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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Automated Identification of Parameters in Control Systems of Machine Tools
verfasst von : P. Gönnheimer, A. Puchta, J. Fleischer
Erschienen in: Production at the leading edge of technology
Verlag: Springer Berlin Heidelberg
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Abstract
Especially in the context of Artificial Intelligence (AI) applications and increasing Overall Equipment Effectiveness (OEE) requirements, the use of data in production is gaining in importance. Applications in the field of process or condition monitoring use, for example, machine component parameters such as motor currents, travel speeds and position information. However, as the data is usually only accessible in the machine control systems in non-standard structures and semantics, while having a large number of potential variables, the identification and use of these parameters and data sources represents a significant challenge. This paper therefore presents an approach to automatically identify and assign machine parameters on the basis of time series data. For the identification, feature- and deep learning-based classification approaches are used and compared. Classification results show a general usability of the approaches for the identification of machine parameters.