Abstract
Robustness against internal or external disturbances is a key competence of Organic Computing Systems. Hereby, a rarely discussed aspect are physical disturbances, therefore, failures or breakdowns that affect a systems physical components. Before experiencing such a disturbance, physical components may show various measurable signs of deterioration that might be assessed through sensor data. If interpreted correctly, it would be possible to predict future physical disturbances and act appropriately in order to prevent them from possibly harming the overall system. As the actual structure of such data as well as the behaviour that disturbances produce might not be known a priori, it is of interest to equip Organic Computing Systems with the ability to learn to predict them autonomously. We utilize the Automated Machine Learning Framework TPOT for an online-learning-inspired methodology for learning to predict physical disturbances in an iterative manner. We evaluate our approach using a freely available dataset from the broader domain of Predictive Maintenance research and show that our approach is able to build predictors with reasonable prediction quality autonomously.
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Görlich-Bucher, M., Heider, M., Hähner, J. (2023). Predicting Physical Disturbances in Organic Computing Systems Using Automated Machine Learning. In: Goumas, G., Tomforde, S., Brehm, J., Wildermann, S., Pionteck, T. (eds) Architecture of Computing Systems. ARCS 2023. Lecture Notes in Computer Science, vol 13949. Springer, Cham. https://doi.org/10.1007/978-3-031-42785-5_4
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