IoT-Based SHM Using Digital Twins for Interoperable and Scalable Decentralized Smart Sensing Systems
Advancing digitalization is reaching the realm of lightweight construction and structural–mechanical components. Through the synergistic combination of distributed sensors and intelligent evaluation algorithms, traditional structures evolve into smart sensing systems. In this context, Structural Hea...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/15/3/121 |
| _version_ | 1797240644064772096 |
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| author | Jiahang Chen Jan Reitz Rebecca Richstein Kai-Uwe Schröder Jürgen Roßmann |
| author_facet | Jiahang Chen Jan Reitz Rebecca Richstein Kai-Uwe Schröder Jürgen Roßmann |
| author_sort | Jiahang Chen |
| collection | DOAJ |
| description | Advancing digitalization is reaching the realm of lightweight construction and structural–mechanical components. Through the synergistic combination of distributed sensors and intelligent evaluation algorithms, traditional structures evolve into smart sensing systems. In this context, Structural Health Monitoring (SHM) plays a key role in managing potential risks to human safety and environmental integrity due to structural failures by providing analysis, localization, and records of the structure’s loading and damaging conditions. The establishment of networks between sensors and data-processing units via Internet of Things (IoT) technologies is an elementary prerequisite for the integration of SHM into smart sensing systems. However, this integrating of SHM faces significant restrictions due to scalability challenges of smart sensing systems and IoT-specific issues, including communication security and interoperability. To address the issue, this paper presents a comprehensive methodological framework aimed at facilitating the scalable integration of objects ranging from components via systems to clusters into SHM systems. Furthermore, we detail a prototypical implementation of the conceptually developed framework, demonstrating a structural component and its corresponding Digital Twin. Here, real-time capable deformation and strain-based monitoring of the structure are achieved, showcasing the practical applicability of the proposed framework. |
| first_indexed | 2024-04-24T18:10:42Z |
| format | Article |
| id | doaj.art-d21952732b5d4bbb8efbac2d6c40fc56 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-04-24T18:10:42Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-d21952732b5d4bbb8efbac2d6c40fc562024-03-27T13:46:50ZengMDPI AGInformation2078-24892024-02-0115312110.3390/info15030121IoT-Based SHM Using Digital Twins for Interoperable and Scalable Decentralized Smart Sensing SystemsJiahang Chen0Jan Reitz1Rebecca Richstein2Kai-Uwe Schröder3Jürgen Roßmann4Institute for Man-Machine Interaction, RWTH Aachen University, Ahornstr. 55, 52074 Aachen, GermanyInstitute for Man-Machine Interaction, RWTH Aachen University, Ahornstr. 55, 52074 Aachen, GermanyInstitute of Structural Mechanics and Lightweight Design, RWTH Aachen University, Wüllnerstraße 7, 52062 Aachen, GermanyInstitute of Structural Mechanics and Lightweight Design, RWTH Aachen University, Wüllnerstraße 7, 52062 Aachen, GermanyInstitute for Man-Machine Interaction, RWTH Aachen University, Ahornstr. 55, 52074 Aachen, GermanyAdvancing digitalization is reaching the realm of lightweight construction and structural–mechanical components. Through the synergistic combination of distributed sensors and intelligent evaluation algorithms, traditional structures evolve into smart sensing systems. In this context, Structural Health Monitoring (SHM) plays a key role in managing potential risks to human safety and environmental integrity due to structural failures by providing analysis, localization, and records of the structure’s loading and damaging conditions. The establishment of networks between sensors and data-processing units via Internet of Things (IoT) technologies is an elementary prerequisite for the integration of SHM into smart sensing systems. However, this integrating of SHM faces significant restrictions due to scalability challenges of smart sensing systems and IoT-specific issues, including communication security and interoperability. To address the issue, this paper presents a comprehensive methodological framework aimed at facilitating the scalable integration of objects ranging from components via systems to clusters into SHM systems. Furthermore, we detail a prototypical implementation of the conceptually developed framework, demonstrating a structural component and its corresponding Digital Twin. Here, real-time capable deformation and strain-based monitoring of the structure are achieved, showcasing the practical applicability of the proposed framework.https://www.mdpi.com/2078-2489/15/3/121IoTsmart sensing systemsstructural health monitoringdigital twins |
| spellingShingle | Jiahang Chen Jan Reitz Rebecca Richstein Kai-Uwe Schröder Jürgen Roßmann IoT-Based SHM Using Digital Twins for Interoperable and Scalable Decentralized Smart Sensing Systems Information IoT smart sensing systems structural health monitoring digital twins |
| title | IoT-Based SHM Using Digital Twins for Interoperable and Scalable Decentralized Smart Sensing Systems |
| title_full | IoT-Based SHM Using Digital Twins for Interoperable and Scalable Decentralized Smart Sensing Systems |
| title_fullStr | IoT-Based SHM Using Digital Twins for Interoperable and Scalable Decentralized Smart Sensing Systems |
| title_full_unstemmed | IoT-Based SHM Using Digital Twins for Interoperable and Scalable Decentralized Smart Sensing Systems |
| title_short | IoT-Based SHM Using Digital Twins for Interoperable and Scalable Decentralized Smart Sensing Systems |
| title_sort | iot based shm using digital twins for interoperable and scalable decentralized smart sensing systems |
| topic | IoT smart sensing systems structural health monitoring digital twins |
| url | https://www.mdpi.com/2078-2489/15/3/121 |
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