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Detection and elimination of strain reading anomalies in distributed strain sensing readings
Citation Link: https://doi.org/10.15480/882.13497
Publikationstyp
Conference Paper
Date Issued
2024-09-18
Sprache
English
Author(s)
TORE-DOI
Start Page
131
End Page
138
Citation
35. Forum Bauinformatik, fbi 2024: 131-138
Contribution to Conference
Publisher
Technische Universität Hamburg, Institut für Digitales und Autonomes Bauen
Peer Reviewed
true
Distributed strain sensing (DSS) is increasingly used in structural health monitoring (SHM). Unlike conventional measurement technology, DSS allows for quasi-continuous measurement of strains with high spatial resolution, enabling the recording of global strain changes in the structure. However, DSS readings are subject to process-related interference, such as dropouts and strain reading anomalies (SRA). SRA can hinder data analysis during postprocessing or even falsify the results. Because of high amount of data produced by DSS, it is crucial to detect and remove SRA from DSS readings during automated preprocessing. Outlier removing methods from literature were analysed for their potential application to DSS readings. Based on this analysis, a sliding median z-score had been proposed, implemented in the fosanalysis framework and applied to various DSS readings for validation and to determine suitable parameters.
Subjects
Distributed Fibre Optic Sensors
Distributed Strain Sensing
Strain Reading Anomalies
DDC Class
620: Engineering
624: Civil Engineering, Environmental Engineering
519: Applied Mathematics, Probabilities
Publication version
publishedVersion
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Name
Detection and elimination of strain reading anomalies in distributed strain sensing.pdf
Type
Main Article
Size
1.04 MB
Format
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