|Occupation:||European University Lecturer|
|Area:||Ingenium Research Group|
PhD in Industrial Engineering with International mention by the University of Castilla-La Mancha.
He studied Industrial Technical Engineering specializing in Industrial Electronics at the School of Engineering of Toledo, where he spent a 9-month period at the Technological Educational Institute of Crete, in Greece.
Industrial Engineer from the School of Industrial Engineers of Ciudad Real, where he received the "Repsol" award for the best final project of the 2013/14 academic year.
He completed the Master's Degree in Industrial Engineering at the UCLM, and there he began his doctorate in Ingenium Research Group with a research grant in the framework of an European project.
During this stage he spent a 5-month stay at Brunel Innovation Center in Cambridge (United Kingdom), and a month at Drexel University in Philadelphia (USA).
His main lines of research are: Advanced inspection techniques using guided ultrasonic waves, development of new classification algorithms using neural networks and inspection with infrared and radiometric technology and drones.
As a result of his research he has published 13 JCR articles, 3 patents, 13 international congresses and 12 book chapters. On the other hand, he has directed an international thesis and a collaborative project with the company.
In 2017 he received the First International Prize for the technology-based idea Entrepreneurship 5 + 5 in Tunisia.
That same year he joined the Department of Industrial and Aerospace Engineering of the Universidad Europea de Madrid, where he taught the subjects:
- Automation and Control
- Automatic regulation
- Analog Electronics
- Power Electronics and Electronic Instrumentation
- Perception systems
- Electrical engineering
- Advanced Electronics
- Circuit Analysis
- Computer-Assisted Circuit Design
In 2018 he received the ANECA accreditations in the figures of Assistant Professor Doctor, Professor Contracted Doctor and Professor of Private University.
(2020) Chapter 6 - Non-destructive testing of wind turbines using ultrasonic waves, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets, Mayorkinos Papaelias, Fausto Pedro García Márquez, Alexander Karyotakis (ed.), p. 91-101, Boston: Butterworth-Heinemann, url, doi:https://doi.org/10.1016/B978-0-08-101094-5.00006-X
(2020) Chapter 9 - Remote condition monitoring for photovoltaic systems, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets, Mayorkinos Papaelias, Fausto Pedro García Márquez, Alexander Karyotakis (ed.), p. 133-142, Boston: Butterworth-Heinemann, url, doi:https://doi.org/10.1016/B978-0-08-101094-5.00009-5
(2020) Chapter 12 - Non-destructive methods for detection and localisation of partial discharges, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets, Mayorkinos Papaelias, Fausto Pedro García Márquez, Alexander Karyotakis (ed.), p. 177-193, Boston: Butterworth-Heinemann, url, doi:https://doi.org/10.1016/B978-0-08-101094-5.00012-5
(2019) Structural health monitoring for delamination detection and location in wind turbine blades employing guided waves, Wind Energy 22(5), p. 698-711, John Wiley & Sons, Ltd, url, doi:10.1002/we.2316
(2019) Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers, Reliability Engineering & System Safety 184, p. 2-12, url, doi:https://doi.org/10.1016/j.ress.2018.02.013
(2019) Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis, Renewable Energy 132, p. 1034-1048, Pergamon, url, doi:https://doi.org/10.1016/j.renene.2018.08.050
(2018) Future Maintenance Management in Renewable Energies, Renewable Energies, p. 149-159, Springer, doi:https://doi.org/10.1007/978-3-319-45364-4_10
(2018) Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers, Reliability Engineering & System Safety, Elsevier, url, doi:https://doi.org/10.1016/j.ress.2018.02.013
(2018) Emisiones acústicas y procesamiento de señales para la localización de defectos en materiales compuestos, url
(2018) Concentrated Solar Plants Management: Big Data and Neural Network, Renewable Energies, p. 63-881, Springer, doi:https://doi.org/10.1007/978-3-319-45364-4_5
(2018) Wind Energy Power Prospective, Renewable Energies, p. 83-95, Springer, doi:https://doi.org/10.1007/978-3-319-45364-4_6
(2018) Machine Learning for Wind Turbine Blades Maintenance Management, Energies 11(1), p. 13, Multidisciplinary Digital Publishing Institute, url
(2018) Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis, Renewable Energy 116(B), p. 42-54, Elsevier, url, doi:http://dx.doi.org/10.1016/j.renene.2017.03.052
(2017) Machine Learning for Wind Turbine Blades Maintenance Management, MDPI, url
(2017) A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers, Eksploatacja i Niezawodnosc - Maintenance and Reliability 19(4), p. 493-500, Polish Maintenance Society, url
(2017) Emisiones Acústicas y Procesamiento de Señales para la Localización de Defectos en Materiales Compuestos, url
(2017) Cracks and weld detection approach in solar receiver tubes employing EMATS, doi:https://doi.org/10.1177/1475921717734501
(2017) Online Fault Detection in Solar Plants Using a Wireless Radiometer in Unmanned Aerial Vehicles, International Conference on Management Science and Engineering Management, p. 1161-1174, Springer, Cham, url, doi:https://doi.org/10.1007/978-3-319-59280-0_96
(2017) Cracks and welds detection approach in solar receiver tubes employing electromagnetic acoustic transducers, Structural Health Monitoring, p. 1475921717734501, SAGE Publications Sage UK: London, England, doi:https://doi.org/10.1177/1475921717734501
(2017) Machine Learning and Neural Network for Maintenance Management, International Conference on Management Science and Engineering Management, p. 1377-1388, Springer, Cham
(2017) A condition monitoring system for blades of wind turbine maintenance management, Proceedings of the tenth international conference on management science and engineering management, p. 3-11, Springer, Singapore, url, doi:doi: 10.1007/978-981-10-1837-4_1
(2016) Drive-train condition monitoring for offshore wind and tidal turbines, 2nd International Conference on Renewable Energies Offshore (Renew 2016)
(2016) Big data and web intelligence for condition monitoring: A case study on wind turbines, Big Data: Concepts, Methodologies, Tools, and Applications, p. 1295-1308, IGI Global, doi:DOI: 10.4018/978-1-4666-8505-5.ch008
(2015) Acoustic emission and signal processing for fault detection and location in composite materials, Elsevier, url
(2015) Fault Detection and Diagnosis employing the Electromagnetic Sensors EMAT, 12th Int. Conf. Cond. Monit. Mach. Fail. Prev.(CMCFPT), Oxford, UK, p. 1-11
(2015) Fault Detection and Diagnosis employing the Electromagnetic Sensors EMAT, Proceedings of the 12th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2015/MFPT 2015), Curran Associates, Inc. ISBN: 978-1-5108-0712-9
(2015) Energy environment maintenance management
(2015) A new condition monitoring approach for maintenance management in concentrate solar plants, Proceedings of the Ninth International Conference on Management Science and Engineering Management, p. 999-1008, Springer, Berlin, Heidelberg
(2014) Structural health monitoring for concentrated solar plants
(2014) A novel approach to fault detection and diagnosis on wind turbines, Global Nest Journal 16(6), p. 1029-1037, GLOBAL NETWORK ENVIRONMENTAL SCIENCE & TECHNOLOGY 30 VOULGAROKTONOU STR, ATHENS, GR 114 72, GREECE
(2013) Experimental platform design to monitor defects in wind turbine blades based on ultrasound technology, Proceedings of the Energy and Environment Knowledge Week, p. 1-3, ISBN: 978-84-695-8372-2