Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic Sensors
The application of IoT (Internet of Things) technology to the health monitoring of expansion joints is of great importance in enhancing the efficiency of bridge expansion joint maintenance. In this study, a low-power, high-efficiency, end-to-cloud coordinated monitoring system analyzes acoustic sign...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/11/5090 |
| _version_ | 1797596787329990656 |
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| author | Xulong Zhang Zihao Cheng Li Du Yuan Du |
| author_facet | Xulong Zhang Zihao Cheng Li Du Yuan Du |
| author_sort | Xulong Zhang |
| collection | DOAJ |
| description | The application of IoT (Internet of Things) technology to the health monitoring of expansion joints is of great importance in enhancing the efficiency of bridge expansion joint maintenance. In this study, a low-power, high-efficiency, end-to-cloud coordinated monitoring system analyzes acoustic signals to identify faults in bridge expansion joints. To address the issue of scarce authentic data related to bridge expansion joint failures, an expansion joint damage simulation data collection platform is established for well-annotated datasets. Based on this, a progressive two-level classifier mechanism is proposed, combining template matching based on AMPD (Automatic Peak Detection) and deep learning algorithms based on VMD (Variational Mode Decomposition), denoising, and utilizing edge and cloud computing power efficiently. The simulation-based datasets were used to test the two-level algorithm, with the first-level edge-end template matching algorithm achieving fault detection rates of 93.3% and the second-level cloud-based deep learning algorithm achieving classification accuracy of 98.4%. The proposed system in this paper has demonstrated efficient performance in monitoring the health of expansion joints, according to the aforementioned results. |
| first_indexed | 2024-03-11T02:57:58Z |
| format | Article |
| id | doaj.art-b7db3653a9b441188bccb2ad6ea61071 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:57:58Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b7db3653a9b441188bccb2ad6ea610712023-11-18T08:32:24ZengMDPI AGSensors1424-82202023-05-012311509010.3390/s23115090Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic SensorsXulong Zhang0Zihao Cheng1Li Du2Yuan Du3School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, ChinaSchool of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Electronic Science and Engineering, Nanjing University, Nanjing 210023, ChinaSchool of Electronic Science and Engineering, Nanjing University, Nanjing 210023, ChinaThe application of IoT (Internet of Things) technology to the health monitoring of expansion joints is of great importance in enhancing the efficiency of bridge expansion joint maintenance. In this study, a low-power, high-efficiency, end-to-cloud coordinated monitoring system analyzes acoustic signals to identify faults in bridge expansion joints. To address the issue of scarce authentic data related to bridge expansion joint failures, an expansion joint damage simulation data collection platform is established for well-annotated datasets. Based on this, a progressive two-level classifier mechanism is proposed, combining template matching based on AMPD (Automatic Peak Detection) and deep learning algorithms based on VMD (Variational Mode Decomposition), denoising, and utilizing edge and cloud computing power efficiently. The simulation-based datasets were used to test the two-level algorithm, with the first-level edge-end template matching algorithm achieving fault detection rates of 93.3% and the second-level cloud-based deep learning algorithm achieving classification accuracy of 98.4%. The proposed system in this paper has demonstrated efficient performance in monitoring the health of expansion joints, according to the aforementioned results.https://www.mdpi.com/1424-8220/23/11/5090IoTacoustic sensorfault diagnose and classificationend-to-cloud coordinated |
| spellingShingle | Xulong Zhang Zihao Cheng Li Du Yuan Du Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic Sensors Sensors IoT acoustic sensor fault diagnose and classification end-to-cloud coordinated |
| title | Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic Sensors |
| title_full | Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic Sensors |
| title_fullStr | Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic Sensors |
| title_full_unstemmed | Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic Sensors |
| title_short | Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic Sensors |
| title_sort | progressive classifier mechanism for bridge expansion joint health status monitoring system based on acoustic sensors |
| topic | IoT acoustic sensor fault diagnose and classification end-to-cloud coordinated |
| url | https://www.mdpi.com/1424-8220/23/11/5090 |
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