{"id":957,"date":"2019-05-06T12:12:26","date_gmt":"2019-05-06T12:12:26","guid":{"rendered":"http:\/\/161.67.214.10\/wordpress\/?page_id=957"},"modified":"2020-01-23T12:27:39","modified_gmt":"2020-01-23T12:27:39","slug":"optiwindseapower","status":"publish","type":"page","link":"https:\/\/ingenium.uclm.es\/index.php\/optiwindseapower\/","title":{"rendered":"Optiwindseapower"},"content":{"rendered":"\t\t
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\r\n \r\n OPTIWINDSEAPOWER<\/span>\r\n \r\n <\/h1>\r\n \t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\"The project starts with the intention of continuing and completing the National WindSeaEnergy project (DPI2012-31579), observing that there is a great lack of mathematical models that allow the analysis of the signals that are being monitored by means of Non Destructive Tests (NDT) to determine the state of the structures, as well as in the optimal management of the structures. wind turbines and wind farms. OptiWindSeaPower has continued and completed this study initiated in the field of the rotating elements of the wind turbine and their maintenance management, making a more exhaustive study.\"<\/em><\/p><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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GRANT AGREEMENT ID: DPI2015-67264-P<\/h3>

GRANTED:\u00a0 \u00a0 \u00a0 \u00a0 \u20ac 99.900<\/h3>

FUNDED:<\/h3>

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Gesti\u00f3n Integral Optima de Parques E\u00f3licos Offshore Mediante Nuevos Modelos Matem\u00e1ticos (OptiWindSeaPower) <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The European Union’s energy and environmental policies are aimed at promoting and developing offshore wind platforms. This means that the Spanish electricity system will depend more and more on this type of electricity generation systems. The wind turbines for this case are larger, more complex, and require high demands for safety, reliability, availability and maintainability. In this project, this problem is addressed, with the final aim being the comprehensive and optimal management of this type of wind turbine parks.<\/p>

The project starts with the intention of continuing and completing the National WindSeaEnergy project (DPI2012-31579), where more than 300 scientific references were analyzed in high impact journals, observing that there are great deficiencies in mathematical models that allow analyzing the signals that are they are monitoring to determine the state of the structures, as well as the optimal management of wind turbines and wind farms.<\/p>

OptiWindSeaPower intends to continue and complete this study initiated in the field of the rotating elements of the wind turbine, and of the maintenance management thereof, making a more exhaustive study in the monitoring systems and methods of signal processing for the structural elements of said teams. The necessary data from WindSeaEnergy will be taken as a reference, as well as the European OPTIMUS, NIMO, WINPRO and the National IcingBlades projects. For this purpose, the design and development of a test bench of an ultrasound-based monitoring system will be required to determine the structural state of the wind turbines to complete this set of signals. A life cycle cost model for the predictive maintenance system will be developed. It is proposed to use mathematical models based on the analysis in time, frequency and time \/ frequency, as well as Transformed Wavellets, Neural Networks \/ Artificial Intelligence, methods based on the extraction of signal characteristics and derivatives of the System Transfer Function . The multivariate analysis will be done through Logical Decision Trees, which will be analyzed using Binary Decision Diagrams, and the important measures created by heuristic methods. This will allow controlling and optimizing the status of a wind turbine integrally. For the optimal management of the offshore wind farm, it will be considered as a Markovian decision problem, and the Restless Bandit indexes will be analyzed in order to determine the structure of the \u00abWhittle\u00bb indices in different contexts. Finally, new indexes of significance based on costs will be created and the problem of optimization and its resolution will be formulated through metaheuristic methods to determine the optimal investment policy in the management of this type of parks.<\/p>

The work team consists of members of the UCLM (6), the University College of Financial Studies of Madrid (3), and 5 of the universities of Drexel (USA), Birmingham (England), Monterrey (USA), King Fasial University (Saudi Arabia), Kyungpook National University (South Korea), with a clear multidisciplinary character, consisting of members specialized in Industrial Engineering, Economics, Statistics and Operations Research, and Energy.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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Tareas de Trabajo (TT)\u00a0<\/strong>y<\/strong><\/p>

