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Machine Learning for Industrial Process Optimization

  • Currently, process control in automation technology is mostly regulated by fixed process parameters as a compromise between several identically constructed systems or by plant operators, who are often guided by intuition based on decades of experience. Some operators are not able to pass on their knowledge to the next generation due to societal developments, e.g. academization or increased desire for self-actualization. In contrast, the vision of Smart Factories includes intelligent machining processes that should ultimately lead to self-optimization and adaptation to uncontrollable variables. To consistently implement this vision of self-optimizing machines, a defined quality criterion must be automatically monitored and act as a feedback for continual, autonomous and safe optimization. The term safe refers to the compliance with process quality standards, which must always be maintained. In a very conservative branch such as automation technology, no risks whatsoever are allowed through random experiments for data generation in production operations, since, for example, an unscheduled downtime leads to serious financial losses. Furthermore, machine-driven decisions may at no time pose a threat. Thus, decisions under uncertainty may only be taken where the amount of uncertainty can be considered uncritical. Additionally, industrial applications require a guaranteed real-time capability in terms of reaction to ensure that the actions can be taken in time whenever needed. Since economic aspects are often crucial for decisions in industry, necessary experiments under laboratory conditions, for example, should also be as avoidable as possible, while the effort required for integration into a field application should be as simple as possible. The aim of this work is the scientific investigation of the integration of learning feedback for intelligent decision making in the control of industrial processes. The successful integration enables data-driven process optimization. To get closer to the vision of self-optimizing machines, safe optimization methods for industrial applications on the process level are investigated and developed. Here, considering the given restrictions of the automation industry is critical. This work addresses several fields including technical, algorithmic and conceptual aspects. The algorithmic refinements are essential for enabling a wider use of safe optimization for industrial applications. They allow, e.g., the automatic handling of the majority of hyper-parameters and the solution of complex problems by increased computational efficiency. Furthermore, the trade-off between exploration and exploitation of safe optimization in high-dimensional spaces is improved. To account for changeable states perceived via sensor data, contextual Bayesian optimization is modified so that safety requirements are met and real-time capability is satisfied. A software application for industrial safe optimization is implemented within a real-time capable control to be able to interact with other software modules to reach an intelligent decision. Further contributions cover recommendations regarding technical requirements with focus on edge control devices and the conceptual inclusion of machine learning to industrial process control. To emphasize the application relevance and feasibility of the presented concepts, real world lighthouse projects are realized in the course of this work, indented to reduce skepticism and thus initiate the breakthrough of self-optimizing machines.

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Author:Stefano De Blasi
Subtitle (German):Eine Dissertation zur Erlangung des akademischen Grades Doktor der Naturwissenschaften (Dr. rer. nat.)
Place of publication:Fulda
Document Type:Doctoral Thesis
Date of Publication (online):2022/09/28
Year of first Publication:2022
Publishing Institution:Hochschule Fulda
Granting Institution:Hochschule Fulda
Date of final exam:2022/09/16
Release Date:2022/10/05
Tag:Safe optimization, automation technology, self-optimizing machine, industrial control architecture, reinforcement learning
Institutes:Hochschul├╝bergreifendes Promotionszentrum Angewandte Informatik (PZAI)
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International

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