Browsing by Keyword "Neural Networks"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Combined model-based and machine learning approach for damage identification in bridge type structures(2021) Fernández-Navamuel, Ana; Zamora-Sánchez, Diego; Armijo-Prieto, Alberto; Varona-Poncela, Tomás; García-Sánchez, David; García-Villena, Francisco; Ruiz-Cuenca, Francisco; E&I SEGURAS Y RESILIENTES; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓN; Tecnalia Research & InnovationIn this work, we propose a combined approach of model-based and machine learning techniques for damage identification in bridge structures. First, a finite element model is calibrated with real data from experimental vibration modes for the undamaged or baseline state. Second, generic synthetic damage scenarios based on modal parameters are automatically generated with the model to train machine learning algorithms for damage classification (Support Vector Machine, SVM) and damage location and quantification (Neural Network, NN). For an initial validation of the method we use a lab scale truss bridge model, proving that specific damage scenarios can be assessed by the Supervised Machine Learning algorithms trained with generic damage scenarios including a certain variability. The NN provides an assessment in terms of damage location and quantification, whereas the SVM provides a damage severity classification with graphical indication of the damage location and quantification through a reduced dimension plot.Item Fast and reliable fault analysis in complex power systems(Institute of Electrical and Electronics Engineers Inc., 1993) Rodríguez, C.; Martín, J. I.; Ruiz, C.; Lafuente, A.; Rementería, S.; Pérez, J.; Muguerza, J.; Tamura, Y.; Suzuki, H.; Mori, H.; SGNeural network approaches to the design of diagnosis systems for electrical networks have to cope with serious problems derived from the large size of such systems, which makes modularity the obvious solution. A modular approach which is based on functional criteria and provides scalability and adaptability to topological changes is presented. The hypotheses generated by the neural system are justified by a competitive system which detects simple or simultaneous disturbances. This approach allows for a parallel, distributed implementation.Item Intelligent sensor based on acoustic emission analysis applied to gear fault diagnosis(IEEE Computer Society, 2013) Zurita, Daniel; Delgado, Miguel; Ortega, Juan Antonio; Romeral, Luis; FACTORYThe development of intelligent and autonomous monitoring systems applied to rotating machinery, represents the evolution towards the automatic industrial plants supervision. In this regard, an acoustic emission based intelligent sensor is presented in this work. The proposed sensor records regularly the acoustic emission signal generated by gearboxes. A time domain statistical analysis is applied in order to characterize the acquired data. Afterwards, a neural network based algorithm is applied to detect gear fault patterns. Finally, the diagnosis result is sent through a wireless transceiver to the central control unit. Moreover, in order to reach a real autonomous operation, the sensor power is approached by different energy harvesting solutions.