Autoaprenentage i autoavaluació en les noves assignatures de matemàtiques dins del marc del sistema ECTS en l'entorn AteneaLabs

Peer Reviewe

Sensor selection based on principal component analysis for fault detection in wind turbines

Growing interest for improving the reliability of safety-critical structures, such as wind turbines, has led to the advancement of structural health monitoring (SHM). Existing techniques for fault detection can be broadly classified into two major categories: model-based methods and signal processing-based methods. This work focuses in the signal-processing-based fault detection by using principal component analysis (PCA) as a way to condense and extract information from the collected signals. In particular, the goal of this work is to select a reduced number of sensors to be used. From a practical point of view, a reduced number of sensors installed in the structure leads to a reduced cost of installation and maintenance. Besides, from a computational point of view, less sensors implies lower computing time, thus the detection time is shortened. The overall strategy is to firstly create a PCA model measuring a healthy wind turbine. Secondly, with the model, and for each fault scenario and each possible subset of sensors, it measures the Euclidean distance between the arithmetic mean of the projections into the PCA model that come from the healthy wind turbine and the mean of the projections that come from the faulty one. Finally, it finds the subset of sensors that separate the most the data coming from the healthy wind turbine and the data coming from the faulty one. Numerical simulations using a sophisticated wind turbine model (a modern 5MW turbine implemented in the FAST software) show the performance of the proposed method under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics.Postprint (published version

Vibration-Based structural health monitoring using piezoelectric transducers and parametric t-SNE

In this paper, we evaluate the performance of the so-called parametric t-distributed stochastic neighbor embedding (P-t-SNE), comparing it to the performance of the t-SNE, the non-parametric version. The methodology used in this study is introduced for the detection and classification of structural changes in the field of structural health monitoring. This method is based on the combination of principal component analysis (PCA) and P-t-SNE, and it is applied to an experimental case study of an aluminum plate with four piezoelectric transducers. The basic steps of the detection and classification process are: (i) the raw data are scaled using mean-centered group scaling and then PCA is applied to reduce its dimensionality; (ii) P-t-SNE is applied to represent the scaled and reduced data as 2-dimensional points, defining a cluster for each structural state; and (iii) the current structure to be diagnosed is associated with a cluster employing two strategies: (a) majority voting; and (b) the sum of the inverse distances. The results in the frequency domain manifest the strong performance of P-t-SNE, which is comparable to the performance of t-SNE but outperforms t-SNE in terms of computational cost and runtime. When the method is based on P-t-SNE, the overall accuracy fluctuates between 99.5% and 99.75%.Peer ReviewedPostprint (published version

Wind turbine condition monitoring strategy through multiway PCA and multivariate inference

This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, a in [1%, 13%], the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.Peer ReviewedPostprint (published version

Damage diagnosis for offshore fixed wind turbines

This paper proposes a damage diagnosis strategy to detect and classify different type of damages in a laboratory offshore-fixed wind turbine model. The proposed method combines an accelerometer sensor network attached to the structure with a conceived algorithm based on principal component analysis (PCA) with quadratic discriminant analysis (QDA). The paradigm of structural health monitoring can be undertaken as a pattern recognition problem (comparison between the data collected from the healthy structure and the current structure to diagnose given a known excitation). However, in this work, as the strategy is designed for wind turbines, only the output data from the sensors is used but the excitation is assumed unknown (as in reality is provided by the wind). The proposed methodology is tested in an experimental laboratory tower modeling an offshore-fixed jacked-type wind turbine. The obtained results show the reliability of the proposed approach.Peer ReviewedPostprint (published version

Elements bàsics de l'àlgebra lineal. Problemes per a la ciència de dades

Postprint (published version

The publication of press releases as journalistic information. Comparative study of two Spanish newspapers

La distinción de lo que constituye un “evento noticiable” puede dar lugar a muchas interpretaciones. En este mundo de accesibilidad telemática, que es una consecuencia de la globalización, los eventos y los sucesos de todo tipo se pueden clasificar como noticias simplemente vistiéndolos como noticias. De acuerdo con los manuales de estilo y comunicación, la noticia tiene características propias: relevancia, interés social y proximidad, entre otras. Los comunicados de prensa se han perfeccionado como resultado de las agencias de relaciones públicas cada vez más sofisticadas, y con ellas la línea delgada entre la información y la publicidad ahora está borrosa. En este artículo, comparamos comunicados de prensa emitidos por empresas públicas y privadas con breves publicados en las secciones de economía de los periódicos. Como se verá, muchos de ellos coinciden y tienen algunas similitudes. La muestra utiliza breves publicados durante el primer semestre de 2014 en El Mundo y La Vanguardia, los periódicos en español de pago por lectura que ocupan un lugar destacado en el análisis del Estudio General de Medios. La metodología hace uso del programa Maple con su comando DetectPlagiarism para realizar una comparación ad hoc de los textos. El umbral de copia predeterminado para DetectPlagiarism es 0.35. Los índices de similitud entre los breves y los comunicados de prensa de La Vanguardia y El Mundo indican valores superiores a este umbral.Peer ReviewedPostprint (published version

Wind turbine multi-fault detection and classification based on SCADA data

Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines.Postprint (published version

Supervised classification with SCADA data for condition monitoring of wind turbines

The reliability requirements of wind turbines have increased significantly in recent years inthe search for a lower impact on the cost of energy. In addition, the trend towards larger wind turbinesinstalled in remote locations has significantly increased the cost of repair or replacement of the compo-nent. In the wind industry, therefore, condition monitoring is crucial for maximum availability [1]. Thiscontribution makes a review of supervised machine learning classification techniques for wind turbinecondition monitoring using only SCADA data already available. That is, without installing extra sensorsor costly purpose-built data sensing equipment. Although there has been extensive research into the useof machine learning techniques for wind turbine monitoring, the more recent trend in this type of litera-ture is to focus on a specific WT sub-assembly: the bearings and planetary gearbox [2], the generator andpower converter [3], the blades [4], etc. Oil debris systems can detect pitting failures but cannot detectcracking faults. Vibration based systems can detect both pitting and cracking, but most cannot determinethe health of components in the planetary section. This work approaches condition monitoring of variouswind turbine components (torque actuator, pitch actuator, pitch sensor, and generator speed sensor) witha unique strategy. In particular, for this purpose, a review of supervised machine learning classificationtechniques is performed and analyzed.Postprint (published version

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version