Conception of a Reference Architecture for Machine Learning in the Process Industry
René Wöstmann, P. Schlunder, F. Temme, Ralf Klinkenberg, Josef Kimberger, Andrea Spichtinger, Markus Goldhacker, Jochen Deuse
Abstract
The increasing global competition demands continuous optimization of products and processes from companies in the process industry. Where conventional methods of Lean Management and Six Sigma reach their limits, new opportunities and challenges arise through increasing connectivity in the Industrial Internet of Things and machine learning. The majority of industrial projects do not reach the deployment or are isolated solutions, as the structures for data integration, training, deployment and maintenance of models are not established. This paper presents the conception of a reference architecture for machine learning in the process industry to support companies in implementing their own specific structures. The focus is on the development process and an exemplary implementation in the brewing industry.