Reliability Models and Failure Detection Algorithms for Wind Turbines

Durante las pasadas décadas, la industria eólica ha sufrido un crecimiento muysignificativo en Europa llevando a la generación eólica al puesto más relevanteen cuanto a producción energética mediante fuentes renovables. Sin embargo, siconsideramos los aspectos económicos, el sector eólico todavía no ha alcanzadoel nivel competitivo necesario para batir a los sistemas de generación de energíaconvencionales.Los costes principales en la explotación de parques eólicos se asignan a lasactividades relacionadas con la Operación y Mantenimiento (O&M). Esto se debeal hecho de que, en la actualidad, la Operación y Mantenimiento está basadaprincipalmente en acciones correctivas o preventivas. Por tanto, el uso de técnicaspredictivas podría reducir de forma significativa los costes relacionados con lasactividades de mantenimiento mejorando así los beneficios globales de la explotaciónde los parques eólicos.Aunque los beneficios del mantenimiento predictivo se consideran cada díamás importantes, existen todavía la necesidad de investigar y explorar dichastécnicas. Modelos de fiabilidad avanzados y algoritmos de predicción de fallospueden facilitar a los operadores la detección anticipada de fallos de componentesen los aerogeneradores y, en base a ello, adaptar sus estrategias de mantenimiento.Hasta la fecha, los modelos de fiabilidad de turbinas eólicas se basan, casiexclusivamente, en la edad de la turbina. Esto es así porque fueron desarrolladosoriginalmente para máquinas que trabajan en entornos ‘amigables’, por ejemplo, enel interior de naves industriales. Los aerogeneradores, al contrario, están expuestosa condiciones ambientales altamente variables y, por tanto, los modelos clásicosde fiabilidad no reflejan la realidad con suficiente precisión. Es necesario, portanto, desarrollar nuevos modelos de fiabilidad que sean capaces de reproducir el comportamiento de los fallos de las turbinas eólicas y sus componentes, teniendoen cuenta las condiciones meteorológicas y operacionales en su emplazamiento.La predicción de fallos se realiza habitualmente utilizando datos que se obtienendel sistema de Supervisión Control y Adquisición de Datos (SCADA) o de Sistemasde Monitorización de Condición (CMS). Cabe destacar que en turbinas eólicasmodernas conviven ambos tipos de sistemas y la fusión de ambas fuentes de datospuede mejorar significativamente la detección de fallos. Esta tesis pretende mejorarlas prácticas actuales de Operación y Mantenimiento mediante: (1) el desarrollo demodelos avanzados de fiabilidad y detección de fallos basados en datos que incluyanlas condiciones ambientales y operacionales existentes en los parques eólicos y (2)la aplicación de nuevos algoritmos de detección de fallos que usen las condicionesambientales y operacionales del emplazamiento, así como datos procedentes tantode sistemas SCADA como CMS. Estos dos objetivos se han dividido en cuatrotareas.En la primera tarea se ha realizado un análisis exhaustivo tanto de los fallosproducidos en un amplio conjunto de aerogeneradores (amplio en número de turbinasy en longitud de los registros) como de sus tiempos de parada asociados. De estaforma, se han visualizado los componentes que más fallan en función de la tecnologíadel aerogenerador, así como sus modos de fallo. Esta información es vital para eldesarrollo posterior de modelos de fiabilidad y mantenimiento.En segundo lugar, se han investigado las condiciones meteorológicas previasa sucesos con fallos de los principales componentes de los aerogeneradores. Seha desarrollado un entorno de aprendizaje basado en datos utilizando técnicas deagrupamiento ‘k-means clustering’ y reglas de asociación ‘a priori’. Este entorno escapaz de manejar grandes cantidades de datos proporcionando resultados útiles yfácilmente visualizables. Adicionalmente, se han aplicado algoritmos de detecciónde anomalías y patrones para encontrar cambios abruptos y patrones recurrentesen la serie temporal de la velocidad del viento en momentos previos a los fallosde los componentes principales de los aerogeneradores. En la tercera tarea, sepropone un nuevo modelo de fiabilidad que incorpora directamente las condicionesmeteorológicas registradas durante los dos meses previos al fallo. El modelo usados procesos estadísticos separados, uno genera los sucesos de fallos, así comoceros ocasionales mientras que el otro genera los ceros estructurales necesarios paralos algoritmos de cálculo. Los posibles efectos no observados (heterogeneidad) en el parque eólico se tienen en cuenta de forma adicional. Para evitar problemas de‘over-fitting’ y multicolinearidades, se utilizan sofisticadas técnicas de regularización.Finalmente, la capacidad del modelo se verifica usando datos históricos de fallosy lecturas meteorológicas obtenidas en los mástiles meteorológicos de los parqueseólicos.En la última tarea se han desarrollado algoritmos de predicción basados encondiciones meteorológicas y en datos operacionales y de vibraciones. Se ha‘entrenado’ una red de Bayes, para predecir los fallos de componentes en unparque eólico, basada fundamentalmente en las condiciones meteorológicas delemplazamiento. Posteriormente, se introduce una metodología para fusionar datosde vibraciones obtenidos del CMS con datos obtenidos del sistema SCADA, conel objetivo de analizar las relaciones entre ambas fuentes. Estos datos se hanutilizado para la predicción de fallos en el eje principal utilizando varios algoritmosde inteligencia artificial, ‘random forests’, ‘gradient boosting machines’, modelosgeneralizados lineales y redes neuronales artificiales. Además, se ha desarrolladouna herramienta para la evaluación on-line de los datos de vibraciones (CMS)denominada DAVE (‘Distance Based Automated Vibration Evaluation’).Los resultados de esta tesis demuestran que el comportamiento de los fallos delos componentes de aerogeneradores está altamente influenciado por las condicionesmeteorológicas del emplazamiento. El entorno de aprendizaje basado en datos escapaz de identificar las condiciones generales y temporales específicas previas alos fallos de componentes. Además, se ha demostrado que, con los modelos defiabilidad y algoritmos de detección propuestos, la Operación y Mantenimiento delas turbinas eólicas puede mejorarse significativamente. Estos modelos de fiabilidady de detección de fallos son los primeros que proporcionan una representaciónrealística y específica del emplazamiento, al considerar combinaciones complejasde las condiciones ambientales, así como indicadores operacionales y de estadode operación obtenidos a partir de la fusión de datos de vibraciones CMS y datosdel SCADA. Por tanto, este trabajo proporciona entornos prácticos, modelos yalgoritmos que se podrán aplicar en el campo del mantenimiento predictivo deturbinas eólicas.<br /

