Early detection of main bearing damage in wind turbines

According to the European Wind Energy Academy (EAWE), the wind industry has recognized that main bearing failures are a major concern in order to increase the reliability and availability of wind turbines. This is due to the high replacement cost of major repairs and the long downtime associated with main bearing failures. As a result, predicting main bearing failure has become an economically important problem as well as a technological difficulty. This paper presents a data-driven technique based on a closed recurrent unit (GRU) neural network for early failure prediction (months in advance). The main contributions of this work are: (i) The prediction is made exclusively using SCADA (Supervision Control and Data Acquisition) data already present in all industrial wind turbines. Therefore, there is no need to add additional sensors intended for a specific use. (ii) Since the proposed approach only requires healthy data, it can be used in any wind farm even if it has not recorded faulty data. (iii) The suggested algorithm operates under a variety of operational and environmental circumstances. (iv) The methodology is validated in two real inproduction wind turbines. production.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energiaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.3 - Per a 2030, duplicar la taxa mundial de millora de l’eficiència energèticaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.a - Per a 2030, augmentar la cooperació internacional per tal de facilitar l’accés a la investigació i a les tecnolo­gies energètiques no contaminants, incloses les fonts d’energia renovables, l’eficiència energètica i les tecnologies de combustibles fòssils avançades i menys contaminants, i promoure la inversió en infraestructures energètiques i tecnologies d’energia no contaminantPostprint (published version

Detecting bearing failures in wind energy parks: A main bearing early damage detection method using SCADA data and a convolutional autoencoder

Wind energy maintenance and operation costs can total millions of dollars each year in an average industrial-size wind park. Therefore, moving from preventive and corrective maintenance to predictive maintenance is imperative in the wind energy sector. This paper contributes to this challenge by providing a main bearing early damage detection technique that exclusively uses standard supervisory control and data acquisition (SCADA) data (10-min average) and a convolutional autoencoder with the following contributions. (i) Entirely semisupervised (not requiring the labeling of data through work order logs and avoiding the problem of data imbalance between classes) based only on healthy data, thus expanding its range of application (even when the failure of interest has never occurred in the park before). (ii) Validated using real-world SCADA data and shown to be resistant to seasonality, and operational and environmental conditions. (iii) Reliable predictions with minimum false alarms thanks to specially designed fault prognosis indicators based on the image mean square error metric. (vi) The early warning is achieved months in advance, thus providing adequate time for plant operators to plan properly. (v) The main use of exogenous variables in the model (variables that are not affected by other variables, e.g., wind speed, wind turbulence, and ambient temperature) guarantees the detection of damage directly related only to the low-speed shaft temperature (the only nonexogenous variable used by the stated model). (vi) Finally, the proposed strategy is validated in a wind park made up of 12 wind turbines.Postprint (published version

Conceptual design of a vibration test system based on a wave generator channel for lab-scale offshore wind turbine jacket foundations

Structural health monitoring (SHM) systems are designed to continually monitor the health of structures (e.g., civil, aeronautic) by using the information collected through a distributed sensor network. However, performing tests on real structures, such as wind turbines, implies high logistic and operational costs. Therefore, there is a need for a vibration test system to evaluate designs at smaller scales in a laboratory setting in order to collect data and devise predictive maintenance strategies. In this work, the proposed vibration test system is based on a lab-scale wind turbine jacket foundation related primarily to an offshore environment. The test system comprises a scaled wave generator channel, a desktop application (WTtest) to control the channel simulations, and a data acquisition system (DAQ) to collect the information from the sensors connected to the structure. Various equipment such as accelerometers, electrodynamic shaker, and DAQ device are selected as per the design methodology. Regarding the mechanical part, each component of the channel is designed to be like the wave absorber, the mechanical multiplier, the piston-type wavemaker, and the wave generator channel. For this purpose, the finite element method is used in static and fatigue analysis to evaluate the stresses and deformations; this helps determine whether the system will work safely. Moreover, the vibration test system applies to other jacket structures as well, giving it greater utility and applicability in different research fields. In sum, the proposed system is compact and has three well-defined components that work synchronously to develop the experimental simulations.Peer ReviewedPostprint (published version

