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.

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This paper describes the open Novamag database that has been developed for the design of novel Rare-Earth free/lean permanent magnets. The database software technologies, its friendly graphical user interface, advanced search tools and available data are explained in detail. Following the philosophy and standards of Materials Genome Initiative, it contains significant results of novel magnetic phases with high magnetocrystalline anisotropy obtained by three computational high-throughput screening approaches based on a crystal structure prediction method using an Adaptive Genetic Algorithm, tetragonally distortion of cubic phases and tuning known phases by doping. Additionally, it also includes theoretical and experimental data about fundamental magnetic material properties such as magnetic moments, magnetocrystalline anisotropy energy, exchange parameters, Curie temperature, domain wall width, exchange stiffness, coercivity and maximum energy product, that can be used in the study and design of new promising high-performance Rare-Earth free/lean permanent magnets. The results therein contained might provide some insights into the ongoing debate about the theoretical performance limits beyond Rare-Earth based magnets. Finally, some general strategies are discussed to design possible experimental routes for exploring most promising theoretical novel materials found in the database. NOVAMAG H c > M r /2 [5]. Extrinsic properties also depend on temperature, in fact they typically decrease as temperature increases, reducing the magnet's performance, especially close to the Curie temperature T C (i.e. ferromagnetic-paramagenetic transition). The macroscopic behavior is tightly linked to the microscopic properties called intrinsic. Main magnetic intrinsic properties are atomic magnetic moment µ at (the magnetic moment per volume gives the maximum theoretical M s ), exchange interactions J ij (which determine the magnetic order and T C )and magnetocrystalline anisotropy K 1 (that can enhance H c and it is indispensable in modern magnets to get H c > M r /2) [6]. PMs should have high atomic mangetic moments per volume (> 0.1µ B /Å 3 ), strong ferromangetic exchange interactions (able to give T C > 600 K) and high easy axis magnetocrystalline anisotropy (K 1 > 1 MJ/m 3 ) in order to exhibit good extrinsic properties suitable for PM applcations. In particular, magnetic materials with hardness parameter κ = K 1 /(µ 0 M 2 s ) > 1 (called "hard" magnets) are very valuable since can be used to make efficient magnets of any shape [6]. At mesoscopic scale, intergranular structure between the grains and crystallographic defects can strongly affect the performance of a magnet [7]. Therefore, the optimization of the material's microstructure is also very important in the design and devel-