Abstract
This paper is dedicated to solving the problem of concept drift in industrial plants using artificial intelligence methods. For this purpose, methodological approaches and procedures are considered and analyzed. Based on the findings, reference architectures were developed at different abstraction levels that can be used in an industrial environment and enable continuous machine learning. Continuous machine learning offers the possibility of adapting to dynamic changes in the production environment, which are reflected in constantly changing data sets. Through a combination of machine learning techniques, a novel and practical framework for continuous learning, also known as lifelong learning, is presented. The integration of problem-focused machine learning methods is advancing in production, e.g., predictive maintenance, process optimization or fault detection. Thereby, fully or semi-automated adaptations to changing environments requiring continuous improvements are less often explored, although practical use cases often require adaptive capabilities as the physical data distribution may change over time. In this paper, the application was continuously improved based on case studies and empirical results, and finally validated with a quality assurance application. Various methods and approaches for detecting concept and data deviations, retraining, packaging and model updating had to be investigated, which led to the question of what a real industry-oriented implementation could look like. The result is a reference architecture that can run on cloud and edge computing resources. This reference architecture is validated in real-world application in the parquet production sector, proving its feasibility and efficiency.
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Data Availability
The datasets generated and analyzed during the use case are not publicly available due to company privacy reasons (Ascalia-Bauwerk proprietary data). The main contributions of the paper (conceptual and concrete reference architectures, techniques in the field of continual learning) however do not depend on neither a direct consequence of the particular data used but can more generally be applied.
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Acknowledgements
Ascalia would like to acknowledge their colleague Valentino Petric, who was instrumental in the development of the initial machine learning model and its deployment, which was anything but an easy task. Furthermore, they extend their gratitude to Boris Poklepovic and his team at Bauwerk Group Hrvatska for providing the opportunity to address the problem in industry at´ their factory and for tirelessly responding to inquiries with their expertise and readiness to assist.
Funding
This work was funded by European Union’s Horizon 2020 project titled:”Digital twins bringing agility and innovation to manufacturing SMEs, by empowering a network of DIHs with an integrated digital platform that enables Manufacturing as a Service (MaaS)” (DIGITbrain), under grant agreement no. 952071. This work also received financing from the Hungarian project no. TKP2021-NVA-01, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-NVA funding scheme. This work was also sponsored/funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) in the context of the project "Digitaler Zwilling und Künstliche Intelligenz in der vernetzten Fabrik für die integrierte Nutzfahrzeugproduktion, Logistik und Qualitätssicherung" (TWIN4TRUCKS, 13IK010F). The authors are grateful for this support.
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Conceptualization: J.A., Á.H. and M.E.; Methodology: J.A., S.B., E.M. and A.C.M.; Software: M.E., D.J., V.Ž. and D.S.; Writing—original draft: J.A., D.J., Á.H., V.Ž., M.E., D.S. and A.C.M.; Writing—reviewing and editing: J.A., D.J., Á.H., V.Ž., T.L. and M.E.; Supervision: S.B., T.L. and A.C.M.; Validation: D.J., V.Ž. and D.S.
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Antony, J., Jalušić, D., Bergweiler, S. et al. Adapting to Changes: A Novel Framework for Continual Machine Learning in Industrial Applications. J Grid Computing 22, 71 (2024). https://doi.org/10.1007/s10723-024-09785-z
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DOI: https://doi.org/10.1007/s10723-024-09785-z