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VERSION:2.0
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ORGANIZER;CN=ESTAD 2023:mailto:info@metec-estad.com
LOCATION:Room 2
SUMMARY:Assisting the operator: Bringing machine learning based operation assistance into the plant production process
DESCRIPTION:In steel manufacturing a core task of operators today is plant supervision. Cameras are a cost-effective way of displaying crucial parts of the plant to give operators an overview of ongoing production processes. However, the sheer overload of information by dozens of simultaneous video streams can be a challenge for workers and could lead to delayed interventions upon problems. Machine learning as a part of artificial intelligence has proven to be an effective solution for addressing this problem. By applying computer vision models and techniques, machines can gain a high-level understanding from input images and videos. Enriched with domain specific knowledge and additional plant information, it becomes a digital assistant or digital expert. It enables detecting and notifying operators on critical conditions of involved processes and components to increase quality and reduce downtime; digital experts can even actively interact with the process. In order to be able to use machine learning based digital assistants in an industrial environment, a robust framework must be created that is suitable for continuous operation. With this paper we present an approach on how digital assistants can be deployed for industrial applications. We describe the various challenges and outline an intuitive process to build a production-ready solution which is integrated into the existing plant software infrastructure. In addition, we illustrate how several aspects like monitoring and versioning can be realized. Using a real-world example of a digital assistant, we demonstrate the successful realization of our solution.

CLASS:PUBLIC
DTSTART:20230614T093000
DTEND:20230614T095000
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