Main bearing fault prognosis in wind turbines based on gated recurrent unit neural networks

The transition from onshore to offshore wind farms is an imminent fact in the future. It supposes to face hard challenges like difficulties to carry out offshore maintenance operations due to increased downtime (because of several causes like continuously bad environmental conditions) on wind farms. That is why, there is a need to improve maintenance and monitoring practices like those involved in condition-based area. This work proposes a methodology based on three key points: (i) a semi-supervised model built from a gated recurrent unit (GRU) neural network and by using only healthy real SCADA data, (ii) propose a fault prognosis indicator (FPI) to trigger warnings or fault alarms as such, and (iii) detect the main bearing fault several months in advance on a faulty wind turbine. The reported results show the excellent performance of the GRU trained model to predict the main bearing temperature as output by exploiting the capabilities of GRUs (recurrent-based neural networks) to decide what information to forget or preserve through time. In the FPI construction, the use of exponentially weighted moving average (EWMA) helps at the results to avoid the presence of false alarms that is very useful in any detection strategy. Finally, the stated methodology lets to detect the main bearing fault on a WT two months in advance at least, which contributes to plan maintenance actions ahead of time. Furthermore, in this way, the lifespan of this large component may be extended and wind turbine’s uptime may increase in a significant percentage.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.a - Per a 2030, augmentar la cooperació internacional per tal de facilitar l’accés a la investigació i a les tecnolo­gies energètiques no contaminants, incloses les fonts d’energia renovables, l’eficiència energètica i les tecnologies de combustibles fòssils avançades i menys contaminants, i promoure la inversió en infraestructures energètiques i tecnologies d’energia no contaminantObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energiaPostprint (published version

Early fault detection in the main bearing of wind turbines based on Gated Recurrent Unit (GRU) neural networks and SCADA data

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksFailures in the main bearings of wind turbines are critical in terms of downtime and replacement cost. Early diagnosis of their faults would lower the levelized cost of wind energy. Thus, this work discusses a gated recurrent unit (GRU) neural network, which detects faults in the main bearing some months ahead (when the event that initiates/develops the failure releases heat) the actual fatal fault materializes. GRUs feature internal gates that govern information flow and are utilized in this study for their capacity to understand whether data in a time series is crucial enough to preserve or forget. It is noteworthy that the proposed methodology only requires healthy supervisory control and data acquisition (SCADA) data. Thus, it can be deployed to old wind parks (nearing the end of their lifespan) where specific high-frequency condition monitoring sensors are not installed and to new wind parks where faulty historical data do not exist yet. The strategy is trained, validated, and finally tested using SCADA data from an in-production wind park composed of nine wind turbines.Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energiaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.3 - Per a 2030, duplicar la taxa mundial de millora de l’eficiència energèticaPostprint (author's final draft

Determinación del porcentaje optimo de biodiésel y diésel filtrado a 2850 msnm, para reducir la opacidad de un motor con sistema CRDI, y reducir el impacto ambiental

