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Prof. Carlos Quiterio Gomez Muñoz
Villaviciosa de Odón, Universidad Europea de Madrid, 28670 Madrid, Spain

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Research Keywords & Expertise

0 Electronics
0 Piezoelectric
0 Ultrasonic Testing
0 Computer vision with deep learning
0 neural networks & deep learning

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guided waves
Piezoelectric
Ultrasonic Testing
Computer vision with deep learning
neural networks & deep learning

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Chapter
Published: 14 July 2021 in Internet of Things
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One of the greatest needs today in road safety and its conservation work is to obtain traffic data in real time to predict traffic and increase safety of people. In this work, a camera embedded in an Unmanned Automatic Vehicle in static flight has been used to get information about traffic in a roundabout. These infrastructures are key since they are considered conflictive points in the circulation flow, and it is complex to analyze. A system has been developed to analyze images online and that obtains vehicle behavior data in real time. The system offers information such as vehicle count, their instantaneous speed at each moment, average speed of each one, individual trajectory, traffic density, lane changes, trouble spots, etc. The information provided by this system allows a better decision-making, increased security, improved traffic flow, and how to schedule maintenance tasks carried out by conservatives.

ACS Style

Paloma Peiro; Carlos Quiterio Gómez Muñoz; Fausto Pedro Garcíamárquez. Use of UAVS, Computer Vision, and IOT for Traffic Analysis. Internet of Things 2021, 275 -296.

AMA Style

Paloma Peiro, Carlos Quiterio Gómez Muñoz, Fausto Pedro Garcíamárquez. Use of UAVS, Computer Vision, and IOT for Traffic Analysis. Internet of Things. 2021; ():275-296.

Chicago/Turabian Style

Paloma Peiro; Carlos Quiterio Gómez Muñoz; Fausto Pedro Garcíamárquez. 2021. "Use of UAVS, Computer Vision, and IOT for Traffic Analysis." Internet of Things , no. : 275-296.

Review
Published: 04 November 2020 in Energies
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India, Pakistan, and Bangladesh (IPB) are the largest South Asian countries in terms of land area, gross domestic product (GDP), and population. The growth in these countries is impacted by inadequate renewable energy policy and implementation over the years, resulting in slow progress towards human development and economic sustainability. These developing countries are blessed with huge potential for renewable energy resources; however, they still heavily rely on fossil fuels (93%). IPB is a major contributor to the total energy consumption of the world and its most energy-intensive building sector (India 47%, Pakistan 55% and Bangladesh 55%) displays inadequate energy performance. This paper comprehensively reviews the energy mix and consumption in IPB with special emphasis on current policies and its impact on economic and human development. The main performance indicators have been critically analyzed for the period 1970–2017. The strength of this paper is a broad overview on energy and development of energy integration in major South Asian countries. Furthermore, it presents a broad deepening on the main sector of energy consumption, i.e., the building sector. The paper also particularly analyzes the existing buildings energy efficiency codes and policies, with specific long-term recommendations to improve average energy consumption per person. The study also examines the technical and regulatory barriers and recommends specific measures to adapt renewable technologies, with special attention to policies affecting energy consumption. The analysis and results are general and can be applied to other developing countries of the world.

ACS Style

Rashiqa Abdul Salam; Khuram Pervez Amber; Naeem Iqbal Ratyal; Mehboob Alam; Naveed Akram; Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez. An Overview on Energy and Development of Energy Integration in Major South Asian Countries: The Building Sector. Energies 2020, 13, 5776 .

AMA Style

Rashiqa Abdul Salam, Khuram Pervez Amber, Naeem Iqbal Ratyal, Mehboob Alam, Naveed Akram, Carlos Quiterio Gómez Muñoz, Fausto Pedro García Márquez. An Overview on Energy and Development of Energy Integration in Major South Asian Countries: The Building Sector. Energies. 2020; 13 (21):5776.

Chicago/Turabian Style

Rashiqa Abdul Salam; Khuram Pervez Amber; Naeem Iqbal Ratyal; Mehboob Alam; Naveed Akram; Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez. 2020. "An Overview on Energy and Development of Energy Integration in Major South Asian Countries: The Building Sector." Energies 13, no. 21: 5776.

