Browsing by Author "Martinez Luengo, Maria"
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Item Open Access Advanced structural health monitoring strategies for condition-based maintenance planning of offshore wind turbine support structures(2019-04) Martinez Luengo, Maria; Shafiee, Mahmood; Kolios, Athanasios; Engineering and Physical Sciences (EPSRC)Condition-based maintenance strategies need to be adopted as distance-to-shore and water depth increase in the offshore wind industry. The aim of the research presented herein is to develop advance structural health monitoring strategies that enhance the condition-based maintenance of offshore wind turbine support structures. The focus is on the selection of technologies, the implementation process, the analysis of the asset’s structural response under complex loading, the economic justification for structural health monitoring implementation and the effective structural health monitoring data analysis. Research activities consist of the provision of a comprehensive study for structural health monitoring technologies’ utilisation in the offshore wind industry. This is followed by parametric structural modelling, simulation and validation of an operational offshore wind turbine tower, support structure and soil-structure interaction, using commercial software. The evaluation of the asset’s response under complex loading subject to design changes and failure mechanisms is also undertaken. A combination of existing and newly developed methodologies is deployed for the effective data management of structural health monitoring systems and validated with industrial data for the case of strain monitoring. These include unsupervised learning algorithms (neural networks), deterministic and probabilistic methods for noise cleansing and missing data imputation. Guidelines for the structural health monitoring implementation from design stage of a wind farm are proposed and applied to a baseline scenario. This is utilised to assess the economic impact that structural health monitoring has in the lifecycle of the assets. The achieved results show that the implementation of structural health monitoring in offshore wind turbine following the Statistical Pattern Recognition paradigm and the proposed guidelines has the potential to reduce the Operational Expenditure. This reduction is much greater than the cost associated with the implementation of these systems. Monitoring from the commissioning of the assets is crucial for the system’s calibration and establishing thresholds. The developed noise cleansing and missing data imputation methodologies can successfully be employed together to produce more complete low-disturbed datasets.Item Open Access Data management for structural integrity assessment of offshore wind turbine support structures: data cleansing and missing data imputation(Elsevier, 2019-02-05) Martinez Luengo, Maria; Shafiee, Mahmood; Kolios, AthanasiosStructural 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.