Learning and mining from personal digital archives

Given the explosion of new sensing technologies, data storage has become significantly cheaper and consequently, people increasingly rely on wearable devices to create personal digital archives. Lifelogging is the act of recording aspects of life in digital format for a variety of purposes such as aiding human memory, analysing human lifestyle and diet monitoring. In this dissertation we are concerned with Visual Lifelogging, a form of lifelogging based on the passive capture of photographs by a wearable camera. Cameras, such as Microsoft's SenseCam can record up to 4,000 images per day as well as logging data from several incorporated sensors. Considering the volume, complexity and heterogeneous nature of such data collections, it is a signifcant challenge to interpret and extract knowledge for the practical use of lifeloggers and others. In this dissertation, time series analysis methods have been used to identify and extract useful information from temporal lifelogging images data, without benefit of prior knowledge. We focus, in particular, on three fundamental topics: noise reduction, structure and characterization of the raw data; the detection of multi-scale patterns; and the mining of important, previously unknown repeated patterns in the time series of lifelog image data. Firstly, we show that Detrended Fluctuation Analysis (DFA) highlights the feature of very high correlation in lifelogging image collections. Secondly, we show that study of equal-time Cross-Correlation Matrix demonstrates atypical or non-stationary characteristics in these images. Next, noise reduction in the Cross-Correlation Matrix is addressed by Random Matrix Theory (RMT) before Wavelet multiscaling is used to characterize the `most important' or `unusual' events through analysis of the associated dynamics of the eigenspectrum. A motif discovery technique is explored for detection of recurring and recognizable episodes of an individual's image data. Finally, we apply these motif discovery techniques to two known lifelog data collections, All I Have Seen (AIHS) and NTCIR-12 Lifelog, in order to examine multivariate recurrent patterns of multiple-lifelogging users

