Abstract

AbstractThere are many Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) applications being used in real manufacturing plants. Digital twins with AI/ML/DL capabilities are among these applications. Steel is an important process industry, where such applications have vast potential to provide positive impacts on different aspects, including energy consumption and cost benefits. The Cognitive Plants Through Proactive Self-Learning Hybrid Digital Twins (CogniTwin) project aims at adding the cognitive elements to the existing process control systems, enabling their capability to self-organise and offer solutions to unpredicted behaviours. In NOKSEL pilot of CogniTwin, the main problem is related to very high costs of machines breakdowns. By developing digital, hybrid and cognitive twins, the pilot aims at reducing energy consumption and average duration of machine downtimes. Cognitive twins for Spiral Welded Steel Pipes (SWP) will enable predictive maintenance at the steel pipe plant. This study briefs the NOKSEL pilot’s purpose, scope, state and the results gained in the 1st year of the project.KeywordsDigital twinCognitive twinIndustry 4.0Spiral welded steel pipeArtificial intelligencePredictive maintenanceSmart condition monitoring

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