Predictive Maintenance of Floating Offshore Wind Turbine Mooring Lines using Deep Neural Networks

The recent massive deployment of onshore wind farms has caused controversy to arise mainly around the issues of land occupation, noise and visual pollution and impact on wildlife. Fixed offshore turbines, albeit beneficial in those aspects, become economically unfeasible when installed far away from coastlines. The possibility of installing floating offshore wind turbines is currently hindered by their excessive operation and maintenance costs. We have developed a comprehensive model to help companies plan their operations in advance by detecting failure in mooring lines in almost real time using supervised deep learning techniques. Given the lack of real data, we have coupled numerical methods and OpenFAST simulations to build a dataset containing the displacements and rotations of a turbine's floating platform across all directions. These time series and their corresponding frequency spectra are used to obtain a set of key statistical parameters, including means and standard deviations, peak frequencies, and several relevant momenta. We have designed and trained a Deep Neural Network to understand and distinguish amongst a series of common failure modes for mooring lines considering a range of metocean and structural conditions. We have obtained promising results when monitoring severe changes in the line's mass and damping using short time spans, achieving a 95.7% validation accuracy when detecting severe biofouling failure.N Gorostidi has received funding from the Spanish Ministry of Science and Innovation project DEEPINVERSE, with reference PID2019-108111RB-I00 (FEDER/AEI). V Nava has received funding from the project IA4TES - Inteligencia Artificial para la Transición Energética Sostenible funded by Ministry of Economic Affairs and Digital Transformation (MIA.2021.M04.0008); the “BCAM Severo Ochoa” accreditation of excellence (SEV-2017-0718); and the Basque Government through the BERC 2022-2025 program, the Elkartek project EXPERTIA (KK-2021/00048)

Experimental validation of a rans-vof numerical model of the wave generation and propagation in a 2d wave flume

This paper focus on the study of free surface variation in a Numerical Wave Flume (NWF) due to a paddle movement. The NWF is the numerical representation of a 12.5 meters long Experimental Wave Flume (EWF) of the laboratory of the University of the Basque Country. The experiments and the numerical simulations are performed in several depths (0.3, 0.4 and 0.5 meters). Besides different velocities for the paddle movement are induced between 0.064 and 0.1 m/s. The numerical simulations are based on an Eulerian Multiphase of two fluids, air and water, more concretely the Volume of Fluid model. The surface variation in two points (6.0 and 6.3 meters from the wave flume start) is studied in both numerical and experimental wave flumes and compared its variation through the experiment time. Besides, the experiments will be analyzed in the wave maker theory. The results show the models quality in the first moments of the experiments, where the reflection does not appear, in which the results from both experimental and numerical simulations are pretty similar

On building physics-based AI models for the design and SHM of mooring systems

Expert systems in industrial processes are modelled using physics-based approaches, data-driven models or hybrid approaches in which however the underlying physical models generally constitute a separate block with respect to the Artificial Intelligence (AI) technique(s). This work applies the novel concept of “imbrication”-a physics-based AI approach-to the mooring system of offshore renewable energy devices to achieve a complete integration of both perspectives. This approach can reduce the size of the training dataset and computational time while delivering algorithms with higher generalization capability and explicability. We first undertake the design of the mooring system by developing a surrogate model coupled with a Bayesian optimiser. Then, we analyse the structural health monitoring of the mooring system by designing a supervised Deep Neural Network architecture. Herein, we describe the characteristics of the imbrication process, analyse preliminary results of our investigation and provide considerations for orienting further research work and sector applicability

Statistical analysis to perform improvement actions in Final Degree Projects. A proposal for the Degree in Pharmacy

[EN] A statistical analysis of the most important characteristics of the Final Degree Projects (FDP) of the Degree in Pharmacy at the University of the Basque Country (UPV / EHU) has been carrying out. The sample analyzed was 264 FDP. The results of the analysis, a multivariate logistic regression, have confirmed, among other aspects, that a great majority of the FDP contain knowledge of a single module of the curriculum, and their content is usually not directly linked to any of job opportunities of the Degree. To reverse this trend, this paper proposes an intervention that resolves the observed deficiencies and improves the execution dynamics of the FDP. The proposal includes a working methodology of a teaching team that is involved and participates in the approach, elaboration, direction and evaluation of the FDP. In addition, an example of the methodology applied to job opportunity of the Community Pharmacy is presenting. The teaching team is multidisciplinary, formed by specialists in different subjects of all the courses of the Degree, of five areas of knowledge belonging to four of the six modules developed in the curriculum. The teaching team and the participating students work with a methodology of Problem Based Learning (PBL). This proposal strengthens the coordination of teaching teams, the originality and creativity of the FDP, the active role of students and teachers and a direct relationship with professional opportunities, which can be implementing in any degree. This work was financing by the UPV / EHU[ES] Se ha realizado un análisis estadístico de las características más importantes de los Trabajos de Fin de Grado (TFG) del Grado en Farmacia en la Universidad del País Vasco (UPV/EHU). La muestra analizada ha sido de 264 TFG. Los resultados del análisis, una regresión logística multivariante, han constatado, entre otros aspectos, que una gran mayoría de los TFG, contienen conocimientos de un único módulo del plan de estudios, y su contenido no suele estar directamente ligado a alguna de las salidas profesionales del Grado. Para revertir esta tendencia, en este trabajo se plantea una intervención que resuelva las carencias observadas y mejore la dinámica de ejecución de los TFG. La propuesta incluye una metodología de trabajo de un equipo docente que se involucra y participa en el planteamiento, elaboración, dirección y evaluación de los TFG. Además, se presenta un ejemplo de la metodología aplicada a la salida profesional de la Farmacia Comunitaria. El equipo docente es multidisciplinar, formado por especialistas en diversas materias de todos los cursos del Grado, de cinco áreas de conocimiento pertenecientes a cuatro de los seis módulos desarrollados en el plan de estudios. El equipo docente y el alumnado participante trabajan con una metodología de Aprendizaje Basado en Problemas (ABP). Esta propuesta, potencia la coordinación de equipos docentes, la originalidad y creatividad de los TFG, el protagonismo activo del alumnado y profesorado y una relación directa con las salidas profesionales, que puede implementarse en cualquier Grado. Este trabajo contó con la financiación de la UPV/EHU.Berraondo Juaristi, M.; Fernández De Aránguiz Guridi, MY.; Fernández De Aránguiz Guridi, A.; Ruiz Ortega, J.; Ayerbe Diaz, M.; Lecea Arana, B.; Martínez De Marigorta Izaga, E.... (2018). Análisis estadístico para realizar acciones de mejora en los Trabajos Fin de Grado. Una propuesta para el Grado en Farmacia. REDU. Revista de Docencia Universitaria. 16(2):17-38. doi:10.4995/redu.2018.9847SWORD173816

Multidistances and Dispersion Measures

Producción CientíficaIn this paper, we provide a formal notion of absolute dispersion measure that is satisfied by some classical dispersion measures used in Statistics, such as the range, the variance, the mean deviation and the standard deviation, among others, and also by the absolute Gini index, used in Welfare Economics for measuring inequality. The notion of absolute dispersion measure shares some properties with the notion of multidistance introduced and analyzed by Mart´ın and Mayor in several recent papers. We compare absolute dispersion measures and multidistances and we establish that these two notions are compatible by showing some functions that are simultaneously absolute dispersion measures and multidistances. We also establish that remainders obtained through the dual decomposition of exponential means, introduced by Garc´ıa-Lapresta and Marques Pereira, are absolute dispersion measures up to signMinisterio de Economía, Industria y Competitividad (ECO2012-32178)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA066U13