Investigating the Role of Dispatched in Hedgehog Ligand Transport and Delivery

During the development of all metazoans, the Hedgehog (Hh) signaling pathway provides instructional cues influencing a variety of cellular processes. The pathway ligand, Hh, is dually lipidated by cholesterol and palmitate, which effectively anchors the molecule to the lipid bilayer of the signal producing cell. To complicate the Hh pathway induction process, the Hh ligand is often produced at a significant distance from the cells it influences. Only one known conserved molecule, Dispatched (Disp), can alleviate the membrane tethering imparted by Hh lipidation. Underscoring the importance of Disp protein during development, knockout animals succumb to lethality at E9.5, an exact phenocopy of the knockout of the essential signal transducer of the pathway: Smoothened. Furthermore, mutations within Disp have been found in patients with Holoprosencephaly, the most common cause of human forebrain malformations, which is frequently caused by inhibition of Hh signaling during development. Very little is known regarding the functional or regulatory mechanisms enabling Disp to transport and release Hh ligand. This dissertation aimed to narrow this gap and began with investigation into the role of Disp in a largely ignored mechanism of Hh ligand transport known as cytoneme-mediated morphogen transport. This method of morphogen transport utilizes fragile, thin cytoplasmic extensions which deliver cargo directly from the source of production to ligand responding cells. Through the use of a modified electron microscopy fixative, which we named MEM-fix, to maintain cytonemes for traditional cell biological analysis, I established an in vitro cytoneme system capable of modeling in vivo cytoneme biology. This in vitro cytoneme system uses Schneider 2 cells, an embryonically derived Drosphila cell line, which demonstrate competency to produce and utilize cytonemes as a mechanism of Hh transport. Equipped with this tool, I investigated the relationship between cytonemes and Hh pathway components. In doing so, I uncovered a previously unknown Disp requirement in cytoneme-mediated transport of Hh ligand and subsequently a Disp-mediated cytoneme stabilizing effect. Through these studies into Disp-mediated cytoneme transport, I identified a Disp cleavage event of Disp that regulates the ability to release Hh ligand. The second part of this dissertation details a collaborative effort in which we discovered that the Furin family of proprotein convertases facilitate the cleavage of Disp at a conserved site of the first extracellular loop. The Furin family has been implicated in cleaving ligands and receptors of other developmental signaling pathways, but no reports exist linking the Furin family to regulation of the Hh pathway. Therefore, we investigated the functional consequence of this Disp cleavage event, and our results suggest that this cleavage likely influences the proper trafficking of Hh for efficient release both in vitro and in vivo. To our knowledge, this is the first report of a regulatory protein partner controlling the activity of Disp in releasing Hh

Mining sensor data from complex systems

Today, virtually everything, from natural phenomena to complex artificial and physical systems, can be measured and the resulting information collected, stored and analyzed in order to gain new insight. This thesis shows how complex systems often exhibit diverse behavior at different temporal scales, and that data mining methods should be able to cope with the multiple resolutions (scales) at the same time in order to fully understand the data at hand and extract useful information from it. Under these assumptions, we introduce novel data mining and visualization methods for large time series data collected from complex physical systems. In particular, we focus on three fundamental problems: the detection of multi-scale patterns, the recognition of recurrent events, and the interactive visualization of massive time series data. We evaluate our methods on a real-world scenario provided by InfraWatch, a Structural Health Monitoring project centered around the management and analysis of data collected by a large sensor network deployed on a Dutch highway bridge. The application of our methods resulted in the identification of the relevant scales of analysis in the InfraWatch data (and other datasets), the detection of the different recurring motifs and the visualization of terabytes of time series data interactively.STWAlgorithms and the Foundations of Software technolog

The RNA-binding landscape of HAX1 protein indicates its involvement in translation and ribosome assembly

HAX1 is a human protein with no known homologues or structural domains. Mutations in the HAX1 gene cause severe congenital neutropenia through mechanisms that are poorly understood. Previous studies reported the RNA-binding capacity of HAX1, but the role of this binding in physiology and pathology remains unexplained. Here, we report the transcriptome-wide characterization of HAX1 RNA targets using RIP-seq and CRAC, indicating that HAX1 binds transcripts involved in translation, ribosome biogenesis, and rRNA processing. Using CRISPR knockouts, we find that HAX1 RNA targets partially overlap with transcripts downregulated in HAX1 KO, implying a role in mRNA stabilization. Gene ontology analysis demonstrated that genes differentially expressed in HAX1 KO (including genes involved in ribosome biogenesis and translation) are also enriched in a subset of genes whose expression correlates with HAX1 expression in four analyzed neoplasms. The functional connection to ribosome biogenesis was also demonstrated by gradient sedimentation ribosome profiles, which revealed differences in the small subunit:monosome ratio in HAX1 WT/KO. We speculate that changes in HAX1 expression may be important for the etiology of HAX1-linked diseases through dysregulation of translation