El objetivo de esta investigación fue determinar el porcentaje de mezcla idónea entre biodiesel y diésel filtrado, mediante diferentes proporciones de los combustibles para la reducción de niveles de opacidad en camionetas con sistema de Inyección Directa por Conducto Común (CRDI) en la ciudad de Quito. Se definió tres porcentajes de mezcla del Biodiesel (B) al B5, B10 y B15, se tomó en cuenta la opacidad mediante el uso de un dinamómetro automotriz modelo PLS 3000 con el diésel filtrado, como también la opacidad con el diésel comercial para hacer referencia de la reducción del mismo en los cuatro vehículos con sistema CRDI y para el filtrado de diésel comercial, se hizo uso de la máquina de filtrado de diésel por bombeo la cual utiliza filtros Racor PFD10/30 llegando a obtener un nivel de pureza mediante los códigos de limpieza según la Organización Internacional de Normalización (ISO) 14/13/9. Para el análisis estadístico de la opacidad se realizó mediante el Análisis de Varianza (ANOVA) y el método de TUKEY para la diferencia de medias, resultando así que al menos dos tipos de mezclas son diferentes y que el tratamiento que minimiza el porcentaje de opacidad es la mezcla del diésel filtrado & B15. Se concluye que al realizar un filtrado del diésel comercial mejora la calidad del combustible reduciendo considerablemente el porcentaje de opacidad en los vehículos analizados, en consecuencia, si se comercializara diésel filtrado en todo el Ecuador se mejoraría la combustión y se reducirá las emisiones contaminantes. Se recomienda para pruebas llevadas a cabo en laboratorio o en bancos dinamómetros es necesario tener precisión en los equipos de medición para tener una efectividad de medición.This research aimed to determine the ideal mixture percentage between biodiesel and filtered diesel through different proportions of the fuels to reduce opacity levels in trucks with a Common Pipe Direct Injection (CRDI) system in Quito city. Three percentages of the mixture of Biodiesel (B) to B5, B10, and B15 were defined. The opacity was taken into account by using a model PLS 3000 automotive dynamometer with the filtered diesel and the ambiguity with the commercial diesel to refer to its reduction in the four vehicles with the CRDI system. For commercial diesel filtering, the pumped diesel filtering machine was used, which uses Racor PFD10/30 filters, reaching a level of purity through the cleanliness of the code according to the International Organization for Standardization (ISO) 14/13/9. For the statistical analysis of opacity, the Analysis of Variance (ANOVA) and the TUKEY method were performed for the difference in means, resulting in at least two types of mixtures being different, and the treatment that minimizes the percentage of opacity is the mixture of filtered diesel & B15. It is concluded that filtering commercial diesel improves fuel quality, considerably reducing the opacity rate in the analyzed vehicles. Consequently, if filtered diesel were commercialized throughout Ecuador, combustion would be enhanced, and polluting emissions would be reduced. For tests carried out in the laboratory or on dynamometer benches, it is recommended to have precision in the measurement equipment to have an effectiveness of measurement

STUDENT SATISFACTION IN VIRTUAL EDUCATION: A INTERNATIONAL SYSTEMATIC REVIEW

As a result of the SARS-CoV-2 pandemic, there is greater interest in a line of study that demonstrates how the measurement of student satisfaction in virtual education has positive impacts on the university education. In this sense, this research has two main purposes: (a) to describe the conceptualization and typology of satisfaction in virtual education; and (b) to identify the benefits and conditions of these in the training of students. The method has used the PRISMA confession of literature reviews and the inquiries were collected from data sources such as SCOPUS, ERIC and EBSCO Discovery Service. In accordance with the results, a total of 50 international studies have been selected which content contributes to the scientific literature on the systematization of conceptual criteria to categorize types of satisfaction and also the limiting and predominant factors such as the roles of the student and the teacher, the virtual course, connectivity, technology and institutional management. In addition, the benefits obtained in the personal, professional and social aspects of students, which are observed, identified, recognized and incorporated. Finally, it showed the need to develop indicators that delve into each of the conditions to achieve satisfaction.A raíz de la pandemia del SARS-CoV-2, se viene prestando mayor interés a una línea de estudio que demuestra cómo la medición de la satisfacción en la educación virtual tiene impactos positivos en la formación universitaria. En ese sentido, esta investigación presenta dos propósitos: (a) describir la conceptualización y tipología de la satisfacción en la educación virtual; y (b) identificar los beneficios y condiciones de estas en la instrucción. El método llevado a cabo, ha utilizado la declaración PRISMA de revisión sistemática y las pesquisas se recopilaron de fuentes de datos como Scopus, ERIC y EBSCO Discovery Service. En concordancia con los resultados, se ha seleccionado un total de 50 estudios internacionales, cuyo contenido aporta a la literatura científica en la sistematización de criterios conceptuales para categorizar tipos de satisfacción y también los factores condicionantes, limitantes y predominantes para la misma, como son los roles del estudiante y del profesor, el curso virtual, la conectividad, la tecnología y la gestión institucional. Además, se observan, identifican, reconocen e incorporan los beneficios obtenidos en los aspectos personales, profesionales y sociales del estudiantado. Finalmente, se expone la necesidad de desarrollar pesquisas que ahonden en cada una de las condiciones para alcanzar la satisfacción

