Abstract
The paper is bridging systemic and industrial AIA for PDM illustrated by examples. Soft and hard sciences meet in the regime of decisions. Depending on data available and the specific process knowledge, the most important is the complexity content, leading to interdependent decisions. With respect to AIA it makes practical sense to reduce information and use time series analysis, whereas more complex systems are more advantageously using advanced AI methods as machine learning (ML) as well as by means of data availability. The main challenge for the technical systems investigated is that damage shall be predicted and hence normal operation remains uncertain or determined by limited AI implementation.
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Heiden, B., Tonino-Heiden, B., Obermüller, T., Loipold, C., Wissounig, W. (2020). Rising from Systemic to Industrial Artificial Intelligence Applications (AIA) for Predictive Decision Making (PDM) - Four Examples. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_94
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