Actividades Requeridas (i,ii,…)<\/strong><\/p><\/td>

TT<\/strong><\/p>

previas<\/strong><\/p><\/td>

Inicio-Final<\/strong><\/p>

(meses)<\/strong><\/p><\/td><\/tr>

TT1 Estado del Arte y Objetivos<\/strong><\/p><\/td>

\u00a0<\/strong><\/p><\/td>

1-48<\/strong><\/p><\/td><\/tr>

\u00a0\u00a0 1.i.\u00a0\u00a0 Estado del arte<\/p><\/td>

\u00a0<\/td>

1-2<\/p><\/td><\/tr>

\u00a0 1.ii.\u00a0\u00a0 Revisi\u00f3n de los objetivos principales<\/p><\/td>

TT1.i<\/p><\/td>

2-2.5<\/p><\/td><\/tr>

\u00a01.iii.\u00a0\u00a0\u00a0 Actualizaci\u00f3n del estado del arte y los objetivos<\/p><\/td>

TT1.(i,ii)<\/p><\/td>

2.5-48<\/p><\/td><\/tr>

TT2 Desarrollo de nuevos m\u00e9todos para el procesamiento de se\u00f1ales<\/strong><\/p><\/td>

\u00a0<\/strong><\/p><\/td>

2.5-20<\/strong><\/p><\/td><\/tr>

\u00a0\u00a0 2.i.\u00a0\u00a0 An\u00e1lisis de datos (WINPRO, NIMO, OPTIMUS,WindSeaEnergy)<\/p><\/td>

TT1.i<\/p><\/td>

2.5-4<\/p><\/td><\/tr>

\u00a0 2.ii.\u00a0\u00a0 Identificaci\u00f3n de Nuevos Modelos Matem\u00e1ticos (NMM)<\/p><\/td>

TT1.i<\/p><\/td>

4-8<\/p><\/td><\/tr>

\u00a02.iii.\u00a0\u00a0\u00a0 Desarrollo de NMM<\/p><\/td>

TT2.ii<\/p><\/td>

8-18<\/p><\/td><\/tr>

\u00a02.iv.\u00a0\u00a0 Control y Actualizaci\u00f3n de NMM<\/p><\/td>

TT2(ii,iii)<\/p><\/td>

14-20<\/p><\/td><\/tr>

TT3 Dise\u00f1o y Construcci\u00f3n de un Sistema de Monitorizaci\u00f3n<\/strong><\/p><\/td>

\u00a0<\/strong><\/p><\/td>

4-23<\/strong><\/p><\/td><\/tr>

\u00a0\u00a0 3.i.\u00a0\u00a0 Dise\u00f1o de un Sistema de Monitorizaci\u00f3n (SM)<\/p><\/td>

TT(1,2.i)<\/p><\/td>

4-8<\/p><\/td><\/tr>

\u00a0\u00a0\u00a0 3.ii.\u00a0 Desarrollo del SM<\/p><\/td>

TT3.i<\/p><\/td>

8-18<\/p><\/td><\/tr>

\u00a0\u00a0 3.iii.\u00a0 Implementaci\u00f3n de Sensores<\/p><\/td>

TT3.ii<\/p><\/td>

18-21<\/p><\/td><\/tr>

\u00a0 3.iv.\u00a0 Ajuste del Sistema<\/p><\/td>

TT3.iii<\/p><\/td>

21-23<\/p><\/td><\/tr>

TT4. An\u00e1lisis Coste del Ciclo de Vida del SM<\/strong><\/p><\/td>

\u00a0<\/td>

23-35<\/strong><\/p><\/td><\/tr>

\u00a0\u00a0 6.i.\u00a0\u00a0 Estudio de Inversiones y Costes de Operaci\u00f3n del (SM)<\/p><\/td>

TT3<\/p><\/td>

23-26<\/p><\/td><\/tr>

\u00a0 6.ii.\u00a0\u00a0 Desarrollo del Modelo del Coste del Ciclo de Vida del SM<\/p><\/td>

TT3.i<\/p><\/td>

26-32<\/p><\/td><\/tr>

\u00a06.iii.\u00a0\u00a0\u00a0 An\u00e1lisis del Coste del Ciclo de Vida<\/p><\/td>

TT3.ii<\/p><\/td>

32-35<\/p><\/td><\/tr>

TT5 Implementaci\u00f3n de modelos avanzados en la toma de decisiones (TD) Integrando los Distintos NMM<\/strong><\/p><\/td>

