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<p>In modern industries, the integration of <i>system-</i> and <i>system-of-systems</i> (SoS) digital twins (DTs) emerged as a powerful tool for performance monitoring, deviation analysis and predictive maintenance. This study demonstrates how these DTs can assist in troubleshooting the complex multi-layer process for identifying process related issues and failure. This study also highlights experience in implementing DTs in industrial environments, such as the complexity of processes, managing and exchanging real-time data between the manufacturing processes and analytical systems. The research includes a case study on an industrial ring rolling process, where multiple DTs were deployed in-parallel with the operation to continuously monitor the entire process, including individual systems and their interconnections. System-level DTs were combined to construct SoS-level DT, enabling comprehensive performance evaluation. The analytical system was developed by using Python modules, monitoring real-time data over a period of ten months. To aid industrial users, high-dimensional analyses from both system- and SoS-level DTs were synthesized using Principal Component Analysis, providing a quick overview of the overall process. The results illustrated the problem that appeared in the rolling process were due to one of its processing tools. The process anomalies at early stage, assisting in identifying the root causes were also highlighted in this study. Furthermore, there were four challenges experienced in the research, i.e., monitoring and troubleshooting complexity, computing and managing real-time data, handling false alarm, and processing data close to edge devices.</p>

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System and system-of-systems digital twins for predictive maintenance and root cause analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月31日

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  • Muhammad Hassan,
  • Niclas Björsell

摘要

In modern industries, the integration of system- and system-of-systems (SoS) digital twins (DTs) emerged as a powerful tool for performance monitoring, deviation analysis and predictive maintenance. This study demonstrates how these DTs can assist in troubleshooting the complex multi-layer process for identifying process related issues and failure. This study also highlights experience in implementing DTs in industrial environments, such as the complexity of processes, managing and exchanging real-time data between the manufacturing processes and analytical systems. The research includes a case study on an industrial ring rolling process, where multiple DTs were deployed in-parallel with the operation to continuously monitor the entire process, including individual systems and their interconnections. System-level DTs were combined to construct SoS-level DT, enabling comprehensive performance evaluation. The analytical system was developed by using Python modules, monitoring real-time data over a period of ten months. To aid industrial users, high-dimensional analyses from both system- and SoS-level DTs were synthesized using Principal Component Analysis, providing a quick overview of the overall process. The results illustrated the problem that appeared in the rolling process were due to one of its processing tools. The process anomalies at early stage, assisting in identifying the root causes were also highlighted in this study. Furthermore, there were four challenges experienced in the research, i.e., monitoring and troubleshooting complexity, computing and managing real-time data, handling false alarm, and processing data close to edge devices.