DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tanner, NA | ko |
dc.contributor.author | Wait, JR | ko |
dc.contributor.author | Farrar, CR | ko |
dc.contributor.author | Sohn, Hoon | ko |
dc.date.accessioned | 2010-06-10T05:20:28Z | - |
dc.date.available | 2010-06-10T05:20:28Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2003-01 | - |
dc.identifier.citation | JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES, v.14, no.1, pp.43 - 56 | - |
dc.identifier.issn | 1045-389X | - |
dc.identifier.uri | http://hdl.handle.net/10203/18811 | - |
dc.description.abstract | System integration of an online structural health monitoring module was accomplished by coupling commercially available microclectro-mechanical system sensors and a wireless telemetry unit with damage detection firmware. To showcase the capabilities of the integrated monitoring module, a bolted frame structure was constructed, and the preload in one of the bolted joints was controlled by a piezoelectric stack actuator to simulate gradual deterioration of a bolted connection. Two separate damage detection algorithms were used to classify a joint as damaged or undamaged. First, a statistical process control algorithm was used to monitor the correlation of vibration data from two accelerometers mounted across a joint. Changes in correlation were used to detect damage to the joint. For each joint, data were processed locally on a microprocessor integrated with the wireless module, and the diagnosis result was remotely transmitted to the base monitoring station. Second, a more sophisticated damage detection algorithm combining time series analysis and statistical hypothesis testing was employed using a conventional wired data acquisition system to classify a joint on the demonstration structure as damaged or undamaged. | - |
dc.description.sponsorship | Funding for this project was provided by the Department ofEnergy through the internal funding program at Los Alamos National Laboratory known as Laboratory Directed Research and Development. The first author, Neal A. Tanner, was supported by a fellowship from the Fannie and John Hertz Foundation. The authors would also like to acknowledge Jason Hill in the Computer Science Department at the University of California, Berkeley for his consultation regarding programming ofthe motes. Finally, the authors acknowledge the contribution ofCrossb ow, Inc. in San Jose, CA for providing the Mote hardware. | en |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | SAGE PUBLICATIONS LTD | - |
dc.title | Structural health monitoring using modular wireless sensors | - |
dc.type | Article | - |
dc.identifier.wosid | 000183783100005 | - |
dc.identifier.scopusid | 2-s2.0-0042849259 | - |
dc.type.rims | ART | - |
dc.citation.volume | 14 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 43 | - |
dc.citation.endingpage | 56 | - |
dc.citation.publicationname | JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES | - |
dc.identifier.doi | 10.1177/104538903033641 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Sohn, Hoon | - |
dc.contributor.nonIdAuthor | Tanner, NA | - |
dc.contributor.nonIdAuthor | Wait, JR | - |
dc.contributor.nonIdAuthor | Farrar, CR | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | structural health monitoring | - |
dc.subject.keywordAuthor | wireless sensor module | - |
dc.subject.keywordAuthor | remote sensing | - |
dc.subject.keywordAuthor | real-time damage detection | - |
dc.subject.keywordAuthor | statistical process control | - |
dc.subject.keywordAuthor | statistical pattern recognition | - |
dc.subject.keywordAuthor | and time series analysis | - |
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