Application of SHM using an autonomous sensor network

Richard Loendersloot, F.B.A. Schiphorst, T.G.H. Basten, Tiedo Tinga

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
29 Downloads (Pure)

Abstract

Assessment of the condition of a structure in operation and subsequently its remaining maintenance free service time, is of increasing importance for various fields of engineering. The main drivers for this are cost effectiveness, increased system reliability, system safety and reduced environmental impact. The current generation of monitoring systems relies on active, power intensive excitation and wired communication. Systems based on operational vibrations of the structure and employing a network of smart, autonomously operating and wireless sensors offer new possibilities, but also pose new constraints. Damage identification methods are therefore sought that combine local low power usage and low data transmission with a high reliability. The focus of this paper is on operational vibrations and modal based Structural Health Monitoring damage identification methods, applied in large civil structures such as wind turbine towers and bridge decks and, to a lower extent, in large composite structures. Three methods are compared, both experimentally and numerically: Peak Picking (PP), Random Decrement – Frequency Domain Decomposition (RD–FDD) and Random Decrement – covariance based Stochastic Subspace Identification (RD–SSIcov). The RD–FDD method is found to be a suitable method for modal based damage identification, given the restrictions on smart wireless sensor networks.
Original languageEnglish
Title of host publicationProceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM
EditorsF.K. Chang
Place of PublicationStanford, CA, U.S.A.
PublisherDEStech Publications, Inc
Pages854-861
ISBN (Print)978-1-60595-115-7
Publication statusPublished - 10 Sep 2013
Event9th International Workshop on Structural Health Monitoring, IWSHM 2013 - Stanford University, Stanford, United States
Duration: 10 Sep 201312 Sep 2013
Conference number: 9
http://web.stanford.edu/group/sacl/workshop/IWSHM2013/

Publication series

Name
PublisherDEStech publications, inc

Workshop

Workshop9th International Workshop on Structural Health Monitoring, IWSHM 2013
Abbreviated titleIWSHM
CountryUnited States
CityStanford
Period10/09/1312/09/13
Internet address

Fingerprint

Sensor networks
Domain decomposition methods
Bridge decks
Structural health monitoring
Cost effectiveness
Composite structures
Security systems
Wind turbines
Data communication systems
Towers
Environmental impact
Wireless sensor networks
Decomposition
Monitoring
Communication
Sensors

Keywords

  • METIS-295874
  • IR-87244

Cite this

Loendersloot, R., Schiphorst, F. B. A., Basten, T. G. H., & Tinga, T. (2013). Application of SHM using an autonomous sensor network. In F. K. Chang (Ed.), Proceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM (pp. 854-861). Stanford, CA, U.S.A.: DEStech Publications, Inc.
Loendersloot, Richard ; Schiphorst, F.B.A. ; Basten, T.G.H. ; Tinga, Tiedo. / Application of SHM using an autonomous sensor network. Proceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM. editor / F.K. Chang. Stanford, CA, U.S.A. : DEStech Publications, Inc, 2013. pp. 854-861
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abstract = "Assessment of the condition of a structure in operation and subsequently its remaining maintenance free service time, is of increasing importance for various fields of engineering. The main drivers for this are cost effectiveness, increased system reliability, system safety and reduced environmental impact. The current generation of monitoring systems relies on active, power intensive excitation and wired communication. Systems based on operational vibrations of the structure and employing a network of smart, autonomously operating and wireless sensors offer new possibilities, but also pose new constraints. Damage identification methods are therefore sought that combine local low power usage and low data transmission with a high reliability. The focus of this paper is on operational vibrations and modal based Structural Health Monitoring damage identification methods, applied in large civil structures such as wind turbine towers and bridge decks and, to a lower extent, in large composite structures. Three methods are compared, both experimentally and numerically: Peak Picking (PP), Random Decrement – Frequency Domain Decomposition (RD–FDD) and Random Decrement – covariance based Stochastic Subspace Identification (RD–SSIcov). The RD–FDD method is found to be a suitable method for modal based damage identification, given the restrictions on smart wireless sensor networks.",
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Loendersloot, R, Schiphorst, FBA, Basten, TGH & Tinga, T 2013, Application of SHM using an autonomous sensor network. in FK Chang (ed.), Proceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM. DEStech Publications, Inc, Stanford, CA, U.S.A., pp. 854-861, 9th International Workshop on Structural Health Monitoring, IWSHM 2013, Stanford, United States, 10/09/13.

Application of SHM using an autonomous sensor network. / Loendersloot, Richard; Schiphorst, F.B.A.; Basten, T.G.H.; Tinga, Tiedo.

Proceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM. ed. / F.K. Chang. Stanford, CA, U.S.A. : DEStech Publications, Inc, 2013. p. 854-861.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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T1 - Application of SHM using an autonomous sensor network

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Y1 - 2013/9/10

N2 - Assessment of the condition of a structure in operation and subsequently its remaining maintenance free service time, is of increasing importance for various fields of engineering. The main drivers for this are cost effectiveness, increased system reliability, system safety and reduced environmental impact. The current generation of monitoring systems relies on active, power intensive excitation and wired communication. Systems based on operational vibrations of the structure and employing a network of smart, autonomously operating and wireless sensors offer new possibilities, but also pose new constraints. Damage identification methods are therefore sought that combine local low power usage and low data transmission with a high reliability. The focus of this paper is on operational vibrations and modal based Structural Health Monitoring damage identification methods, applied in large civil structures such as wind turbine towers and bridge decks and, to a lower extent, in large composite structures. Three methods are compared, both experimentally and numerically: Peak Picking (PP), Random Decrement – Frequency Domain Decomposition (RD–FDD) and Random Decrement – covariance based Stochastic Subspace Identification (RD–SSIcov). The RD–FDD method is found to be a suitable method for modal based damage identification, given the restrictions on smart wireless sensor networks.

AB - Assessment of the condition of a structure in operation and subsequently its remaining maintenance free service time, is of increasing importance for various fields of engineering. The main drivers for this are cost effectiveness, increased system reliability, system safety and reduced environmental impact. The current generation of monitoring systems relies on active, power intensive excitation and wired communication. Systems based on operational vibrations of the structure and employing a network of smart, autonomously operating and wireless sensors offer new possibilities, but also pose new constraints. Damage identification methods are therefore sought that combine local low power usage and low data transmission with a high reliability. The focus of this paper is on operational vibrations and modal based Structural Health Monitoring damage identification methods, applied in large civil structures such as wind turbine towers and bridge decks and, to a lower extent, in large composite structures. Three methods are compared, both experimentally and numerically: Peak Picking (PP), Random Decrement – Frequency Domain Decomposition (RD–FDD) and Random Decrement – covariance based Stochastic Subspace Identification (RD–SSIcov). The RD–FDD method is found to be a suitable method for modal based damage identification, given the restrictions on smart wireless sensor networks.

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Loendersloot R, Schiphorst FBA, Basten TGH, Tinga T. Application of SHM using an autonomous sensor network. In Chang FK, editor, Proceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM. Stanford, CA, U.S.A.: DEStech Publications, Inc. 2013. p. 854-861