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Denoising of structural health monitoring data: method and coding

Denoising of structural health monitoring data: method and coding
Autor(en): , , , , ORCID
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Beitrag für IABSE Congress: Structural Engineering for Future Societal Needs, Ghent, Belgium, 22-24 September 2021, veröffentlicht in , S. 504-510
DOI: 10.2749/ghent.2021.0504
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Numerous denoising approaches have already been presented to handle the noise in measured data of structural health monitoring systems. However, the performances and features of these existing meth...
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Bibliografische Angaben

Autor(en): (Department of Bridge Engineering, Tongji University, Shanghai 200092, China)
(Department of Bridge Engineering, Tongji University, Shanghai 200092, China)
(Research Institute of Highway Ministry of Transport, Beijing 100088, China)
(Research Institute of Highway Ministry of Transport, Beijing 100088, China)
ORCID (State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌)
Medium: Tagungsbeitrag
Sprache(n): Englisch
Tagung: IABSE Congress: Structural Engineering for Future Societal Needs, Ghent, Belgium, 22-24 September 2021
Veröffentlicht in:
Seite(n): 504-510 Anzahl der Seiten (im PDF): 7
Seite(n): 504-510
Anzahl der Seiten (im PDF): 7
DOI: 10.2749/ghent.2021.0504
Abstrakt:

Numerous denoising approaches have already been presented to handle the noise in measured data of structural health monitoring systems. However, the performances and features of these existing methods applied in real data-set are not clear enough yet, where the noise is not known in advance. Therefore, based on the measured structural response data from a tied-arch bridge in China, six common data denoising methods are selected for a comparative study. The denoising effects are evaluated based on spectrums. Conclusions on the applicable situations and robustness of involved methods are given. A corresponding program is also developed. This study can provide references for applying the denoising methods in real structural health monitoring system data-set.
Stichwörter:
Lärm Bandbreiten-Analyse
Copyright: © 2021 International Association for Bridge and Structural Engineering (IABSE)
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