TY - CHAP A1 - Lorenzen, Finn A1 - Schönig, Felix A1 - Grote-Ramm, Wolfgang A1 - Oliveira Brito, Marcel A1 - Neuberger, Heiko A1 - Sobolyev, Alexander T1 - Continual Adaptation of Compressor Maps During Operation on Measurement Data For Increased Performance of Model-Based Applications in Turbomachinery T2 - ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition, June 26–30, 2023 Boston, Massachusetts, USA N2 - Higher control engineering concepts, including artificial intelligence and model-based control, are recently becoming more popular in complex industrial applications because they enable a significant increase in efficiency. E.g., a model-based approach can be used to enhance the control and monitoring of several coupled components of a larger turbomachinery train. This requires dynamic models of the components, including all machine characteristics, which may experience a shift in their behavior over the course of their lifetime. Such changes occur due to natural fouling, wear of sub-components or external effects induced by maintenance measures. To overcome this problem, we model machine-characteristic maps with artificial neural networks, which may be used as part of prediction models in a model predictive control unit. If an appropriate data set is available, this allows us to adapt the model to the current behavior of the machine without requiring in-depth knowledge about the underlying physics of this “concept drift”, as it is known in literature. However, the training of neural networks is usually connected with high computational effort while the task needs to be executed in real-time during operation. Furthermore, in real operating conditions, machine sensors can only provide labeled data from the current operating point of the machine, which can be stationary for long operation periods of up to several months. In general, this yields only an unrepresentative data subset of the machine-characteristics, which is not sufficient to retrain the whole model to a new state. To solve these problems, we firstly reduce the model adaptation to a convex optimization problem, which can be efficiently solved in real-time conditions. Secondly, we use a specialized data management system with which we can integrate historical data to supplement the unrepresentative data subset. In this paper we show the application of the outlined method on a compressor map and discuss the advantages and requirements of the method in the context of modeling applications for industrial turbomachinery. KW - compressors KW - Intelligent control KW - Neural networks KW - Modeling Y1 - 2023 SN - 978-0-7918-8697-7 U6 - https://doi.org/10.1115/GT2023-103458 VL - Volume 4 SP - 1 EP - 7 PB - ASME CY - Boston ER -