Journal article
Published: 01 November 2020 in Energies
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This article presents a remote management architecture of an unmanned aerial vehicles (UAVs) fleet to aid in the management of solar power plants and object tracking. The proposed system is a competitive advantage for sola r energy production plants, due to the reduction in costs for maintenance, surveillance, and security tasks, especially in large solar farms. This new approach consists of creating a hardware and software architecture that allows for performing different tasks automatically, as well as remotely using fleets of UAVs. The entire system, composed of the aircraft, the servers, communication networks, and the processing center, as well as the interfaces for accessing the services via the web, has been designed for this specific purpose. Image processing and automated remote control of the UAV allow generating autonomous missions for the inspection of defects in solar panels, saving costs compared to traditional manual inspection. Another application of this architecture related to security is the detection and tracking of pedestrians and vehicles, both for road safety and for surveillance and security issues of solar plants. The novelty of this system with respect to current systems is summarized in that all the software and hardware elements that allow the inspection of solar panels, surveillance, and people counting, as well as traffic management tasks, have been defined and detailed. The modular system presented allows the exchange of different specific vision modules for each task to be carried out. Finally, unlike other systems, calibrated fixed cameras are used in addition to the cameras embedded in the drones of the fleet, which complement the system with vision algorithms based on deep learning for identification, surveillance, and inspection.

ACS Style

Sergio Bemposta Rosende; Javier Sánchez-Soriano; Carlos Quiterio Gómez Muñoz; Javier Fernández Andrés. Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants. Energies 2020, 13, 5712 .

AMA Style

Sergio Bemposta Rosende, Javier Sánchez-Soriano, Carlos Quiterio Gómez Muñoz, Javier Fernández Andrés. Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants. Energies. 2020; 13 (21):5712.

Chicago/Turabian Style

Sergio Bemposta Rosende; Javier Sánchez-Soriano; Carlos Quiterio Gómez Muñoz; Javier Fernández Andrés. 2020. "Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants." Energies 13, no. 21: 5712.

Journal article
Published: 27 August 2020 in Applied Sciences
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The main drawback in many electronic devices is the duration of their batteries. Energy harvesting provides a solution for these low-consumption devices. Piezoelectric energy harvesting use is growing because it collects small amounts of clean energy and transforms it to electricity. Synthetic piezoelectric materials are a feasible alternative to generate energy for low consumption systems. In addition to the energy generation, each pressure cycle in the piezoelectric material can provide information for the device, for example, counting the passage of people. The main contribution of this work is to study, build, and test a low-cost energy harvesting floor using piezoelectric transducers to estimate the amount of energy that could be produced for a connected device. Several piezoelectric transducers have been employed and analyzed, providing accurate results.

ACS Style

Carlos Quiterio Gómez Muñoz; Gabriel Zamacola Alcalde; Fausto Pedro García Márquez. Analysis and Comparison of Macro Fiber Composites and Lead Zirconate Titanate (PZT) Discs for an Energy Harvesting Floor. Applied Sciences 2020, 10, 5951 .

AMA Style

Carlos Quiterio Gómez Muñoz, Gabriel Zamacola Alcalde, Fausto Pedro García Márquez. Analysis and Comparison of Macro Fiber Composites and Lead Zirconate Titanate (PZT) Discs for an Energy Harvesting Floor. Applied Sciences. 2020; 10 (17):5951.

Chicago/Turabian Style

Carlos Quiterio Gómez Muñoz; Gabriel Zamacola Alcalde; Fausto Pedro García Márquez. 2020. "Analysis and Comparison of Macro Fiber Composites and Lead Zirconate Titanate (PZT) Discs for an Energy Harvesting Floor." Applied Sciences 10, no. 17: 5951.

Journal article
Published: 05 March 2020 in Energies
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Wind turbine blades are constantly submitted to different types of particles such as dirt, ice, etc., as well as all the different environmental parameters that affect the behaviour and efficiency of the energy generation system. These parameters can cause faults to the wind turbine blades, modifying their behaviour due, for example, to the turbulence. A new method is presented in this paper based on cross-correlations to determine the presence of delamination in the blades. The experiments were conducted in two real wind turbine blades to analyse the fault and non-fault blades using ultrasonic guided waves. Finally, the energy analysis of the signal based on wavelet transforms allowed to determine energies abrupt changes in the correlation of the signals and to locate the faults.