\u00a0<\/strong><\/p><\/td>

20-35<\/strong><\/p><\/td><\/tr>

\u00a0\u00a0\u00a0\u00a0 4.i.\u00a0 Dise\u00f1o y Desarrollo de TD<\/p><\/td>

TT2<\/p><\/td>

20-23<\/p><\/td><\/tr>

\u00a0\u00a0\u00a0 4.ii.\u00a0 An\u00e1lisis Cualitativo del TD<\/p><\/td>

TT4.i<\/p><\/td>

23-27<\/p><\/td><\/tr>

\u00a0\u00a0 4.iii.\u00a0 Conversi\u00f3n del TD al BDD<\/p><\/td>

TT4.ii<\/p><\/td>

27-30<\/p><\/td><\/tr>

\u00a0 4.iv.\u00a0 An\u00e1lisis Cuantitativo del TD<\/p><\/td>

TT4.i<\/p><\/td>

30-33<\/p><\/td><\/tr>

\u00a0\u00a0 4.v.\u00a0 Medidas de Importancia de los Eventos del TD<\/p><\/td>

TT4.iv<\/p><\/td>

33-35<\/p><\/td><\/tr>

TT6 Gesti\u00f3n \u00d3ptima de los Recursos Empleados en el Mantenimiento de los Parques E\u00f3licos Offshore<\/strong><\/p><\/td>

\u00a0<\/strong><\/p><\/td>

35-46<\/strong><\/p><\/td><\/tr>

\u00a0\u00a0 5.i.\u00a0\u00a0 Formulaci\u00f3n del Problema<\/p><\/td>

TT5<\/p><\/td>

35-37<\/p><\/td><\/tr>

\u00a0 5.ii.\u00a0\u00a0 Desarrollos de la Estructura de Costes, Probabilidades de Transici\u00f3n, Conjunto de Acciones, etc.<\/p><\/td>

TT4.i<\/p><\/td>

35-38<\/p><\/td><\/tr>

\u00a05.iii.\u00a0\u00a0\u00a0 Optimizaci\u00f3n de la Planificaci\u00f3n de las Pol\u00edticas de Mantenimiento y los Recursos Empleados en Mantenimiento<\/p><\/td>

TT6.ii<\/p><\/td>

38-40<\/p><\/td><\/tr>

\u00a05.iv.\u00a0\u00a0 An\u00e1lisis de los Indicadores de Transcendencia<\/p><\/td>

TT6.iii<\/p><\/td>

40-42<\/p><\/td><\/tr>

TT7. Optimizaci\u00f3n de inversiones basado en TT4, TT5 y TT6<\/strong><\/p><\/td>

\u00a0<\/td>

35-46<\/strong><\/p><\/td><\/tr>

\u00a0\u00a0 7.i.\u00a0\u00a0 Determinaci\u00f3n de las Inversiones de TD de TT5<\/p><\/td>

TT5<\/p><\/td>

35-38<\/p><\/td><\/tr>

\u00a0 7.ii.\u00a0\u00a0 Creaci\u00f3n de nuevos \u00edndices de significaci\u00f3n basados en costes<\/p><\/td>

TT5<\/p><\/td>

35-39<\/p><\/td><\/tr>

\u00a07.iii.\u00a0\u00a0\u00a0 Dise\u00f1o y Elaboraci\u00f3n de Modelo de Optimizaci\u00f3n de Inversiones<\/p><\/td>

TT7.ii<\/p><\/td>

39-43<\/p><\/td><\/tr>

\u00a07.iv.\u00a0\u00a0 An\u00e1lisis de los \u00edndices de significancia basados en costes<\/p><\/td>

TT7.iii<\/p><\/td>

43-46<\/p><\/td><\/tr>

TT8. Direcci\u00f3n, Difusi\u00f3n de Resultados y Trabajos Futuros<\/strong><\/p><\/td>

\u00a0<\/strong><\/p><\/td>

1-48<\/strong><\/p><\/td><\/tr>

\u00a0\u00a0 8.i.\u00a0\u00a0 Direcci\u00f3n de Proyecto<\/p><\/td>

Todas TT<\/p><\/td>

1-48<\/p><\/td><\/tr>

\u00a0 8.ii.\u00a0\u00a0 Estudiante(s) de Doctorado<\/p><\/td>

Todas TT<\/p><\/td>

1-48<\/p><\/td><\/tr>

\u00a08.iii.\u00a0\u00a0\u00a0 Difusi\u00f3n de Resultados<\/p><\/td>

Todas TT<\/p><\/td>

20-48<\/p><\/td><\/tr>

\u00a08.iv.\u00a0\u00a0 Determinaci\u00f3n y Preparaci\u00f3n de Futuros Trabajos<\/p><\/td>

Todas TT<\/p><\/td>

44-48<\/p><\/td><\/tr><\/tbody><\/table><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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2021<\/h2>

