Progress and trends in fault diagnosis for renewable and sustainable energy system based on infrared thermography: A review

Du, Bolun; He, Yigang; He, Yunze; Zhang, Chaolong

doi:10.1016/j.infrared.2020.103383

Cited by 43 publications

(23 citation statements)

References 162 publications

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“…At present, IRT is a mature technology, which can be used to monitor thermal conditions in real time and without contact. Thermography allows for the detection of equipment failures during operation, thus providing a significant reduction in downtime and associated maintenance costs [37]. Its use has been extended to different applications, including the monitoring of civil structures, electrical installations, machinery, and equipment; as well as in different industries, such as nuclear, aerospace, or food [38].…”

Section: Model-basedmentioning

confidence: 99%

MANTRA: An Effective System Based on Augmented Reality and Infrared Thermography for Industrial Maintenance

et al. 2021

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In recent years, the benefits of both Augmented Reality (AR) technology and infrared thermography (IRT) have been demonstrated in the industrial maintenance sector, allowing maintenance operations to be carried out in a safer, faster, and more efficient manner. However, there still exists no solution that optimally combines both technologies. In this work, we propose a new AR system—MANTRA—with specific application to industrial maintenance. The system can automatically align virtual information and temperature on any 3D object, in real time. This is achieved through the joint use of an RGB-D sensor and an IRT camera, leading to high accuracy and robustness. To achieve this objective, a pose estimation method that combines a deep-learning-based object detection method, YOLOV4, together with the template-based LINEMOD pose estimation method, as well as a model-based 6DOF pose tracking technique, was developed. The MANTRA system is validated both quantitatively and qualitatively through a real use-case, demonstrating the effectiveness of the system compared to traditional methods and those using only AR.

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Section: Model-basedmentioning

confidence: 99%

MANTRA: An Effective System Based on Augmented Reality and Infrared Thermography for Industrial Maintenance

et al. 2021

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“…This indicates that the damaged VAWT experiences significantly higher wind loads, which can have a considerable impact on its structural health. In published literature [8][9][10], researchers have extensively used digital signal processing techniques to find out the dominant frequencies in the torque signals and have attempted to link them to the damage the VAWT has experienced.…”

Section: Torque Output Comparison Between Healthy and Damaged Vawtsmentioning

confidence: 99%

“…Fault detection and diagnostics of wind turbines research has received a lot of attention in recent years. Life cycle assessment, fault diagnosis, and Nondestructive Testing (NDT) techniques have been widely used to detect blade faults and monitor the health of wind turbines [8]. A fault in the blade, such as crack or erosion, can lead to catastrophic failure and unexpected breakdown of the wind turbine system.…”

Section: Introductionmentioning

confidence: 99%

Effects of Damaged Rotor on Wake Dynamics of Vertical Axis Wind Turbines

Asim

Islam

2021

Energies

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Vertical Axis Wind Turbines (VAWTs) are omnidirectional turbomachines commonly used in rural areas for small-to-medium-scale power generation. The complex flow observed in the wake region of VAWTs is affected by a number of factors, such as rotor blades design. A damaged rotor significantly alters the flow field in the wake region of the VAWT, degrading its power generation capability. Published literature on damaged wind turbine blades is severely limited to torque signal analysis and basic flow field description in the wake region. In this study, detailed numerical investigations have been carried out to establish and quantify the relationship between damaged rotor and the wake dynamics of a VAWT. Time-based Computational Fluid Dynamics analyses have been performed on two VAWT models, one undamaged and the other with a missing rotor blade. Proper Orthogonal Decomposition has been used to extract the energy content and temporal coefficients of the various flow patterns associated with the wake region. The results indicate that the first pressure-based flow mode contains 99% of the energy and provides a functional basis for accurate reconstruction of the wake. It is envisaged that this study will aid the development of novel machine learning algorithms for rotor damage detection in wind farms.

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“…Electrical methods are mostly based on I-V characteristics analysis [2], power losses analysis (PLA) [3], statistical and signal processing approaches (SSPA) [4], etc. On the other hand, the nonelectrical techniques are based on infrared thermography, which has known increasing use to con-trol the renewable energy system over recent years [5][6][7]. These techniques are based on the use of thermal cameras and drones in order to detect the hottest areas in the PV field, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image

Et-taleby

Boussetta

Benslimane

2020

International Journal of Photoenergy

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Clustering or grouping is among the most important image processing methods that aim to split an image into different groups. Examining the literature, many clustering algorithms have been carried out, where the K-means algorithm is considered among the simplest and most used to classify an image into many regions. In this context, the main objective of this work is to detect and locate precisely the damaged area in photovoltaic (PV) fields based on the clustering of a thermal image through the K-means algorithm. The clustering quality depends on the number of clusters chosen; hence, the elbow, the average silhouette, and NbClust R package methods are used to find the optimal number K. The simulations carried out show that the use of the K-means algorithm allows detecting precisely the faults in PV panels. The excellent result is given with three clusters that is suggested by the elbow method.

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Progress and trends in fault diagnosis for renewable and sustainable energy system based on infrared thermography: A review

Cited by 43 publications

References 162 publications

MANTRA: An Effective System Based on Augmented Reality and Infrared Thermography for Industrial Maintenance

MANTRA: An Effective System Based on Augmented Reality and Infrared Thermography for Industrial Maintenance

Effects of Damaged Rotor on Wake Dynamics of Vertical Axis Wind Turbines

Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image

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