ACS Style

Fausto Pedro García Marquez; Carlos Quiterio Gómez Muñoz. A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing. Energies 2020, 13, 1192 .

AMA Style

Fausto Pedro García Marquez, Carlos Quiterio Gómez Muñoz. A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing. Energies. 2020; 13 (5):1192.

Chicago/Turabian Style

Fausto Pedro García Marquez; Carlos Quiterio Gómez Muñoz. 2020. "A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing." Energies 13, no. 5: 1192.

Journal article
Published: 28 June 2019 in Renewable Energy
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Delamination is a common problem in wind turbine blades, creating stress concentration areas that can lead to the partial or complete rupture of the blade. This paper presents a novel delamination classification approach for reliability monitoring systems in wind turbine blades. It is based on the feature extraction of a nonlinear autoregressive with exogenous input system (NARX) and linear auto-regressive model (AR). A novelty in this paper is NARX as a Feature Extraction method for wind turbine blade delamination classification. Further, the NARX feature is demonstrated to be significantly better than linear AR feature for blade damage detection, and NARX can describe the inherent nonlinearity of blade delamination correctly. A real case study considers different levels of delamination employing ultrasonic guided waves that are sensitive to delamination. Firstly, the signals obtained are filtered and de-noised by wavelet transforms. Then, the features of the signal are extracted by NARX, and the number of features is selected considering the Neighbourhood Component Analysis as main novelties. Finally, six scenarios with different delamination sizes have been performed by supervised Machine Learning methods: Decision Trees, Discriminant Analysis, Quadratic Support Vector Machines, Nearest Neighbours and Ensemble Classification.

ACS Style

Alfredo Arcos Jiménez; Long Zhang; Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez. Maintenance management based on Machine Learning and nonlinear features in wind turbines. Renewable Energy 2019, 146, 316 -328.

AMA Style

Alfredo Arcos Jiménez, Long Zhang, Carlos Quiterio Gómez Muñoz, Fausto Pedro García Márquez. Maintenance management based on Machine Learning and nonlinear features in wind turbines. Renewable Energy. 2019; 146 ():316-328.

Chicago/Turabian Style

Alfredo Arcos Jiménez; Long Zhang; Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez. 2019. "Maintenance management based on Machine Learning and nonlinear features in wind turbines." Renewable Energy 146, no. : 316-328.

Journal article
Published: 13 May 2019 in International Journal of Disaster Risk Reduction
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In the last few years, emergencies have brought about catastrophic casualties and property loss. After an emergency, the emergency manager who is in charge of the rescue work has to identify an optimal rescue plan from several alternatives. Therefore, it is necessary to develop an effective method for selecting an optimal rescue plan to reduce the damages of emergencies. Usually, an emergency decision has three features: time urgency, incomplete information and potential severe results. While it is difficult to provide the accurate numerical evaluations with the incomplete information under limited time, the probabilistic linguistic term set (PLTS) is introduced to represent the opinions of emergency decision making panel (EDMP) in this paper. In this way, EDMP can provide suggestions with linguistic terms, and the manager can directly collect qualitative opinions in a short time. In addition, fault tree analysis (FTA) is used to evaluate the probabilities of potential severe results, and analysis network process (ANP) is adopted to derive the weights of criteria that determining the damages of potential severe results. Then, we can quantify the potential severe results of alternatives so as to choose the one with the minimum damages. In this study, we first attempt to integrate PLTS and FTA-ANP method to comprehensively consider the three features of emergency decision to select an optimal rescue plan. Finally, a case study is employed to verify the feasibility of the proposed method, and the comparisons and discussion are presented to show the rationality and novelty of this study.

ACS Style

Xiaoyang Zhou; Liqin Wang; Jindong Qin; Jian Chai; Carlos Quiterio Gómez Muñoz. Emergency rescue planning under probabilistic linguistic information: An integrated FTA-ANP method. International Journal of Disaster Risk Reduction 2019, 37, 101170 .

