Advances in Data Pre-Processing Methods for Distributed Fiber Optic Strain Sensing
Because of their high spatial resolution over extended lengths, distributed fiber optic sensors (DFOS) enable us to monitor a wide range of structural effects and offer great potential for diverse structural health monitoring (SHM) applications. However, even under controlled conditions, the useful...
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7454 |
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| author | Bertram Richter Lisa Ulbrich Max Herbers Steffen Marx |
| author_facet | Bertram Richter Lisa Ulbrich Max Herbers Steffen Marx |
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| description | Because of their high spatial resolution over extended lengths, distributed fiber optic sensors (DFOS) enable us to monitor a wide range of structural effects and offer great potential for diverse structural health monitoring (SHM) applications. However, even under controlled conditions, the useful signal in distributed strain sensing (DSS) data can be concealed by different types of measurement principle-related disturbances: strain reading anomalies (SRAs), dropouts, and noise. These disturbances can render the extraction of information for SHM difficult or even impossible. Hence, cleaning the raw measurement data in a pre-processing stage is key for successful subsequent data evaluation and damage detection on engineering structures. To improve the capabilities of pre-processing procedures tailored to DSS data, characteristics and common remediation approaches for SRAs, dropouts, and noise are discussed. Four advanced pre-processing algorithms (geometric threshold method (GTM), outlier-specific correction procedure (OSCP), sliding modified <i>z</i>-score (SMZS), and the cluster filter) are presented. An artificial but realistic benchmark data set simulating different measurement scenarios is used to discuss the features of these algorithms. A flexible and modular pre-processing workflow is implemented and made available with the algorithms. Dedicated algorithms should be used to detect and remove SRAs. GTM, OSCP, and SMZS show promising results, and the sliding average is inappropriate for this purpose. The preservation of crack-induced strain peaks’ tips is imperative for reliable crack monitoring. |
| format | Article |
| id | doaj-art-5f8cb48d57934fd8bbeaf6d5fcaf9844 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-5f8cb48d57934fd8bbeaf6d5fcaf98442024-12-13T16:31:34ZengMDPI AGSensors1424-82202024-11-012423745410.3390/s24237454Advances in Data Pre-Processing Methods for Distributed Fiber Optic Strain SensingBertram Richter0Lisa Ulbrich1Max Herbers2Steffen Marx3Institute of Concrete Structures, TUD Dresden University of Technology, 01062 Dresden, GermanyHentschke Bau GmbH, Zeppelinstr. 15, 02625 Bautzen, GermanyInstitute of Concrete Structures, TUD Dresden University of Technology, 01062 Dresden, GermanyInstitute of Concrete Structures, TUD Dresden University of Technology, 01062 Dresden, GermanyBecause of their high spatial resolution over extended lengths, distributed fiber optic sensors (DFOS) enable us to monitor a wide range of structural effects and offer great potential for diverse structural health monitoring (SHM) applications. However, even under controlled conditions, the useful signal in distributed strain sensing (DSS) data can be concealed by different types of measurement principle-related disturbances: strain reading anomalies (SRAs), dropouts, and noise. These disturbances can render the extraction of information for SHM difficult or even impossible. Hence, cleaning the raw measurement data in a pre-processing stage is key for successful subsequent data evaluation and damage detection on engineering structures. To improve the capabilities of pre-processing procedures tailored to DSS data, characteristics and common remediation approaches for SRAs, dropouts, and noise are discussed. Four advanced pre-processing algorithms (geometric threshold method (GTM), outlier-specific correction procedure (OSCP), sliding modified <i>z</i>-score (SMZS), and the cluster filter) are presented. An artificial but realistic benchmark data set simulating different measurement scenarios is used to discuss the features of these algorithms. A flexible and modular pre-processing workflow is implemented and made available with the algorithms. Dedicated algorithms should be used to detect and remove SRAs. GTM, OSCP, and SMZS show promising results, and the sliding average is inappropriate for this purpose. The preservation of crack-induced strain peaks’ tips is imperative for reliable crack monitoring.https://www.mdpi.com/1424-8220/24/23/7454structural health monitoringdistributed fiber optic sensingdata qualityautomationdata pre-processingdata filtering |
| spellingShingle | Bertram Richter Lisa Ulbrich Max Herbers Steffen Marx Advances in Data Pre-Processing Methods for Distributed Fiber Optic Strain Sensing Sensors structural health monitoring distributed fiber optic sensing data quality automation data pre-processing data filtering |
| title | Advances in Data Pre-Processing Methods for Distributed Fiber Optic Strain Sensing |
| title_full | Advances in Data Pre-Processing Methods for Distributed Fiber Optic Strain Sensing |
| title_fullStr | Advances in Data Pre-Processing Methods for Distributed Fiber Optic Strain Sensing |
| title_full_unstemmed | Advances in Data Pre-Processing Methods for Distributed Fiber Optic Strain Sensing |
| title_short | Advances in Data Pre-Processing Methods for Distributed Fiber Optic Strain Sensing |
| title_sort | advances in data pre processing methods for distributed fiber optic strain sensing |
| topic | structural health monitoring distributed fiber optic sensing data quality automation data pre-processing data filtering |
| url | https://www.mdpi.com/1424-8220/24/23/7454 |
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