Cracks and welds detection approach in solar receiver tubes employing electromagnetic acoustic transducers

Muñoz, Carlos Quiterio Gómez; Jiménez, Alfredo; Márquez, Fausto Pedro Garcı́a; Kogia, Maria; Cheng, Lei; Mohimi, Abbas; Papaelias, Mayorkinos

doi:10.1177/1475921717734501

Cited by 46 publications

(13 citation statements)

References 34 publications

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“…These systems allow useful information about the condition of the WTs to be extracted. This information can be used to check the status of equipment, identify potential problems before failures occur, minimize downtimes, schedule relevant repairs and maximize the efficiency of the conversion processes [15][16][17]. Since the number and the complexity of the components are growing, it is crucial to employ new technologies in the development of maintenance management strategies [6,18].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Maintenance Management Employing Unmanned Aerial Vehicles in Renewable Energies

Moraleda

Marugán

Márquez

2018

Lecture Notes on Multidisciplinary Industrial Engineering

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Nowadays, wind energy is one of the most important renewable energy sources for covering the current electrical demand. The sophistication and the complexity of the wind energy systems are constantly growing. Therefore, new maintenance management techniques are required for ensure the efficiency and rentability of these systems. This paper is focused on the extraction of information from the noise generated by wind turbines. The main causes of noise generation by wind turbines are analyzed in this paper. The noise information allows some types of failures to be detected incipiently and, consequently, preventive and corrective maintenance tasks can be improved. In this field, the use of unmanned aerial vehicles (UAV) can be very useful and facilitate the execution of the inspections. This paper presents a collection of methods for detecting failures through the processing of wind turbine noise. In addition, the possibility of employing UAVs for implement those methods is studied.

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Section: Introductionmentioning

confidence: 99%

Acoustic Maintenance Management Employing Unmanned Aerial Vehicles in Renewable Energies

Moraleda

Marugán

Márquez

2018

Lecture Notes on Multidisciplinary Industrial Engineering

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“…There are several techniques used for ice detection [23]. Ultrasonic Guided Waves (UGW) are one of the most efficient and reliable techniques since they can detect discontinuities in large areas [24,25], including defects on a surface [26,27], e.g. cracks.…”

Section: Ncamentioning

confidence: 99%

Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

et al. 2019

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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.

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“…New systems are being installed in wind turbines (WTs), eg, condition monitoring systems (CMSs) or supervisory control and data acquisition (SCADA) . The efficiency of these systems has been proved in several research studies, eg, references .…”

Section: Introductionmentioning

confidence: 99%

Advanced analytics for detection and diagnosis of false alarms and faults: A real case study

Marugán

Márquez

2019

Wind Energy

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Onshore and offshore wind farms require a high level of advanced maintenance. Supervisory control and data acquisition (SCADA) and condition monitoring systems are now being employed, generating large amounts of data. They require robust and flexible approaches to convert dataset into useful information. This paper presents a novel approach based on the correlations of SCADA variables to detect and identify faults and false alarms in wind turbines. A correlation matrix between all the SCADA variables is used for pattern recognition. A new method based on curve fittings is employed for detecting false alarms and abnormal behaviours or faults in the components. The study is done in a real case study, validated with false alarms.

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Cracks and welds detection approach in solar receiver tubes employing electromagnetic acoustic transducers

Cited by 46 publications

References 34 publications

Acoustic Maintenance Management Employing Unmanned Aerial Vehicles in Renewable Energies

Acoustic Maintenance Management Employing Unmanned Aerial Vehicles in Renewable Energies

Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

Advanced analytics for detection and diagnosis of false alarms and faults: A real case study

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