AMA Style

Xiaoyang Zhou, Liqin Wang, Jindong Qin, Jian Chai, Carlos Quiterio Gómez Muñoz. Emergency rescue planning under probabilistic linguistic information: An integrated FTA-ANP method. International Journal of Disaster Risk Reduction. 2019; 37 ():101170.

Chicago/Turabian Style

Xiaoyang Zhou; Liqin Wang; Jindong Qin; Jian Chai; Carlos Quiterio Gómez Muñoz. 2019. "Emergency rescue planning under probabilistic linguistic information: An integrated FTA-ANP method." International Journal of Disaster Risk Reduction 37, no. : 101170.

Journal article
Published: 01 April 2019 in Reliability Engineering & System Safety
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ACS Style

Alfredo Arcos Jiménez; Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez. Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers. Reliability Engineering & System Safety 2019, 184, 2 -12.

AMA Style

Alfredo Arcos Jiménez, Carlos Quiterio Gómez Muñoz, Fausto Pedro García Márquez. Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers. Reliability Engineering & System Safety. 2019; 184 ():2-12.

Chicago/Turabian Style

Alfredo Arcos Jiménez; Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez. 2019. "Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers." Reliability Engineering & System Safety 184, no. : 2-12.

Research article
Published: 29 January 2019 in Wind Energy
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Wind power is becoming one of the most important renewable energies in the world. The reduction in operating and maintenance costs of the wind turbines has been identified as one of the biggest challenges to establish this energy as an alternative to fossil fuels. Predictive maintenance can detect a potential failure at an early stage reducing operating costs. Structural health monitoring together with non‐destructive techniques are an effective method to detect incipient delamination in wind turbine blades. Ultrasonic guided waves offer possibilities to inspect delamination and disunion between layers in composite structures. Delamination results in a concentration of tensions in certain areas near the fault, which can propagate and create the total break of the blade. This paper presents a new approach for disunity detection between layers comparing two real blades, also new in the literature, one of them built with three disbonds introduced in its manufacturing process. The signals are denoised by Daubechies wavelet transform. The threshold for the denoising is obtained by a wavelet coefficients selection rule using the Birgé‐Massart penalization method. The signals were normalized and their envelopes were obtained by Hilbert transform. Finally, a pattern recognition based on correlations was applied.

ACS Style

Carlos Quiterio Gómez Muñoz; Fausto Pedro García Marquez; Borja Hernandez Crespo; Kena Makaya. Structural health monitoring for delamination detection and location in wind turbine blades employing guided waves. Wind Energy 2019, 22, 698 -711.

AMA Style

Carlos Quiterio Gómez Muñoz, Fausto Pedro García Marquez, Borja Hernandez Crespo, Kena Makaya. Structural health monitoring for delamination detection and location in wind turbine blades employing guided waves. Wind Energy. 2019; 22 (5):698-711.

Chicago/Turabian Style

Carlos Quiterio Gómez Muñoz; Fausto Pedro García Marquez; Borja Hernandez Crespo; Kena Makaya. 2019. "Structural health monitoring for delamination detection and location in wind turbine blades employing guided waves." Wind Energy 22, no. 5: 698-711.

Journal article
Published: 16 August 2018 in Renewable Energy
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The mass of ice on wind turbines blades is one of the main problems that energy companies have in cold climates. This paper presents a novel approach to detect and classify ice thickness based on pattern recognition through guided ultrasonic waves and Machine Learning. To successfully achieve a supervised classification, it is necessary to employ a method that allows the correct extraction and selection of features of the ultrasonic signal. The main novelty in this work is that the approach considers four feature extraction methods to validate the results, grouped by linear (AutoRegressive (AR) and Principal Component Analysis) and nonlinear (nonlinear-AR eXogenous and Hierarchical Non-Linear Principal Component Analysis), and feature selection is done by Neighbourhood Component Analysis. A supervised classification was performed through Machine Learning with twenty classifiers such as Decision tree, Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, and Ensemble Classifiers. Finally, an evaluation of the classifiers was done in single frequency and multi-frequency modes, obtaining accurate results.