Systematic prediction of feedback regulatory network motifs

Comprendre le câblage complexe de la régulation cellulaire reste un défi des plus redoutables.Les connaissances fondamentales sur le câblage et le fonctionnement du réseau d’homéostasiedes protéines aideront à mieux comprendre comment l’homéostasie des protéines échouedans les maladies et comment les modèles de régulation du réseau d’homéostasie desprotéines peuvent être ciblés pour une intervention thérapeutique. L’étude vise à développeret à appliquer une nouvelle méthodologie de calcul pour l’identification systématique etla caractérisation des systèmes de rétroaction en homéostasie des protéines. La rechercheproposée combine des idées et des approches issues de la science des protéines, de la biologiedes systèmes de levure, de la biologie computationnelle et de la biologie des réseaux.La difficulté dans la tâche d’incorporer des données multi-plateformes multi-omiques estamplifiée par le vaste réseau de gènes, protéines et métabolites interconnectés qui seréunissent pour remplir une fonction spécifique. Pour ma thèse de maîtrise, j’ai développéun algorithme PBPF (Path-Based Pattern Finding), qui recherche et énumère les motifsde réseau de la topologie requise. Il s’agit d’un algorithme basé sur la théorie des graphesqui utilise la combinaison d’une méthode transversale de profondeur et d’une méthodede recherche par largeur ensuite pour identifier les topologies de sous-graphes de réseaurequises. En outre, le fonctionnement de l’algorithme a été démontré dans les domainesde l’homéostasie des protéines chezSaccharomyces cerevisiae. Une approche systématiqued’intégration des données de la biologie des systèmes a été orchestrée, qui montre l’iden-tification systématique de motifs de rétroaction régulatrice connus dans l’homéostasie desprotéines. Il revendique fortement la capacité d’identifier de nouveaux motifs de rétroactionréglementaire envahissants. L’application de l’algorithme peut être étendue à d’autressystèmes biologiques, par exemple, pour identifier des motifs de rétroaction spécifiques àl’état cellulaire dans le cas de cellules souches.Understanding the intricate wiring of cellular regulation remains a most formidable chal-lenge. The fundamental insights into the wiring and functioning of the protein homeostasisnetwork will help to better understand how protein homeostasis fails in diseases and howthe regulatory patterns of protein homeostasis network can be targeted for therapeuticintervention. The study aims at developing and applying novel computational methodologyfor the systematic identification and characterization of feedback systems in proteinhomeostasis. The proposed research combines ideas and approaches from protein science,yeast systems biology, computational biology, as well as network biology. The difficultyin the task of incorporating multi-platform multi-omics data is amplified by the largenetwork of inter-connected genes, proteins and metabolites that come together to perform aspecific function. For my master’s thesis, I developed a path-based pattern finding (PBPF)algorithm, which searches and enumerates network motifs of required topology. It is a graphtheory based algorithm which utilizes the combination of depth-first transverse method andbreadth-first search method to identify the required network sub-graph topologies. Further,the functioning of the algorithm has been demonstrated in the realms of protein homeostasisinSaccharomyces cerevisiae. A systematic approach of integration of systems biologydata has been orchestrated, which shows the systematic identification of known regulatoryfeedback motifs in protein homeostasis. It claims the unique ability to identify novelpervasive regulatory feedback motifs. The application of the algorithm can be extended toother biological systems, for example, to identify cell-state specific feedback motifs in caseof stem-cells

Deciphering Transcriptional Regulation using Deep Neural Networks

The DNA holds the recipe of all life functions. To decipher the instructions, one has to learn and understand its complex syntax. The non-coding DNA contains regulatory elements, that are essential to control and activate gene expression in the right place at the right time. Previous studies have applied deep learning for gene expression prediction, directly from non-coding sequences, successfully. Almeida et al. [1] showed that a Convolutional Neural Network could learn regulatory syntax from long same-length fragments from the fruit fly. In this thesis, we tested how well deep neural networks could predict gene expression from short DNA fragments of varying lengths from the Atlantic salmon. Furthermore, we extracted what the models had learned, and tested if the sequence features corresponded to known regulatory sequence patterns (motifs). Two deep neural network architectures were built, a Convolutional Neural Network (CNN) and a hybrid Convolutional and Long Short-Term Memory Neural Network (CNN-LSTM). We trained the models to predict the gene expression effect of DNA fragments from open chromatin of liver cells. The two model architectures performed equally well, and the performances depended on the amount of noise in the validation data, reaching a correlation of 0.68 on the sequences of top 10% base mean. We extracted motifs both from the first convolutional filters and from sequence importance scores, and we compared the motifs to the JASPAR database of known vertebrate transcription factor binding site motifs. Among the significant matches to JASPAR, we found some general transcription factors like the TFCP2, HSF and AP-1, as well as some liver-specific transcription factors like the KLF15 and HNF6. Most motifs did not match any JASPAR motif. We explained the tendency of CNNs to distribute partial motifs across several filters, and that other sequence features might be important for prediction as well. Our results suggest that the models learned regulatory DNA syntax equally well, despite their different architectures, and we compared the motif findings in light of these differences. This thesis demonstrates the potential of deep neural networks for analysis of ATAC-STARR-seq data, and suggests improvements worth exploring further to possibly increase performance. We also stress the need for more robust model interpretation techniques, which could unlock valuable knowledge in the future of genomics