Cristina Castejón

Traversable Region Modeling for Outdoor Navigation

This article presents a new methodology to build, in real-time, compact local and global maps for outdoor navigation. The environment information is obtained from a 3D laser range. The navigation model, called Traversable Regions Model (TRM), is based on Voronoi diagram technique but adapted to large outdoor environments, that is, the model is built from 3D data. In the manuscript we also present a novel contribution to the regions modeling field in robotics. The method allows to calculate the roughness degree of an unknown terrain based on the normal vector deviation. The parameter which measure the roughness degree is called spherical variance and it will be useful to determine the traversable areas. The model built allows defining safe trajectories depending on the robots capabilities and the terrain properties and will represent, in a topo-geometric way, the environment as local and global maps. The methodology presented is validated in real outdoor environments with an outdoor robot developed in our lab, called

Automatic condition monitoring system for crack detection in rotating machinery

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Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors

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A fuzzy-based suspension vehicle depending on terrain

In the maintenance of motor driven systems, detection of cracks in shafts play a critical role. Condition monitoring and fault diagnostics detect and distinguish different kinds of machinery faults, and provide a significant improvement in maintenance efficiency. In this study, we apply the discrete wavelet transform theory and multiresolution analysis (MRA) to vibration signals to find characteristic patterns of shafts with a transversal crack. The feature vectors generated are used as input to an intelligent classification system based on artificial neural networks (ANNs). Wavelet theory provides signal timescale information, and enables the extraction of significant features from vibration signals that can be used for damage detection. The feature vectors generated for every fault condition feed a radial basis function neural network (ANN-RBF) and apply supervised learning designed and adapted for different fault crack conditions. Together, MRA and RBF constitute an automatic monitoring system with a fast diagnosis online capability. The proposed method is applied to simulated numerical signals to prove its soundness. The numerical data are acquired from a modified Jeffcott Rotor model with four transverse breathing crack sizes. The results demonstrate that this novel diagnostic method that combines wavelets and an artificial neural network is an efficient tool for the automatic detection of cracks in rotors.

Incipient Fault Detection in Bearings Through the use of WPT Energy and Neural Networks

Abstract Maintenance is essential to prevent catastrophic failures in rotating machinery. A crack can cause a failure with costly processes of reparation, especially in a rotating shaft. In this study, the Wavelet Packets transform energy combined with Artificial Neural Networks with Radial Basis Function architecture (RBF-ANN) are applied to vibration signals to detect cracks in a rotating shaft. Data were obtained from a rig where the shaft rotates under its own weight, at steady state at different crack conditions. Nine defect conditions were induced in the shaft (with depths from 4% to 50% of the shaft diameter). The parameters for Wavelet Packets transform and RBF-ANN are selected to optimize its success rates results. Moreover, ‘Probability of Detection’ curves were calculated showing probabilities of detection close to 100% of the cases tested from the smallest crack size with a 1.77% of false alarms.

Topographical analysis for Voronoi-based modelling

In this paper, a new methodology to build compact local maps in real time for outdoor robot navigation is presented. The environment information is obtained from a 3-D scanner laser. The navigation model, which is called traversable region model, is based on a Voronoi diagram technique, but adapted to large outdoor environments. The model obtained with this methodology allows a definition of safe trajectories that depend on the robots capabilities and the terrain properties, and it will represent, in a topogeometric way, the environment as local and global maps. The application presented is validated in real outdoor environments with the robot called GOLIAT.

EMD-Based Methodology for the Identification of a High-Speed Train Running in a Gear Operating State

Wavelet transform (WT) has been used in the diagnosis of cracked rotors since the 1990s. At present, WT is one of the most commonly used tools to treat signals in several fields. Understandably, this has been an area of extensive scientific research, which is why this paper aims to summarize briefly the major advances in the field since 2008. The present review considers advances in the use and application of WT, the selection of the parameters used, and the key achievements in using WT for crack diagnosis.

Web-based application for descriptive geometry learning

This paper presents a Fuzzy Logic Controller for a semi-active suspension vehicle, depending on terrain. The terrain, which could be traversed by the vehicle, is classified as flat or uneven terrain. The behaviour of the suspension vehicle is different in each class of terrain. The main aim of the controller is to improve passenger comfort, to control the vehicle body attitude and to provide guidance along the track. The proposed Fuzzy Logic Controller has been studied using a quarter vehicle model. The results show the effectiveness of our algorithm.