ACS Style

Alfredo Arcos Jiménez; Fausto Pedro García Márquez; Victoria Borja Moraleda; Carlos Quiterio Gómez Muñoz. Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis. Renewable Energy 2018, 132, 1034 -1048.

AMA Style

Alfredo Arcos Jiménez, Fausto Pedro García Márquez, Victoria Borja Moraleda, Carlos Quiterio Gómez Muñoz. Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis. Renewable Energy. 2018; 132 ():1034-1048.

Chicago/Turabian Style

Alfredo Arcos Jiménez; Fausto Pedro García Márquez; Victoria Borja Moraleda; Carlos Quiterio Gómez Muñoz. 2018. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis." Renewable Energy 132, no. : 1034-1048.

Chapter
Published: 17 February 2018 in Renewable Energies
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This chapter describes the future on maintenance management in renewable energy industry. The main advances and research studies shows that it will be based on non-destructive testing (NDT). NDT are tests performed to detect internal or surface discontinuities in materials or determine certain properties. NDT leads to improvement of product quality, public safety and prevention of catastrophic failures. NDT techniques are used in Structural Health Monitoring (SHM) systems for Fault Detection and Diagnosis (FDD). Some NDT techniques are used to prevent serious failures in critical components such as blades, gearbox, tower or receiver tubes. NDT is increasing in many scientific and industrial fields, from wind energy production to the transportation of gases and liquids. Consequently, it is possible to reduce the corrective/preventive maintenance tasks, and to increase the life cycle of the structure.

ACS Style

Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez. Future Maintenance Management in Renewable Energies. Renewable Energies 2018, 149 -159.

AMA Style

Carlos Quiterio Gómez Muñoz, Fausto Pedro García Márquez. Future Maintenance Management in Renewable Energies. Renewable Energies. 2018; ():149-159.

Chicago/Turabian Style

Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez. 2018. "Future Maintenance Management in Renewable Energies." Renewable Energies , no. : 149-159.

Chapter
Published: 17 February 2018 in Renewable Energies
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Wind energy and its perspective is introduced and described in this chapter. Wind farms, in contrast to conventional power plants, are exposed to the inclement and variability of weather. As a result of these variations, wind turbines are subjected to high mechanical loads, which require a high level of maintenance to provide a cost-effective power output and care the life cycle of the equipment. The demand for wind energy continues to rise at an exponential rate, due to the reduction in operating and maintenance costs and increasing reliability of wind turbines. Wind turbines make use of condition monitoring systems that allow information to be gathered regarding the condition of the main components, and determine anomalous operating situations. The power generation plants have incorporated a basic online monitoring control system. This system generally includes sensors for monitoring key machine parameters, such as temperature, speed, fluid levels, unbalance in the rotor, etc.

ACS Style

Carlos Quiterio Gómez Muñoz; Fausto Pedro García Marquez. Wind Energy Power Prospective. Renewable Energies 2018, 83 -95.

AMA Style

Carlos Quiterio Gómez Muñoz, Fausto Pedro García Marquez. Wind Energy Power Prospective. Renewable Energies. 2018; ():83-95.

Chicago/Turabian Style

Carlos Quiterio Gómez Muñoz; Fausto Pedro García Marquez. 2018. "Wind Energy Power Prospective." Renewable Energies , no. : 83-95.

Journal article
Published: 01 February 2018 in Renewable Energy
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ACS Style

Carlos Quiterio Gómez Muñoz; Alfredo Arcos Jiménez; Fausto Pedro García Márquez. Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis. Renewable Energy 2018, 116, 42 -54.

AMA Style

Carlos Quiterio Gómez Muñoz, Alfredo Arcos Jiménez, Fausto Pedro García Márquez. Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis. Renewable Energy. 2018; 116 ():42-54.

Chicago/Turabian Style

Carlos Quiterio Gómez Muñoz; Alfredo Arcos Jiménez; Fausto Pedro García Márquez. 2018. "Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis." Renewable Energy 116, no. : 42-54.