Isaac Segovia Ramirez, Behnam Mohammadi-Ivatloo, Fausto Pedro Garcia Marquez<\/span> (2021)<\/span> Alarms management by supervisory control and data acquisition system for wind turbines<\/span>, EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY<\/span> 23<\/span>(1)<\/span>, p. 110-116<\/span>, pdf<\/span><\/a><\/span>, doi:http:\/\/dx.doi.org\/10.17531\/ ein.2021.1.12.<\/span><\/a><\/span>
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2020<\/h2>

Bishwajit Dey, Fausto Pedro Garcia Marquez, Sourav Kr. Basak<\/span> (2020)<\/span> Smart Energy Management of Residential Microgrid System by a Novel Hybrid MGWOSCACSA Algorithm<\/span>, Energies<\/span> 13<\/span>(13)<\/span>, url<\/span><\/a><\/span>, doi:10.3390\/en13133500<\/span><\/a><\/span>
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PS Granados, Carlos Quiterio Gomez Mu\u00f1oz, Fausto Pedro Garcia Marquez<\/span> (2020)<\/span> Detection of structural defects in wind turbine blades employing guided waves and machine learning methods<\/span>, Developments in Renewable Energies Offshore<\/span>, p. 509-514<\/span>, CRC Press<\/span>, url<\/span><\/a><\/span>
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Carlos Quiterio Gomez Mu\u00f1oz, Christian Paredes Alvarez, Fausto Pedro Garcia Marquez<\/span> (2020)<\/span> Smart Farming: Intelligent Management Approach for Crop Inspection and Evaluation Employing Unmanned Aerial Vehicles<\/span>, International Conference on Management Science and Engineering Management<\/span>, p. 119-130<\/span>, Springer, Cham<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-030-49889-4_11<\/span><\/a><\/span>
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Jes\u00fas Jos\u00e9, Jes\u00fas Jos\u00e9 Llandres, Carlos Quiterio G\u00f3mez Mu\u00f1oz, Fausto Pedro Garcia Marquez<\/span> (2020)<\/span> Non-destructive methods for detection and localisation of partial discharges<\/span>, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets<\/span>, p. 177-193<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1016\/B978-0-08-101094-5.00012-5<\/span><\/a><\/span>
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Jorge Alonso Moro, Carlos Quitero G\u00f3mez Mu\u00f1oz, Fausto Pedro Garcia Marquez<\/span> (2020)<\/span> Predictive Analysis of Robotic Manipulators Through Inertial Sensors and Pattern Recognition<\/span>, Handbook of Research on Big Data Clustering and Machine Learning<\/span>, p. 334-344<\/span>, IGI-Global<\/span>, url<\/span><\/a><\/span>, doi:DOI: 10.4018 \/ 978-1-7998-0106-1.ch015<\/span><\/a><\/span>
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Omid Sadeghian, Arash Moradzadeh, Behnam Mohammadi-Ivatloo, Mehdi Abapour, Fausto Pedro Garcia Marquez<\/span> (2020)<\/span> Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach<\/span>, Energies<\/span> 13<\/span>(11)<\/span>, p. 2840<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.3390\/en13112840<\/span><\/a><\/span>
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Fausto Pedro Garcia Marquez, Isaac Segovia Ramirez, Behnam Mohammadi-Ivatloo, Alberto Pliego Marugan<\/span> (2020)<\/span> Reliability Dynamic Analysis by Fault Trees and Binary Decision Diagrams<\/span>, MDPI<\/span> 11<\/span>(6)<\/span>, p. 324<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.3390\/info11060324<\/span><\/a><\/span>
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Fausto Pedro Garcia Marquez, Alfredo Arcos Jimenez, Carlos Quiterio Gomez Mu\u00f1oz<\/span> (2020)<\/span> Chapter 6 – Non-destructive testing of wind turbines using ultrasonic waves<\/span>, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets<\/span>, Mayorkinos Papaelias, Fausto Pedro Garc\u00eda M\u00e1rquez, Alexander Karyotakis (ed.)<\/span>, p. 91-101<\/span>, Boston: Butterworth-Heinemann<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1016\/B978-0-08-101094-5.00006-X<\/span><\/a><\/span>
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Fausto Pedro Garcia Marquez, Jesus Maria Pinar Perez<\/span> (2020)<\/span> Chapter 4 – Non-destructive testing for the evaluation of icing blades in wind turbines<\/span>, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets<\/span>, Mayorkinos Papaelias, Fausto Pedro Garc\u00eda M\u00e1rquez, Alexander Karyotakis (ed.)<\/span>, p. 49-68<\/span>, Boston: Butterworth-Heinemann<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1016\/B978-0-08-101094-5.00004-6<\/span><\/a><\/span>
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Fausto Pedro Garcia Marquez, Jesus Maria Pinar Perez<\/span> (2020)<\/span> Chapter 1 – Wind turbines: A general reliability analysis<\/span>, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets<\/span>, Mayorkinos Papaelias, Fausto Pedro Garc\u00eda M\u00e1rquez, Alexander Karyotakis (ed.)<\/span>, p. 1-18<\/span>, Boston: Butterworth-Heinemann<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1016\/B978-0-08-101094-5.00001-0<\/span><\/a><\/span>
<\/p>\n

