Data management for structural integrity assessment of offshore wind turbine support structures: data cleansing and missing data imputation. (1st February 2019) - Martinez-Luengo, Maria; Shafiee, Mahmood; Kolios, Athanasios
Abstract: 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. Highlights: A four-step methodology for the effective data management of SHMS of OWTs was developed. The applicability of this methodology was tested employing real SHM data from three operational WTs. Noise cleansing and missing data imputation were employed to increase accuracy the fatigue assessment. After cleansing took place, missing data was imputed with an average absolute error of 2.1% in 95% of cases. Fatigue is underestimated not only when data cleansing is not carried out but also when it is carried out but MDI is not.