Journal article
Published: 21 December 2017 in Energies
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Delamination in Wind Turbine Blades (WTB) is a common structural problem that can generate large costs. Delamination is the separation of layers of a composite material, which produces points of stress concentration. These points suffer greater traction and compression forces in working conditions, and they can trigger cracks, and partial or total breakage of the blade. Early detection of delamination is crucial for the prevention of breakages and downtime. The main novelty presented in this paper has been to apply an approach for detecting and diagnosing the delamination WTB. The approach is based on signal processing of guided waves, and multiclass pattern recognition using machine learning. Delamination was induced in the WTB to check the accuracy of the approach. The signal is denoised by wavelet transform. The autoregressive Yule–Walker model is employed for feature extraction, and Akaike’s information criterion method for feature selection. The classifiers are quadratic discriminant analysis, k-nearest neighbors, decision trees, and neural network multilayer perceptron. The confusion matrix is employed to evaluate the classification, especially the receiver operating characteristic analysis by: recall, specificity, precision, and F-score.

ACS Style

Alfredo Arcos Jiménez; Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez. Machine Learning for Wind Turbine Blades Maintenance Management. Energies 2017, 11, 13 .

AMA Style

Alfredo Arcos Jiménez, Carlos Quiterio Gómez Muñoz, Fausto Pedro García Márquez. Machine Learning for Wind Turbine Blades Maintenance Management. Energies. 2017; 11 (1):13.

Chicago/Turabian Style

Alfredo Arcos Jiménez; Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez. 2017. "Machine Learning for Wind Turbine Blades Maintenance Management." Energies 11, no. 1: 13.

Research article
Published: 11 October 2017 in Structural Health Monitoring
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There is a significant rising in development of new concentrated solar plants due to global energy demands. Concentrated solar plant requires to improve the operational and maintainability in this industry. This article presents a new approach to identify defects in the solar receiver tubes and welds employing a simple electromagnetic acoustic transducer. The absorber tubes in normal working conditions must withstand high temperatures, which can cause the tubes to deteriorate in areas such as welding, or it can cause hot spots due to defects or corrosion. A proper predictive maintenance program for the absorber pipes is required to detect defects in the tubes at an early stage, reducing corrective maintenance costs and increasing the reliability, availability, and safety of the concentrated solar plant. This article presents a novel approach based on signal processing and pattern recognition for predictive maintenance employing electromagnetic acoustic transducers. Hilbert transform is used to obtain the envelope of the signal that is smoothed by wavelet transform. It reduces the probability of detecting false-positive alarms. The algorithm uses the distance of the sensors from the edges to perform a self-identification of signal events. The events are located using two possible ways of ultrasound propagation, forward and reverse, and the time of flight of each echo. The algorithm correlates the theoretical events with events found experimentally. These echoes could come from different paths due to the electromagnetic acoustic transducer that generates forward and reverse shear waves. The main novelty in this approach is that the detection and location of the defect is determined considering two echoes that come from the same defect, but they arrive at the sensor flowing by different paths. The results are obtained with a double validation by matching the echoes that meet certain conditions. It increases the accuracy of the inspection and reduces false alarms. The approach has been tested and validated in an experimental platform that simulates the concentrated solar plants.

ACS Style

Carlos Quiterio Gómez Muñoz; Alfredo Arcos Jiménez; Fausto Pedro García Marquez; Maria Kogia; Liang Cheng; Abbas Mohimi; Mayorkinos Papaelias. Cracks and welds detection approach in solar receiver tubes employing electromagnetic acoustic transducers. Structural Health Monitoring 2017, 17, 1046 -1055.

AMA Style

Carlos Quiterio Gómez Muñoz, Alfredo Arcos Jiménez, Fausto Pedro García Marquez, Maria Kogia, Liang Cheng, Abbas Mohimi, Mayorkinos Papaelias. Cracks and welds detection approach in solar receiver tubes employing electromagnetic acoustic transducers. Structural Health Monitoring. 2017; 17 (5):1046-1055.

Chicago/Turabian Style

Carlos Quiterio Gómez Muñoz; Alfredo Arcos Jiménez; Fausto Pedro García Marquez; Maria Kogia; Liang Cheng; Abbas Mohimi; Mayorkinos Papaelias. 2017. "Cracks and welds detection approach in solar receiver tubes employing electromagnetic acoustic transducers." Structural Health Monitoring 17, no. 5: 1046-1055.

