Data management for structural integrity assessment of offshore wind turbine support structures: data cleansing and missing data imputation

Simple item page
dc.contributor.authorMartinez Luengo, Maria
dc.contributor.authorShafiee, Mahmood
dc.contributor.authorKolios, Athanasios
dc.date.accessioned2019-03-18T16:42:53Z
dc.date.available2019-03-18T16:42:53Z
dc.date.issued2019-02-05
dc.description.abstractStructural Health Monitoring (SHM) and Condition Monitoring (CM) Systems are currently utilised to collect data from offshore wind turbines (OWTs), to enhance the accurate estimation of their operational performance. However, industry accepted practices for effectively managing the information that these systems provide have not been widely established yet. This paper presents a four-step methodological framework for the effective data management of SHM systems of OWTs and illustrates its applicability in real-time continuous data collected from three operational units, with the aim of utilising more complete and accurate datasets for fatigue life assessment of support structures. Firstly, a time-efficient synchronisation method that enables the continuous monitoring of these systems is presented, followed by a novel approach to noise cleansing and the posterior missing data imputation (MDI). By the implementation of these techniques those data-points containing excessive noise are removed from the dataset (Step 2), advanced numerical tools are employed to regenerate missing data (Step 3) and fatigue is estimated for the results of these two methodologies (Step 4). Results show that after cleansing, missing data can be imputed with an average absolute error of 2.1%, while this error is kept within the [+ 15.2%−11.0%] range in 95% of cases. Furthermore, only 0.15% of the imputed data fell outside the noise thresholds. Fatigue is found to be underestimated both, when data cleansing does not take place and when it takes place but MDI does not. This makes this novel methodology an enhancement to conventional structural integrity assessment techniques that do not employ continuous datasets in their analyses.en_UK
dc.identifier.citationMartinez-Luengo, M, Shafiee M and Kolios A., Data management for structural integrity assessment of offshore wind turbine support structures: data cleansing and missing data imputation, Ocean Engineering, Volume 173, Issue February 2019, pp.867-883.en_UK
dc.identifier.issn0029-8018
dc.identifier.urihttps://doi.org/10.1016/j.oceaneng.2019.01.003
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/13998
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectArtificial neural network (ANN)en_UK
dc.subjectMissing data imputationen_UK
dc.subjectNoise cleansingen_UK
dc.subjectData synchronisationen_UK
dc.subjectOffshore winden_UK
dc.subjectStructural health monitoring (SHM)en_UK
dc.titleData management for structural integrity assessment of offshore wind turbine support structures: data cleansing and missing data imputationen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Data_management_for_structural_integrity_assessment_of_offshore_wind_ turbine-2019.pdf
Size:
3.5 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SWEE)