| Email: | Alfredo.ArcosJimenez@uclm.es |
| Occupation: | Castilla-La Mancha University Associate Lecturer |
| Area: | Ingenium Research Group |
PhD in Industrial Engineering (ETSI Industriales of Ciudad Real at University of Castilla-La Mancha, hereafter UCLM, Spain); Master Degree PRL (Francisco de Vitoria University in Madrid, Spain) in 2014; Mechanical Engineering Degree (EIT Industriales of Almadén at UCLM Mancha, Spain); Technical Industrial Engineering in 1995 (EIT Industriales of Cordoba at U.C.O, Spain); Expertise in engineering and accredited by the DPC system COGITI of Spain. His research topics are related to the structural integrity inspection of components involved on the generation of renewable energy via non-destructive testing.
Publications
2020
(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) Maintenance management based on Machine Learning and nonlinear features in wind turbines, Renewable Energy 146, p. 316-328, Pergamon, url, doi:10.1016/J.RENENE.2019.06.135
2019
(2019) OptiWindSeaPower: Gestión Integral Óptima de Parques Eólicos Offshore Mediante Nuevos Modelos Matemáticos (1º parte), Asociación Española de Ensayos No Destructivos 86, p. 26-39
(2019) OptiWindSeaPower: Gestión Integral Óptima de Parques Eólicos Offshore Mediante Nuevos Modelos Matemáticos (2ª parte), Asociación Española de Ensayos No Destructivos 89, p. 34-48, url
(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
(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) Concentrated Solar Plants Management: Big Data and Neural Network, Renewable Energies, p. 63-881, Springer, url, doi:https://doi.org/10.1007/978-3-319-45364-4_5
(2018) Machine Learning for Wind Turbine Blades Maintenance Management, Energies 11(1), p. 13, Multidisciplinary Digital Publishing Institute, url, doi:https://doi.org/10.3390/en11010013
(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
(2017) Artificial intelligence for concentrated solar plant maintenance management, Advances in Intelligent Systems and Computing 502, url, doi:10.1007/978-981-10-1837-4_11
(2017) New Pipe Notch Detection and Location Method for Short Distances employing Ultrasonic Guided Waves, Acta Acustica united with Acustica, p. 772-781, url, doi:https://doi.org/10.3813/AAA.919106
(2017) Cracks and Welds Detection Approach in Solar Receiver Tubes Employing EMATs, Structural Health Monitoring, url, doi:https://doi.org/10.1177/1475921717734501
(2017) New Pipe Notch Detection and Location Method for Short Distances employing Ultrasonic Guided Waves, url, doi:https://doi.org/10.3813/AAA.919106
(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, doi:https://doi.org/10.17531/ein.2017.4.1
(2017) Cracks and weld detection approach in solar receiver tubes employing EMATS, url, doi:https://doi.org/10.1177/1475921717734501
(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, url, doi:https://doi.org/10.1177/1475921717734501
2016
(2016) Calculus of the defect severity with EMATs by analyzing the attenuation curves of the guided waves, url, doi:https://doi.org/10.12989/sss.2017.19.2.195
2015
(2015) Fault Detection and Diagnosis employing the Electromagnetic Sensors EMAT, 12th Int. Conf. Cond. Monit. Mach. Fail. Prev.(CMCFPT), Oxford, UK, p. 1-11, url