Active Thermometry Based DS18B20 Temperature Sensor Network for Offshore Pipeline Scour Monitoring Using -Means Clustering Algorithm
This work presents an offshore pipeline scour monitoring sensor network system based on active thermometry. The system consists of thermal cables, data acquisition unit, and data processing unit. As the thermal cables emit heats, the distributed DS18B20 digital temperature sensors record temperature...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2013-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/852090 |
| _version_ | 1797706376069251072 |
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| author | Xuefeng Zhao Weijie Li Lei Zhou Gang-Bing Song Qin Ba Jinping Ou |
| author_facet | Xuefeng Zhao Weijie Li Lei Zhou Gang-Bing Song Qin Ba Jinping Ou |
| author_sort | Xuefeng Zhao |
| collection | DOAJ |
| description | This work presents an offshore pipeline scour monitoring sensor network system based on active thermometry. The system consists of thermal cables, data acquisition unit, and data processing unit. As the thermal cables emit heats, the distributed DS18B20 digital temperature sensors record temperature information over time. The scour-induced exposure and free spanning can be identified by analyzing the temperature curves. Pipeline exposure and free-spanning experiments were carried out in laboratory, whose results show that the system is able to give overall information about the development of pipeline scour. Difference values analysis reveals the changing patterns of heat transfer behavior for line heat source in sediment and water scenarios. Two features, magnitude and temporal instability, are extracted from temperature curves to better differentiate sediment and water scenarios. Based on these two features, K -means clustering algorithm is adopted for pattern classification of the system, which was implemented in MATLAB and facilitated the automatic detection of the scour monitoring sensor network system. The proposed sensor network has the advantages of low cost, high precision and construction flexiblility, providing a promising approach for offshore pipeline scour monitoring, especially suitable for nearshore environment. |
| first_indexed | 2024-03-12T05:50:19Z |
| format | Article |
| id | doaj.art-32d4def5e7b248f3bad3a41268e585ec |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T05:50:19Z |
| publishDate | 2013-06-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-32d4def5e7b248f3bad3a41268e585ec2023-09-03T05:09:46ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772013-06-01910.1155/2013/852090Active Thermometry Based DS18B20 Temperature Sensor Network for Offshore Pipeline Scour Monitoring Using -Means Clustering AlgorithmXuefeng Zhao0Weijie Li1Lei Zhou2Gang-Bing Song3Qin Ba4Jinping Ou5 School of Civil Engineering, Dalian University of Technology, Dalian 116024, China School of Civil Engineering, Dalian University of Technology, Dalian 116024, China Engineering Company, Offshore Oil Engineering Co., Ltd., Tianjin 300451, China Department of Mechanical Engineering, University of Houston, Houston, TX 77204-4006, USA School of Civil Engineering, Dalian University of Technology, Dalian 116024, China School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, ChinaThis work presents an offshore pipeline scour monitoring sensor network system based on active thermometry. The system consists of thermal cables, data acquisition unit, and data processing unit. As the thermal cables emit heats, the distributed DS18B20 digital temperature sensors record temperature information over time. The scour-induced exposure and free spanning can be identified by analyzing the temperature curves. Pipeline exposure and free-spanning experiments were carried out in laboratory, whose results show that the system is able to give overall information about the development of pipeline scour. Difference values analysis reveals the changing patterns of heat transfer behavior for line heat source in sediment and water scenarios. Two features, magnitude and temporal instability, are extracted from temperature curves to better differentiate sediment and water scenarios. Based on these two features, K -means clustering algorithm is adopted for pattern classification of the system, which was implemented in MATLAB and facilitated the automatic detection of the scour monitoring sensor network system. The proposed sensor network has the advantages of low cost, high precision and construction flexiblility, providing a promising approach for offshore pipeline scour monitoring, especially suitable for nearshore environment.https://doi.org/10.1155/2013/852090 |
| spellingShingle | Xuefeng Zhao Weijie Li Lei Zhou Gang-Bing Song Qin Ba Jinping Ou Active Thermometry Based DS18B20 Temperature Sensor Network for Offshore Pipeline Scour Monitoring Using -Means Clustering Algorithm International Journal of Distributed Sensor Networks |
| title | Active Thermometry Based DS18B20 Temperature Sensor Network for Offshore Pipeline Scour Monitoring Using -Means Clustering Algorithm |
| title_full | Active Thermometry Based DS18B20 Temperature Sensor Network for Offshore Pipeline Scour Monitoring Using -Means Clustering Algorithm |
| title_fullStr | Active Thermometry Based DS18B20 Temperature Sensor Network for Offshore Pipeline Scour Monitoring Using -Means Clustering Algorithm |
| title_full_unstemmed | Active Thermometry Based DS18B20 Temperature Sensor Network for Offshore Pipeline Scour Monitoring Using -Means Clustering Algorithm |
| title_short | Active Thermometry Based DS18B20 Temperature Sensor Network for Offshore Pipeline Scour Monitoring Using -Means Clustering Algorithm |
| title_sort | active thermometry based ds18b20 temperature sensor network for offshore pipeline scour monitoring using means clustering algorithm |
| url | https://doi.org/10.1155/2013/852090 |
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