Publications

@article{Santos2015,
title = {An SVM-Based Solution for Fault Detection in Wind Turbines},
author = {Pedro Santos and Luisa F Villa and Anibal Reñones and Andrés Bustillo and Jesús Maudes-Raedo},
url = {http://www.mdpi.com/1424-8220/15/3/5627},
doi = {10.3390/s150305627},
issn = {1424-8220},
year = {2015},
date = {2015-03-09},
journal = {Sensors},
volume = {15},
number = {3},
pages = {5627-5648},
abstract = {Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.},
keywords = {Fault diagnosis, Neural networks, Support vector machines, wind turbines},
pubstate = {published},
tppubtype = {article}
}

@article{Santos2014,
title = {Modelling Laser Milling of Microcavities for the Manufacturing of DES with Ensembles},
author = {Pedro Santos and Daniel Teixidor and Jesús Maudes-Raedo and Joaquim Ciurana},
url = {https://www.hindawi.com/journals/jam/2014/439091/},
doi = {10.1155/2014/439091},
isbn = {1110-757X},
year = {2014},
date = {2014-04-17},
journal = {Journal of Applied Mathematics},
volume = {2014},
pages = {15},
abstract = {A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial task.},
keywords = {Ensemble methods, Laser milling, Neural networks, Support vector machines},
pubstate = {published},
tppubtype = {article}
}

@article{RandomProjectionsLinearSVMs,
title = {Random projections for linear SVM ensembles},
author = {Jesús Maudes and Juan José Rodríguez and César García-Osorio and Carlos Pardo},
url = {http://dx.doi.org/10.1007/s10489-011-0283-2},
doi = {10.1007/s10489-011-0283-2},
issn = {0924-669X, 1573-7497},
year = {2011},
date = {2011-01-01},
journal = {Applied Intelligence},
volume = {34},
pages = {347-359},
publisher = {Springer Netherlands},
note = {10.1007/s10489-011-0283-2},
keywords = {Data Mining, Ensemble methods, Support vector machines},
pubstate = {published},
tppubtype = {article}
}