Abstract
This chapter introduces methods from the field of visual analytics and machine learning which are able to handle high feature dimensions, timed systems and hybrid systems, i.e. systems comprising both discrete and continuous signals. Further, a three steps tool chain is introduced which guides the operator from the visualization of the normal behavior to the anomaly detection and also to the localization of faulty modules in production plants.
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Acknowledgments
This work is an extension of the chapter titled Visual Anomaly Detection in Production Plants by Alexander Maier, Tim Tack and Oliver Niggemann, published in 9th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2012), Rome, Italy.
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Tack, T., Maier, A., Niggemann, O. (2014). On Visual Analytics in Plant Monitoring. In: Ferrier, JL., Bernard, A., Gusikhin, O., Madani, K. (eds) Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-319-03500-0_2
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DOI: https://doi.org/10.1007/978-3-319-03500-0_2
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