Sistemas de autoaprendizaje y autoevaluación dentro de la plataforma Atenea

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

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

Editorial: Wind turbine fault and damage diagnosis and prognosis

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

Detection of jacket offshore wind turbine structural damage using an 1D-convolutional neural network with a support vector machine layer

Because offshore wind turbines, particularly their foundations, operate in hostile environments, implementing a structural health monitoring system is one of the best ways to monitor their condition, schedule maintenance, and predict possible fatal failures at lower costs. A novel strategy for detecting damage in offshore wind turbine jacket foundations is developed in this work, based on a vibration monitoring methodology that reshapes the data into a multichannel array, with as many channels as correlated sensors with the predicted variable, a 1-D deep convolutional neural network to extract temporal features from the monitored data, and a support vector machine as a final classification layer. The obtained model allows the detection of three types of bar states: healthy bar, cracked bar, and bar with an unlocked bolt.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Wind turbine main bearing failure prediction using a hybrid neural network

Energy is necessary for economic growth and improved well-being, but it poses a great challenge to be generated without increasing costs and avoiding pollution. A viable option is wind energy because it is a clean and renewable. However, continuous monitoring and maintenance of wind turbines is required for the further development of wind farms. Main bearing failures were identified by the European Academy of Wind Energy as a critical issue in terms of increasing the availability and reliability of wind turbines. In this work, it is proposed a hybrid neural network for main bearing failure prognosis. This network consists of a two-dimensional convolutional neural network (to extract spatial-temporal characteristics from the data) sequentially connected with a long short-term memory network (to learn sequence patterns) to predict the slow-speed shaft temperature (the closest temperature to the main bearing). The mean square error between its real measurement and its prediction gives a failure indicator. When it is greater than a defined threshold, then an alarm is triggered and gives the maintenance staff time to check the component. The advantage of this strategy is that it does not need faulty data to be trained, since it is based on a normality model, that is, it is trained with a single class of data (healthy) and does not require incurring high costs per acquisition of new sensors since SCADA data is used (which comes in all industrial size turbine). The results show that the use of a hybrid network can identify failures around four months before a fatal failure occurs.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Hysteresis modeling of a class of RC-OTA hysteretic-chaotic generators

A class of RC-OTA hysteretic-chaotic generators has been proposed from the electronics point of view. Hysteresis is captured by an electronic realization. To extend its applicability, hysteretic mathematical modeling is an important issue. Here, a new hysteretic model is proposed. With this model, we realized a well known hysteretic-chaotic attractor using Simnon.Postprint (published version

Stabilized updated Lagrangian corrected SPH for explicit dynamic problems

This is the pre-peer reviewed version of the following article:Vidal, Y.; Bonet, J.; Huerta, A. Stabilized updated Lagrangian corrected SPH for explicit dynamic problems. "International journal for numerical methods in engineering", Març 2007, vol. 69, núm. 13, p. 2687-2710, which has been published in final form at http://www3.interscience.wiley.com/journal/112777203/abstractSmooth Particle Hydrodynamics with a total Lagrangian formulation are, in general, more robust than nite elements for large distortion problems. Nevertheless, updating the reference con¯guration may still be necessary in some problems involving extremely large distortions. However, as discussed here a standard updated formulation suffers the presence of zero energy modes that are activated and may spoil completely the solution. It is important to note that, unlike an Eulerian formulation,the updated Lagrangian does not present tension instability but only zero energy modes. Here an stabilization technique is incorporated to the updated formulation to obtain an improved method without mechanisms and capable to solve problems with extremely large distortions.Peer ReviewedPostprint (author’s final draft

Editorial: Women in science: energy research 2023

Objectius de Desenvolupament Sostenible::5 - Igualtat de GènereObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version