Alvaro Huerta Herraiz, Alberto Pliego Marugan, Isaac Segovia Ramirez, Mayorkinos Papaelias, Fausto Pedro Garcia Marquez<\/span> (2020)<\/span> Chapter 8 – Remotely operated vehicle applications<\/span>, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets<\/span>, Mayorkinos Papaelias, Fausto Pedro Garc\u00eda M\u00e1rquez, Alexander Karyotakis (ed.)<\/span>, p. 119-132<\/span>, Boston: Butterworth-Heinemann<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1016\/B978-0-08-101094-5.00008-3<\/span><\/a><\/span>
<\/p>\n

Alfredo Arcos Jimenez, Long Zhang, Carlos Quiterio Gomez Mu\u00f1oz, Fausto Pedro Garcia Marquez<\/span> (2020)<\/span> Maintenance management based on Machine Learning and nonlinear features in wind turbines<\/span>, Renewable Energy<\/span> 146<\/span>, p. 316-328<\/span>, Pergamon<\/span>, url<\/span><\/a><\/span>, doi:10.1016\/J.RENENE.2019.06.135<\/span><\/a><\/span>
<\/p>\n

2019<\/h2>

Fausto Pedro Garcia Marquez, Victoria Borja Moraleda, Alfredo Arcos Jimenez, Carlos Quiterio Gomez Mu\u00f1oz<\/span> (2019)<\/span> OptiWindSeaPower: Gesti\u00f3n Integral \u00d3ptima de Parques E\u00f3licos Offshore Mediante Nuevos Modelos Matem\u00e1ticos (2\u00aa parte)<\/span>, Asociaci\u00f3n Espa\u00f1ola de Ensayos No Destructivos<\/span> 89<\/span>, p. 34-48<\/span>, url<\/span><\/a><\/span>
<\/p>\n

Fausto Pedro Garcia Marquez, Isaac Segovia Ramirez, Alberto Pliego Marugan<\/span> (2019)<\/span> Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing<\/span>, Energies<\/span> 12<\/span>(9)<\/span>, pdf<\/span><\/a><\/span>, doi:10.3390\/en12091753<\/span><\/a><\/span>
<\/p>\n

Ana Maria Peco Chacon, Long Zhang, Fausto Pedro Garcia Marquez<\/span> (2019)<\/span> Maintenance Management in Wind Turbines by Monitoring the Bearing Temperature<\/span>, International Conference on Management Science and Engineering Management<\/span>, Cham Springer (ed.)<\/span>, p. 678-687<\/span>, Toronto: Springer<\/span>, url<\/span><\/a><\/span>, doi:DOI https:\/\/doi.org\/10.1007\/978-3-030-21248-3<\/span><\/a><\/span>
<\/p>\n

Alberto Pliego Marugan, Ana Maria Peco Chacon, Fausto Pedro Garcia Marquez<\/span> (2019)<\/span> Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines<\/span>, Reliability Engineering & System Safety<\/span> 191<\/span>, p. 106574<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1016\/j.ress.2019.106574<\/span><\/a><\/span>
<\/p>\n

Alberto Pliego Marugan, Fausto Pedro Garcia Marquez<\/span> (2019)<\/span> Advanced analytics for detection and diagnosis of false alarms and faults: A real case study<\/span>, Wind Energy<\/span> 0<\/span>(0)<\/span>, John Wiley & Sons, Ltd<\/span>, url<\/span><\/a><\/span>, doi:10.1002\/we.2393<\/span><\/a><\/span>
<\/p>\n

Alfredo Arcos Jimenez, Carlos Quiterio Gomez Mu\u00f1oz, Fausto Pedro Garcia Marquez<\/span> (2019)<\/span> Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers<\/span>, Reliability Engineering & System Safety<\/span> 184<\/span>, p. 2-12<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1016\/j.ress.2018.02.013<\/span><\/a><\/span>
<\/p>\n