PUBLICIDAD INTERACTIVA MEDIANTE EL RECORRIDO DE UN ZOOLÓGICO VIRTUAL USANDO REALIDAD AUMENTADA (INTERACTIVE ADVERTISING THROUGH THE TRAVEL OF A VIRTUAL ZOOLOGICAL USING AUGMENTED REALITY)

Resumen El presente documento describe la investigación realizada para el desarrollo de publicidad interactiva a través de una aplicación móvil que usa Realidad Aumentada y que consiste en el recorrido de un Zoológico Virtual que genere consciencia ecológica entre los usuarios. El recorrido permite identificar, usando dispositivos móviles, “marcas” que representan a los animales del zoológico y que “disparan” una imagen animada en 3D del animal identificado. Cada vez que el usuario logra una identificación recibe un puntaje que se va acumulando hasta el final del recorrido, y que luego puede canjearse por productos u ofertas. El objetivo que se alcanza con este trabajo es el de demostrar el uso de la Realidad Aumentada como una forma de hacer publicidad interactiva y en este caso en particular generar consciencia ecológica. El documento presenta una breve introducción, antecedentes, continúa con la metodología utilizada, se hace una breve discusión sobre el prototipo obtenido y se analizan los resultados obtenidos. Palabras Clave: Imagen 3D, Realidad Aumentada, Tecnologías de la información y comunicación, Zoológico Virtual. Abstract This document describes the research carried out for the development of interactive advertising through a mobile application that uses Augmented Reality and that consists of the tour of a Virtual Zoo that generates ecological awareness among users. The route allows identify, using mobile devices, “marks” that represent the animals of the zoo and that “shoot” a 3D animated image of the identified animal. Each time the user achieves identification, he receives a score that accumulates until the end of the tour, and which can then be exchanged for products or offers. The objective achieved with this work is to demonstrate the use of Augmented Reality as a way to make interactive advertising and in this case in particular generate ecological awareness. The document presents a brief introduction, background, continues with the methodology used, there is a brief discussion about the prototype obtained and the results are analyzed Keywords: 3D Image, Augmented Reality, Information and communication technologies, Virtual Zoo

UNA PLATAFORMA EDUCATIVA PARA EL APRENDIZAJE COLABORATIVO SÍNCRONO: “K’ULU” (AN EDUCATIONAL PLATFORM FOR SYNCHRONOUS COLLABORATIVE LEARNING: “K'ULU”)

Resumen La presente investigación se basa en la descripción de la forma en que se elaboró una plataforma que soporta el aprendizaje colaborativo educativo síncrono denominada “K’ulu’” (Mapache en Maya). En esta plataforma, los alumnos interactúan en forma síncrona por turnos sobre la misma vista, las acciones que cada uno hace en su turno se replica en todas las vistas de cada uno de los participantes (en forma síncrona). Se tiene un área en la que los estudiantes pueden comunicarse entre sí en forma síncrona (un Chat) y ayudarse a resolver los problemas presentados en la plataforma. Esta plataforma contiene varias características como Comunicación síncrona, Contenido multimedia (audio, vídeo, imagen), Libros digitales, Sesiones de aprendizaje entre usuarios que son estudiantes de sexto grado de educación primaria de Chetumal, Quintana Roo. Palabras Claves: Aprendizaje Colaborativo, Chat, Interacción Síncrona, Sesiones de aprendizaje. Abstract This research is based on the description of the way in which a platform that supports synchronous educational collaborative learning (CSCL) called “K’ulu’” (Raccoon in Maya) was developed. In this platform, the students interact synchronously in turns on the same view, the actions that each one does in their turn is replicated in all the views of each of the participants (synchronously). There is an area where students can communicate with each other synchronously (a Chat) and help them solve the problems presented on the platform. This platform contains several features such as Synchronous Communication, Multimedia content (audio, video, image), Digital books, Learning sessions between users who are sixth grade students of Chetumal primary education, Quintana Roo. Keywords: Collaborative Learning, Chat, Synchronous Interaction, Learning Session