Active Thermometry Based DS18B20 Temperature Sensor Network for Offshore Pipeline Scour Monitoring Using K-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...
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Veröffentlicht in: | International journal of distributed sensor networks 2013-01, Vol.2013 (-), p.1-11 |
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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. |
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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.</description><identifier>ISSN: 1550-1329</identifier><identifier>ISSN: 1550-1477</identifier><identifier>EISSN: 1550-1477</identifier><identifier>DOI: 10.1155/2013/852090</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Puplishing Corporation</publisher><subject>Algorithms ; Civil engineering ; Experiments ; Fourier transforms ; Heat ; Monitoring systems ; Networks ; Offshore ; Offshore construction ; Offshore engineering ; Offshore structures ; Pipelines ; Sensors ; Studies ; Technological change</subject><ispartof>International journal of distributed sensor networks, 2013-01, Vol.2013 (-), p.1-11</ispartof><rights>2013 Xuefeng Zhao et al.</rights><rights>Copyright © 2013 Xuefeng Zhao et al. Xuefeng Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c446t-c90f19ef534056e3a998ff93660b629283f44e57d01f9dc28ec9a0dcef04d7313</citedby><cites>FETCH-LOGICAL-c446t-c90f19ef534056e3a998ff93660b629283f44e57d01f9dc28ec9a0dcef04d7313</cites><orcidid>0000-0002-0344-8981 ; 0000-0002-1704-4021</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,27924,27925</link.rule.ids></links><search><creatorcontrib>Zhao, Xue-Feng</creatorcontrib><creatorcontrib>Li, Weijie</creatorcontrib><creatorcontrib>Zhou, Lei</creatorcontrib><creatorcontrib>Song, Gang-Bing</creatorcontrib><creatorcontrib>Ba, Qin</creatorcontrib><creatorcontrib>Ou, Jinping</creatorcontrib><title>Active Thermometry Based DS18B20 Temperature Sensor Network for Offshore Pipeline Scour Monitoring Using K-Means Clustering Algorithm</title><title>International journal of distributed sensor networks</title><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.</description><subject>Algorithms</subject><subject>Civil engineering</subject><subject>Experiments</subject><subject>Fourier transforms</subject><subject>Heat</subject><subject>Monitoring systems</subject><subject>Networks</subject><subject>Offshore</subject><subject>Offshore construction</subject><subject>Offshore engineering</subject><subject>Offshore structures</subject><subject>Pipelines</subject><subject>Sensors</subject><subject>Studies</subject><subject>Technological change</subject><issn>1550-1329</issn><issn>1550-1477</issn><issn>1550-1477</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>AFRWT</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNptkU1P3DAQhqMKpPLRE-dKlrggVYHxR5z4uGxLQeWjEss5cp3xbiCJF9sp4gf0f-NlS4sQl5nRzKN3ZvRm2R6FQ0qL4ogB5UdVwUDBh2wrdSCnoiw3XmrO1MdsO4RbAC6ZpFvZn4mJ7W8kswX63vUY_SM51gEb8vWaVscMyAz7JXodR4_kGofgPLnE-OD8HbGpvrI2LFya_WyX2LVDgowbPblwQxudb4c5uQmr-CO_QD0EMu3GEPF5MOnmiYiLfjfbtLoL-Olv3sluTr7Npqf5-dX3s-nkPDdCyJgbBZYqtAUXUEjkWqnKWsWlhF-SKVZxKwQWZQPUqsawCo3S0Bi0IJqSU76THax1l97djxhi3bfBYNfpAd0YaiqkKERJQSZ0_w16m94a0nWJYhVwUSmeqC9ryngXgkdbL33ba_9YU6hXltQrS-q1Jf_XBz3HV3rvop_XKCY1tPqfbgGcV5w_AQzyk38</recordid><startdate>20130101</startdate><enddate>20130101</enddate><creator>Zhao, Xue-Feng</creator><creator>Li, Weijie</creator><creator>Zhou, Lei</creator><creator>Song, Gang-Bing</creator><creator>Ba, Qin</creator><creator>Ou, Jinping</creator><general>Hindawi Puplishing Corporation</general><general>SAGE Publications</general><general>Sage Publications Ltd</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>AFRWT</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7SP</scope><scope>7U5</scope><scope>7XB</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-0344-8981</orcidid><orcidid>https://orcid.org/0000-0002-1704-4021</orcidid></search><sort><creationdate>20130101</creationdate><title>Active Thermometry Based DS18B20 Temperature Sensor Network for Offshore Pipeline Scour Monitoring Using K-Means Clustering Algorithm</title><author>Zhao, Xue-Feng ; 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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.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Puplishing Corporation</pub><doi>10.1155/2013/852090</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-0344-8981</orcidid><orcidid>https://orcid.org/0000-0002-1704-4021</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Civil engineering Experiments Fourier transforms Heat Monitoring systems Networks Offshore Offshore construction Offshore engineering Offshore structures Pipelines Sensors Studies Technological change |
title | Active Thermometry Based DS18B20 Temperature Sensor Network for Offshore Pipeline Scour Monitoring Using K-Means Clustering Algorithm |
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