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Data Management for Structural Integrity Assessment of Offshore Wind Turbine Support Structures: Data Cleansing and Missing Data Imputation

Martinez-Luengo, Maria, Shafiee, Mahmood, Kolios, Athanasios (2019) Data Management for Structural Integrity Assessment of Offshore Wind Turbine Support Structures: Data Cleansing and Missing Data Imputation. Ocean Engineering, 173 . pp. 867-883. ISSN 0029-8018. (doi:10.1016/j.oceaneng.2019.01.003) (KAR id:79679)

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Structural 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.

Item Type: Article
DOI/Identification number: 10.1016/j.oceaneng.2019.01.003
Uncontrolled keywords: Structural health monitoring (SHM); Offshore wind; Data synchronisation; Noise cleansing; Missing data imputation; Artificial neural network (ANN)
Subjects: T Technology > TJ Mechanical engineering and machinery
V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Mahmood Shafiee
Date Deposited: 23 Jan 2020 00:28 UTC
Last Modified: 16 Feb 2021 14:10 UTC
Resource URI: (The current URI for this page, for reference purposes)
Shafiee, Mahmood:
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