Journal article
Published: 21 September 2017 in Eksploatacja i Niezawodnosc - Maintenance and Reliability
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ACS Style

Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez; Alfredo Arcos Jimenez; Liang Cheng; Maria Kogia; Abbas Mohimi; Mayorkinos Papaelias. A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2017, 19, 493 -500.

AMA Style

Carlos Quiterio Gómez Muñoz, Fausto Pedro García Márquez, Alfredo Arcos Jimenez, Liang Cheng, Maria Kogia, Abbas Mohimi, Mayorkinos Papaelias. A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers. Eksploatacja i Niezawodnosc - Maintenance and Reliability. 2017; 19 (4):493-500.

Chicago/Turabian Style

Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez; Alfredo Arcos Jimenez; Liang Cheng; Maria Kogia; Abbas Mohimi; Mayorkinos Papaelias. 2017. "A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers." Eksploatacja i Niezawodnosc - Maintenance and Reliability 19, no. 4: 493-500.

Journal article
Published: 01 September 2017 in Acta Acustica united with Acustica
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This paper presents a novel signal processing approach that is able to automatically identify notches in pipelines in short distances. In addition, this method locates the geometric position of the notch and determines the size. The approach for fault detection and diagnosis presented look for a solution and then validates the solution by analyzing the signal which flows in the opposite direction. Micro Fiber Composite (MFC) transducers are used in an austenitic stainless steel pipeline, used in solar concentrators, in order to generate Ultrasonic Guided Waves. The main results presented in this paper can be summarized as: identification of edges or welds by multi-parametric analysis and comparison with the theoretical results predicted, notch location in the pipe by comparison of the position of echoes weighted with their amplitudes, and the flow sizing of them by using attenuation curves of the echoes when they propagate along the pipeline. This approach leads to employ only one transmitter and one receptor for notch detection, location and diagnosis. The main advantage for the industry is the double check of presence of a notch with respect to other systems, which reduces false alarms during the inspections.Spanish Ministerio de Economía y Competitividad, under Research Grant DPI2015-67264-P1.119 JCR (2016) Q3, 19/31 AcousticsUE

ACS Style

Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez; Benjamin Lev; Alfredo Arcos. New Pipe Notch Detection and Location Method for Short Distances employing Ultrasonic Guided Waves. Acta Acustica united with Acustica 2017, 103, 772 -781.

AMA Style

Carlos Quiterio Gómez Muñoz, Fausto Pedro García Márquez, Benjamin Lev, Alfredo Arcos. New Pipe Notch Detection and Location Method for Short Distances employing Ultrasonic Guided Waves. Acta Acustica united with Acustica. 2017; 103 (5):772-781.

Chicago/Turabian Style

Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez; Benjamin Lev; Alfredo Arcos. 2017. "New Pipe Notch Detection and Location Method for Short Distances employing Ultrasonic Guided Waves." Acta Acustica united with Acustica 103, no. 5: 772-781.

Conference paper
Published: 29 June 2017 in Proceedings of the Eleventh International Conference on Management Science and Engineering Management
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The paper presents a novel approach that allows to optimize the ultrasonic wave sensors for a condition monitoring system employing. It can detect and diagnosis different faults with a signal, such as delamination, mud or ice on blades of wind turbines. This methodology allows to avoid the redundancy of sensors, since a specific number of ultrasonic transducers can determine the structural condition using guided waves. The signal is pre-processed with the aim of removing the noise, then extracted and selected features to be later classified by Machine Learning and Neural Networks. Finally, for each damage or anomaly, the best classifier will be evaluated. The best classifier of each damage will act on a parallel network that will process the signal sent by the sensor.

ACS Style

Alfredo Arcos Jiménez; Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez. Machine Learning and Neural Network for Maintenance Management. Proceedings of the Eleventh International Conference on Management Science and Engineering Management 2017, 1377 -1388.

AMA Style

Alfredo Arcos Jiménez, Carlos Quiterio Gómez Muñoz, Fausto Pedro García Márquez. Machine Learning and Neural Network for Maintenance Management. Proceedings of the Eleventh International Conference on Management Science and Engineering Management. 2017; ():1377-1388.

