codesys softmotion: Integrierte bewegungssteuerung in einem iec 61131-3 programmiersystem (2017), https://de.codesys.com/produkte/codesys-motion-cnc/softmotion.html
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
Eickmeyer, J., Krueger, T., Frischkorn, A., Hoppe, T., Li, P., Pethig, F., Schriegel, S., Niggemann, O.: Intelligente zustandsberwachung von windenergieanlagen als cloudservice. In: Automation 2015. Baden-Baden (Jun 2015)
Eickmeyer, J., Li, P., Givehchi, O., Pethig, F., Niggemann, O.: Data driven modeling for system-level condition monitoring on wind power plants. In: Proceedings of the 26th InternationalWorkshop on Principles of Diagnosis (DX-2015) co-located with 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (Safeprocess 2015), Paris, France, August 31 - September 3, 2015. pp. 43–50 (2015)
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)
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.: Online passive learning of timed automata for cyber-physical production systems. In: 12th IEEE International Conference on Industrial Informatics (INDIN). pp. 60–66 (July 2014)
Maier, A.: Identification of timed behavior models for diagnosis in production systems. Ph.D. thesis, Paderborn, Univ. (2015)
Maier, A., Niggemann, O.: On the learning of timing behavior for anomaly detection in cyber-physical production systems. In: International Workshop on the Principles of Diagnosis (DX). Paris, France (Aug 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., Maier, A., Just, R., Jäger, M.: Anomaly detection in production plants using timed automata: Automated learning of models from observations. In: Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics. pp. 363 – 369. No. 1 (2013)
Niggemann, O., Maier, A., Vodencarevic, A., Jantscher, B.: Fighting the modeling bottleneck - learning models for production plants. Workshop “Modellbasierte Entwicklung Eingebetteter Systeme” (MBEES) (7 2011)
OCME: Shrink-wrap packers vega (Mar 2017), http://www.ocme.com/en/our-solutions/secondary-packaging/vega
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., Ltsch, J.: Machine-learned cluster identification in high-dimensional data. Journal of Biomedical Informatics 66, 95 – 104 (2017), http://www.sciencedirect.com/science/article/pii/S153204641630185X
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 effcient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (Jun 1991)
Vogel-Heuser, B., Diedrich, C., Fay, A., Jeschke, S., Kowalewski, S.and Wollschlaeger, M., Goehner, P.: Challenges for software engineering in automation. Journal of Software Engineering and Applications 7(5) (May 2014), http://dx.doi.org/10.4236/jsea.2014.75041
Yin, H.: The Self-Organizing Maps: Background, Theories, Extensions and Applications, pp. 715–762. Springer Berlin Heidelberg, Berlin, Heidelberg (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
<p>This chapter is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated.</p> <p>The images or other third party material in this chapter are included in the work's Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.</p>
Copyright information
© 2018 The Author(s)
About this chapter
Cite this chapter
von Birgelen, A., Niggemann, O. (2018). Enable learning of Hybrid Timed Automata in Absence of Discrete Events through 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_3
Download citation
DOI: https://doi.org/10.1007/978-3-662-57805-6_3
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-57804-9
Online ISBN: 978-3-662-57805-6
eBook Packages: EngineeringEngineering (R0)