Alfredo Arcos Jimenez, Fausto Pedro Garcia Marquez, Victoria Borja Moraleda, Carlos Quiterio Gomez Mu\u00f1oz<\/span> (2019)<\/span> Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis<\/span>, Renewable Energy<\/span> 132<\/span>, p. 1034-1048<\/span>, Pergamon<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1016\/j.renene.2018.08.050<\/span><\/a><\/span>
<\/p>\n

2018<\/h2>

Alberto Pliego Marugan, Ra\u00fal Ruiz de la Hermosa, Fausto Pedro Garcia Marquez<\/span> (2018)<\/span> Big Data and Wind Turbines Maintenance Management<\/span>, Renewable Energies<\/span>, p. 111-125<\/span>, Springer<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-319-45364-4_8<\/span><\/a><\/span>
<\/p>\n

Victoria Borja Moraleda, Alberto Pliego Marugan, Fausto Pedro Garcia Marquez<\/span> (2018)<\/span> Acoustic Maintenance Management Employing Unmanned Aerial Vehicles in Renewable Energies<\/span>, International Conference on Management Science and Engineering Management<\/span>, p. 969-981<\/span>, Springer, Cham<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-319-93351-1_76<\/span><\/a><\/span>
<\/p>\n

Carlos Quiterio Gomez Mu\u00f1oz, Fausto Pedro Garcia Marquez<\/span> (2018)<\/span> Future Maintenance Management in Renewable Energies<\/span>, Renewable Energies<\/span>, p. 149-159<\/span>, Springer<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-319-45364-4_10<\/span><\/a><\/span>
<\/p>\n

Alfredo Arcos Jimenez, Carlos Quiterio Gomez Mu\u00f1oz, Fausto Pedro Garcia Marquez<\/span> (2018)<\/span> Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers<\/span>, Reliability Engineering & System Safety<\/span>, Elsevier<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1016\/j.ress.2018.02.013<\/span><\/a><\/span>
<\/p>\n

Isaac Segovia Ramirez, Alberto Pliego Marugan, Fausto Pedro Garcia Marquez<\/span> (2018)<\/span> Remotely Piloted Aircraft System and Engineering Managenment: A Real Case Study<\/span>, International Conference on Management Science and Engineering Management<\/span>, p. 1173-1185<\/span>, Springer, Cham<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-319-93351-1_920<\/span><\/a><\/span>
<\/p>\n

Fausto Pedro Garcia Marquez, Carlos Quiterio Gomez Mu\u00f1oz, Isaac Segovia Ramirez<\/span> (2018)<\/span> Emisiones ac\u00fasticas y procesamiento de se\u00f1ales para la localizaci\u00f3n de defectos en materiales compuestos<\/span>, url<\/span><\/a><\/span>
<\/p>\n

Carlos Quiterio Gomez Mu\u00f1oz, Fausto Pedro Garcia Marquez<\/span> (2018)<\/span> Wind Energy Power Prospective<\/span>, Renewable Energies<\/span>, p. 83-95<\/span>, Springer<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-319-45364-4_6<\/span><\/a><\/span>
<\/p>\n

Alberto Pliego Marugan, Fausto Pedro Garcia Marquez, Jesus Mar\u00eda Pinar Perez, Diego Ruiz-Hern\u00e1ndez<\/span> (2018)<\/span> A survey of artificial neural network in wind energy systems<\/span>, Applied Energy<\/span>, url<\/span><\/a><\/span>, doi:10.1016\/j.apenergy.2018.07.084<\/span><\/a><\/span>
<\/p>\n

Jes\u00fas Mar\u00eda Pinar P\u00e9rez, Fausto Pedro Garcia Marquez<\/span> (2018)<\/span> Managing Costs and Review for Icing Problems<\/span>, Renewable Energies<\/span>, p. 97-109<\/span>, Springer<\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-319-45364-4_7<\/span><\/a><\/span>
<\/p>\n

Alfredo Arcos Jimenez, Carlos Quiterio Gomez Mu\u00f1oz, Fausto Pedro Garcia Marquez<\/span> (2018)<\/span> Machine Learning for Wind Turbine Blades Maintenance Management<\/span>, Energies<\/span> 11<\/span>(1)<\/span>, p. 13<\/span>, Multidisciplinary Digital Publishing Institute<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.3390\/en11010013<\/span><\/a><\/span>
<\/p>\n