Chicago/Turabian Style

Alfredo Arcos Jiménez; Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez. 2017. "Machine Learning and Neural Network for Maintenance Management." Proceedings of the Eleventh International Conference on Management Science and Engineering Management , no. : 1377-1388.

Conference paper
Published: 29 June 2017 in Proceedings of the Eleventh International Conference on Management Science and Engineering Management
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A novel Non-Destructive Test (NDT) is presented in this paper. It employs a radiometric sensor that measures the infrared emissivity of the solar panel surface embedded in an unmanned aerial vehicle. The measurements provided by the sensor will determine if the panel is healthy, damaged or dirty. A thermographic camera has been used to check the temperature variations and validate the results by the sensor. The study shows that the amount of dirt influences the temperature on the surface and the energy generated. Similarly, faults in photovoltaic cells influence the temperature of the panel. The NDT system is less expensive than traditional thermographic sensors or cameras. Early detection of these problems, together with an optimal maintenance strategy, allows to reduce costs and increase the competitiveness of this renewable energy source.

ACS Style

Carlos Quiterio Gómez Muñoz; Alfredo Peinado Gonzalo; Isaac Segovia Ramirez; Fausto Pedro García Márquez. Online Fault Detection in Solar Plants Using a Wireless Radiometer in Unmanned Aerial Vehicles. Proceedings of the Eleventh International Conference on Management Science and Engineering Management 2017, 1161 -1174.

AMA Style

Carlos Quiterio Gómez Muñoz, Alfredo Peinado Gonzalo, Isaac Segovia Ramirez, Fausto Pedro García Márquez. Online Fault Detection in Solar Plants Using a Wireless Radiometer in Unmanned Aerial Vehicles. Proceedings of the Eleventh International Conference on Management Science and Engineering Management. 2017; ():1161-1174.

Chicago/Turabian Style

Carlos Quiterio Gómez Muñoz; Alfredo Peinado Gonzalo; Isaac Segovia Ramirez; Fausto Pedro García Márquez. 2017. "Online Fault Detection in Solar Plants Using a Wireless Radiometer in Unmanned Aerial Vehicles." Proceedings of the Eleventh International Conference on Management Science and Engineering Management , no. : 1161-1174.

Journal article
Published: 01 November 2016 in Measurement
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Wind farms are located in areas with a high probability of ice occurrence. Icing involves problems such as energy losses, mechanical failures and downtimes. The priority is to detect icing in order to avoid these problems. Icing detection is a complex procedure For example, low temperatures are not a guaranty of ice formation, and other variables may affect. Different techniques have been recently proposed to detect ice in wind turbine blades. They are mainly based on damping of ultrasonic waves on the blade surface, or measuring the resonant frequency of a probe. But these methods have some drawbacks that may cause the system to fail, e.g. the behaviour of ultrasonic waves in composite materials is difficult to predict due to different fiber orientations, and the ice detection by changes in the resonance frequency could lead to false alarms due to variations in working conditions. This paper takes advantage of the remote sensing techniques to propose a novel approach for icing detection without physical contact. The approach is based on the drastic emissivity change that it is produced over a surface characterized with a low emissivity value when ice appears. An experiment was conducted using a broad-band thermal radiometer and a section of a wind turbine blade. Radiometric temperature measurements were collected over the blade with and without an aluminium foil patch. The piece of blade was cooled down and different scenarios were considered, including frozen with and without ice. This study was completed with a sensitivity analysis of the approach to dust accumulation, accounting for real operation conditions. Results show the feasibility of this technique to detect ice formation and discern between frozen and icing conditions.

ACS Style

Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez; Juan Manuel Sánchez Tomás. Ice detection using thermal infrared radiometry on wind turbine blades. Measurement 2016, 93, 157 -163.

AMA Style

Carlos Quiterio Gómez Muñoz, Fausto Pedro García Márquez, Juan Manuel Sánchez Tomás. Ice detection using thermal infrared radiometry on wind turbine blades. Measurement. 2016; 93 ():157-163.

Chicago/Turabian Style

Carlos Quiterio Gómez Muñoz; Fausto Pedro García Márquez; Juan Manuel Sánchez Tomás. 2016. "Ice detection using thermal infrared radiometry on wind turbine blades." Measurement 93, no. : 157-163.