Integrating the Digital Twin of a Shop Floor Conveyor in the Manufacturing Control System

Răileanu, Silviu; Borangiu, Theodor; Ivănescu, Nick; Morariu, Octavian; Anton, Florin Daniel

doi:10.1007/978-3-030-27477-1_10

Cited by 19 publications

(8 citation statements)

References 14 publications

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“…monitoring and forecasting of the behaviour of the physical twin [61] or may be used as a means to populate data repositories to be used on higher levels [70]). When integrating the various DT modules into a system architecture, this could be used for a global decision making and integrated into a higher-level control system, such as the MES [71,72].…”

Section: Discussionmentioning

confidence: 99%

MES-integrated digital twin frameworks

Negri

Berardi

Fumagalli

et al. 2020

Journal of Manufacturing Systems

125

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Section: Discussionmentioning

confidence: 99%

MES-integrated digital twin frameworks

Negri

Berardi

Fumagalli

et al. 2020

Journal of Manufacturing Systems

125

View full text Add to dashboard Cite

“…A study on the integration of DT technology to a shop floor conveyor into the manufacturing control system by [ 76 ] presented that DT architecture, as seen in Figure 1 and Figure 2 , comprises the data, modeling, analysis, and application layers. The (i) data layer is responsible for collecting and storing data from various sources, such as sensors, Internet of Things (IoT) devices, and other systems that generate data.…”

Section: Digital Twin Architecturementioning

confidence: 99%

Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities

Bofill,

Abisado,

Villaverde

et al. 2023

Sensors

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High efficiency and safety are critical factors in ensuring the optimal performance and reliability of systems and equipment across various industries. Fault monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare. The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system’s health and enabling proactive maintenance and decision making.

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“…A study on the integration of DT technology to a shop floor conveyor into the manufacturing control system by [76] presented that DT architecture, as seen in Figures 1 and 2, comprises the data, modeling, analysis, and application layers. The (i) data layer is responsible for collecting and storing data from various sources, such as sensors, Internet of Things (IoT) devices, and other systems that generate data.…”

Section: Layers Of Digital Twin Architecturementioning

confidence: 99%

Exploring Digital Twin-based Fault Monitoring: Challenges and Opportunities

Bofill,

Abisado,

Sampedro

2023

Preprint

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High efficiency and safety are critical factors in ensuring optimal performance and reliability of systems and equipment across various industries. Fault Monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system’s health and enabling proactive maintenance and decision-making.

show abstract

Integrating the Digital Twin of a Shop Floor Conveyor in the Manufacturing Control System

Cited by 19 publications

References 14 publications

MES-integrated digital twin frameworks

MES-integrated digital twin frameworks

Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities

Exploring Digital Twin-based Fault Monitoring: Challenges and Opportunities

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