Mayorkinos Papaelias, Fausto Pedro Garcia Marquez, Isaac Segovia Ramirez<\/span> (2018)<\/span> Concentrated Solar Power: Present and Future<\/span>, Renewable Energies<\/span>, p. 51-61<\/span>, Springer<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-319-45364-4_4<\/span><\/a><\/span>
<\/p>\n

Carlos Quiterio Gomez Mu\u00f1oz, Alfredo Arcos Jimenez, Fausto Pedro Garcia Marquez<\/span> (2018)<\/span> Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis<\/span>, Renewable Energy<\/span> 116<\/span>(B)<\/span>, p. 42-54<\/span>, Elsevier<\/span>, url<\/span><\/a><\/span>, doi:http:\/\/dx.doi.org\/10.1016\/j.renene.2017.03.052<\/span><\/a><\/span>
<\/p>\n

2017<\/h2>

Fausto Pedro Garcia Marquez, Alberto Pliego Marugan, Jesus Maria Pinar Perez, Mayorkinos Papaelias<\/span> (2017)<\/span> An Approach for Dynamic Analysis of Electrical\/Electronic Components in Wind Turbines<\/span>, Energies<\/span> 10<\/span>(8)<\/span>, url<\/span><\/a><\/span>
<\/p>\n

Kamal Mohammedi, Tahar Benmessaoud, Fausto Pedro Garcia Marquez, Alberto Pliego Marugan<\/span> (2017)<\/span> Fuzzy Logic Applied to SCADA Systems<\/span>, Springer<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-319-59280-0_61<\/span><\/a><\/span>
<\/p>\n

Carlos Quiterio Gomez Mu\u00f1oz, Fausto Pedro Garcia Marquez, Benjamin Lev, Alfredo Arcos Jimenez<\/span> (2017)<\/span> New Pipe Notch Detection and Location Method for Short Distances employing Ultrasonic Guided Waves<\/span>, Acta Acustica united with Acustica<\/span>, p. 772-781<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.3813\/AAA.919106<\/span><\/a><\/span>
<\/p>\n

Fausto Pedro Garcia Marquez, Alberto Pliego Marugan, Jes\u00fas Mar\u00eda Pinar P\u00e9rez, Stuart Hillmansen, Mayorkinos Papaelias<\/span> (2017)<\/span> Optimal dynamic analysis of electrical\/electronic components in wind turbines<\/span>, Energies<\/span> 10<\/span>(8)<\/span>, p. 1111<\/span>, Multidisciplinary Digital Publishing Institute<\/span>, url<\/span><\/a><\/span>, doi:doi:10.3390\/en10081111<\/span><\/a><\/span>
<\/p>\n

Fausto Pedro Garcia Marquez, Alberto Pliego Marugan<\/span> (2017)<\/span> SCADA and Artificial neural networks for maintenance management<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-319-59280-0_75<\/span><\/a><\/span>
<\/p>\n

Carlos Quiterio Gomez Mu\u00f1oz, Alfredo Arcos Jimenez, Fausto Pedro Garcia Marquez, Maria Kogia, Liang Cheng, Abbas Mohimi, Mayorkinos Papaelias<\/span> (2017)<\/span> Cracks and Welds Detection Approach in Solar Receiver Tubes Employing EMATs<\/span>, Structural Health Monitoring<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1177\/1475921717734501<\/span><\/a><\/span>
<\/p>\n

Alberto Pliego Marugan, Fausto Pedro Garcia Marquez<\/span> (2017)<\/span> SCADA and Artificial Neural Networks for Maintenance Management<\/span>, International Conference on Management Science and Engineering Management<\/span>, p. 912-919<\/span>, Springer, Cham<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-319-59280-0_75<\/span><\/a><\/span>
<\/p>\n

Fausto Pedro Garcia Marquez, Carlos Quiterio Gomez Mu\u00f1oz, Benjamin Lev, Alfredo Arcos Jimenez<\/span> (2017)<\/span> New Pipe Notch Detection and Location Method for Short Distances employing Ultrasonic Guided Waves<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.3813\/AAA.919106<\/span><\/a><\/span>
<\/p>\n

Tahar Benmessaoud, Alberto Pliego Marugan, Kamal Mohammedi, Fausto Pedro Garcia Marquez<\/span> (2017)<\/span> Fuzzy Logic Applied to SCADA Systems<\/span>, International Conference on Management Science and Engineering Management<\/span>, p. 749-757<\/span>, Springer, Cham<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-319-59280-0_61<\/span><\/a><\/span>
<\/p>\n

