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Asset management knowledge graph for production plants in process industry

Asset management Wissensgraphen für Produktionsanlagen in der Prozessindustrie
  • Ramy Hana

    Ramy Hana graduated in electrical Engineering with a specialisation in process automation from TU-University in 2020. He worked as an intern for Bayer AG in the process performance improvement department where he developed an anomaly detection library for the purpose of process monitoring. In 2021, he joined the chair of Information and Automation Systems at the RWTH Aachen University with a research focus on field device performance monitoring.

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    and Tobias Kleinert

    Tobias Kleinert graduated in Mechanical Engineering at RWTH Aachen University and completed his PhD in 2005 at the chair of Prof. Jan Lunze at Ruhr-Universität Bochum. Until 2020, he worked at BASF in the fields of Advanced Control, Process Control Systems, Manufacturing Execution Systems, Functional Safety, Digitalization and Smart Manufacturing. Since 2020, he is head of Chair of Information and Automation Systems at RWTH, with a focus on data management and flexible automation in industrial productions.

Abstract

The introduction and adoption of various semantic models for describing assets within a plant have paved the way for new opportunities. This contribution centers on the establishment and continuous maintenance of a plant asset management system based on knowledge graphs, utilizing the information contained in various semantic models. Additionally, it highlights how the graph can be leveraged in the creation and configuration of AI-based anomaly detection models, aimed at the monitoring of measurement values in a process plant. The concept paves the way for the wide deployment of AI models in monitoring applications in process industry.

Zusammenfassung

Die Etablierung und Einführung verschiedener semantischer Modelle zur Beschreibung von Assets innerhalb einer Anlage bilden die Grundlage für neue Anwendungsmöglichkeiten. In diesem Beitrag steht die Einrichtung und kontinuierliche Pflege eines auf Wissensgraphen basierenden Anlagenmanagementsystems im Fokus, das die in verschiedenen Modellen enthaltenen Informationen integriert. Darüber hinaus wird aufgezeigt, wie der Graph für die Erstellung und Konfiguration von KI-basierte Modellen zur Erkennung von Anomalien genutzt werden kann, die auf die Überwachung von Messwerten in einer Prozessanlage abzielen. Das Konzept ebnet den Weg für den umfangreichen Einsatz von KI-Modellen in Überwachungsanwendungen für Prozessindustrie.


Corresponding author: Ramy Hana, Chair of Information and Automation Systems for Process and Material Technology, RWTH Aachen: Rheinisch-Westfalische Technische Hochschule Aachen, Aachen, Germany, E-mail: 

About the authors

Ramy Hana

Ramy Hana graduated in electrical Engineering with a specialisation in process automation from TU-University in 2020. He worked as an intern for Bayer AG in the process performance improvement department where he developed an anomaly detection library for the purpose of process monitoring. In 2021, he joined the chair of Information and Automation Systems at the RWTH Aachen University with a research focus on field device performance monitoring.

Tobias Kleinert

Tobias Kleinert graduated in Mechanical Engineering at RWTH Aachen University and completed his PhD in 2005 at the chair of Prof. Jan Lunze at Ruhr-Universität Bochum. Until 2020, he worked at BASF in the fields of Advanced Control, Process Control Systems, Manufacturing Execution Systems, Functional Safety, Digitalization and Smart Manufacturing. Since 2020, he is head of Chair of Information and Automation Systems at RWTH, with a focus on data management and flexible automation in industrial productions.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2023-11-30
Accepted: 2024-07-12
Published Online: 2024-10-09
Published in Print: 2024-10-28

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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