Fausto Pedro Garcia Marquez, Carlos Quiterio Gomez Mu\u00f1oz, Isaac Segovia Ramirez<\/span> (2017)<\/span> Emisiones Ac\u00fasticas y Procesamiento de Se\u00f1ales para la Localizaci\u00f3n de Defectos en Materiales Compuestos<\/span>, url<\/span><\/a><\/span>
<\/p>\n

Carlos Quiterio Gomez Mu\u00f1oz, Alfredo Arcos Jimenez, Liang Cheng, Fausto Pedro Garcia Marquez, Maria Kogia, Abbas Mohimi, Mayorkinos Papelias<\/span> (2017)<\/span> Cracks and weld detection approach in solar receiver tubes employing EMATS<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1177\/1475921717734501<\/span><\/a><\/span>
<\/p>\n

Alberto Pliego Marugan, Fausto Pedro Garcia Marquez, Benjamin Lev<\/span> (2017)<\/span> Optimal decision-making via binary decision diagrams for investments under a risky environment<\/span>, International Journal of Production Research<\/span> 55<\/span>(18)<\/span>, p. 5271-5286<\/span>, Taylor & Francis<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1080\/00207543.2017.1308570<\/span><\/a><\/span>
<\/p>\n

Carlos Quiterio Gomez Mu\u00f1oz, Alfredo Arcos Jimenez, Fausto Pedro Garcia Marquez, Maria Kogia, Liang Cheng, Abbas Mohimi, Mayorkinos Papaelias<\/span> (2017)<\/span> Cracks and welds detection approach in solar receiver tubes employing electromagnetic acoustic transducers<\/span>, Structural Health Monitoring<\/span>, p. 1475921717734501<\/span>, SAGE Publications Sage UK: London, England<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1177\/1475921717734501<\/span><\/a><\/span>
<\/p>\n

Alfredo Arcos Arcos Jimenez, Carlos Quiterio Gomez Mu\u00f1oz, Fausto Pedro Garcia Marquez<\/span> (2017)<\/span> Machine Learning and Neural Network for Maintenance Management<\/span>, International Conference on Management Science and Engineering Management<\/span>, p. 1377-1388<\/span>, Springer, Cham<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.1007\/978-3-319-59280-0_115<\/span><\/a><\/span>
<\/p>\n

2016<\/h2>

Fausto Pedro Garcia Marquez, Maria Kogia, Liang Cheng, Alfredo Arcos Jimenez, Carlos Quiterio Gomez Mu\u00f1oz, Mayorkinos Papaelias<\/span> (2016)<\/span> Calculus of the defect severity with EMATs by analyzing the attenuation curves of the guided waves<\/span>, url<\/span><\/a><\/span>, doi:https:\/\/doi.org\/10.12989\/sss.2017.19.2.195<\/span><\/a><\/span>
<\/p>\n

2013<\/h2>

Jes\u00fas Mar\u00eda Pinar P\u00e9rez, Fausto Pedro Garcia Marquez, Andrew Tobias, Mayorkinos Papaelias<\/span> (2013)<\/span> Wind turbine reliability analysis<\/span>, Renewable and Sustainable Energy Reviews<\/span> 23<\/span>, p. 463-472<\/span>, Pergamon<\/span>, url<\/span><\/a><\/span>, doi:http:\/\/dx.doi.org\/10.1016\/j.rser.2013.03.018<\/span><\/a><\/span>
<\/p>\n

J M Chac\u00f3n Munoz, Fausto Pedro Garcia Marquez, Mayorkinos Papaelias<\/span> (2013)<\/span> Railroad inspection based on ACFM employing a non-uniform B-spline approach<\/span>, Mechanical Systems and Signal Processing<\/span> 40<\/span>(2)<\/span>, p. 605-617<\/span>, Academic Press<\/span>, url<\/span><\/a><\/span>, doi:doi: 10.1016\/j.ymssp.2013.05.004<\/span><\/a><\/span>
<\/p>\n

2003<\/h2>

Fausto Pedro Garcia Marquez, Felix Schmid, Javier Conde Collado<\/span> (2003)<\/span> A reliability centered approach to remote condition monitoring. A railway points case study<\/span>, Reliability Engineering & System Safety<\/span> 80<\/span>(1)<\/span>, p. 33-40<\/span>, Elsevier<\/span>, doi:https:\/\/doi.org\/10.1016\/S0951-8320(02)00166-7<\/span><\/a><\/span>
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