CERES :: Browsing by Author "Avdelidis, Nicolas Peter"

Browsing by Author "Avdelidis, Nicolas Peter"

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Open Access
Aircraft skin inspections: towards a new model for dent evaluation
(British Institute of Non-destructive Testing, 2023-07-01) Lafiosca, Pasquale; Fan, Ip-Shing; Avdelidis, Nicolas Peter
The aircraft maintenance, repair and overhaul (MRO) industry is gradually switching to 3D scanning for dent inspection. High-accuracy devices allow for quick and repeatable measurements, which translate into efficient reporting and more objective damage evaluations. However, the potential of 3D scanners is far from being exploited. This is due to the traditional way in which the structural repair manual (SRM) deals with dents, that is, considering length, width and depth as the only relevant measures. Being equivalent to describing a dent similarly to a 'box', the current approach discards any information about the actual shape. This leads to a high degree of ambiguity, with very different shapes (and corresponding fatigue life) being classified as the same, and nullifies the effort of acquiring such a great amount of information from high-accuracy 3D scanners. In this paper, a seven-parameter model is proposed to describe the actual dent shape, thus enabling the exploitation of the high-fidelity data produced by 3D scanners. The compact set of values can then be compared against historical data and structural evaluations based on the same model. The proposed approach has been evaluated in both simulations and point cloud data generated by 8tree's dentCHECK tool, suggesting an increased capability in the evaluation of damage, enabling more targeted interventions and, ultimately, saving costs.
Open Access
Application of NDT thermographic imaging of aerospace structures
(Elsevier, 2019-02-13) Deane, Shakeb; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Zhang, Hai; Yazdani Nezhad, Hamed; Williamson, Alex A.; Mackley, Tim; Davis, Maxwell J.; Maldague, Xavier P. V.; Tsourdos, Antonios
This work aims to address the effectiveness and challenges of Non-Destructive Testing (NDT) inspection and improve the detection of defects without causing damage to the material or operator. It focuses on two types of NDT methods; pulsed thermography and vibrothermography. The paper also explores the possibility of performing automated aerial inspection using an unmanned aerial vehicle (UAV) provided with a thermographic imaging system. The concept of active thermography is discussed for inspecting aircraft CFRP panels along with the proposal for performing aerial inspection using the UAV for real time inspection. Static NDT results and the further UAV research indicate that the UAV inspection approach could significantly reduce the inspection time, cost, and workload, whilst potentially increasing the probability of detection.
Open Access
Automated aircraft dent inspection via a modified Fourier transform profilometry algorithm
(MDPI, 2022-01-07) Lafiosca, Pasquale; Fan, Ip-Shing; Avdelidis, Nicolas Peter
The search for dents is a consistent part of the aircraft inspection workload. The engineer is required to find, measure, and report each dent over the aircraft skin. This process is not only hazardous, but also extremely subject to human factors and environmental conditions. This study discusses the feasibility of automated dent scanning via a single-shot triangular stereo Fourier transform algorithm, designed to be compatible with the use of an unmanned aerial vehicle. The original algorithm is modified introducing two main contributions. First, the automatic estimation of the pass-band filter removes the user interaction in the phase filtering process. Secondly, the employment of a virtual reference plane reduces unwrapping errors, leading to improved accuracy independently of the chosen unwrapping algorithm. Static experiments reached a mean absolute error of ∼0.1 mm at a distance of 60 cm, while dynamic experiments showed ∼0.3 mm at a distance of 120 cm. On average, the mean absolute error decreased by ∼34%, proving the validity of the proposed single-shot 3D reconstruction algorithm and suggesting its applicability for future automated dent inspections.
Open Access
Automated impact damage detection technique for composites based on thermographic image processing and machine learning classification
(MDPI, 2022-11-22) Alhammad, Muflih; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Torbali, Muhammet E.; Genest, Marc; Zhang, Hai; Zolotas, Argyrios; Maldgue, Xavier P. V.
Composite materials are one of the primary structural components in most current transportation applications, such as the aerospace industry. Composite material diagnostics is a promising area in the fight against structural damage in aircraft and spaceships. Detection and diagnostic technologies often provide analysts with a valuable and rapid mechanism to monitor the health and safety of composite materials. Although many attempts have been made to develop damage detection techniques and make operations more efficient, there is still a need to develop/improve existing methods. Pulsed thermography (PT) technology was used in this study to obtain healthy and defective data sets from custom-designed composite samples having similar dimensions but different thicknesses (1.6 and 3.8). Ten carbon fibre-reinforced plastic (CFRP) panels were tested. The samples were subjected to impact damage of various energy levels, ranging from 4 to 12 J. Two different methods have been applied to detect and classify the damage to the composite structures. The first applied method is the statistical analysis, where seven different statistical criteria have been calculated. The final results have proved the possibility of detecting the damaged area in most cases. However, for a more accurate detection technique, a machine learning method was applied to thermal images; specifically, the Cube Support Vector Machine (SVM) algorithm was selected. The prediction accuracy of the proposed classification models was calculated within a confusion matrix based on the dataset patterns representing the healthy and defective areas. The classification results ranged from 78.7% to 93.5%, and these promising results are paving the way to develop an automated model to efficiently evaluate the damage to composite materials based on the non-distractive testing (NDT) technique.
Open Access
Autonomous systems imaging of aerospace structures
(Unknown, 2018-12-31) Deane, Shakeb; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Zhang, Hai; Yazdani Nezhad, Hamed; Williamson, Alex A.; Maldague, Xavier P. V.; Tsourdos, Antonios
Aircraft manufacturers are constantly improving their aircraft ensuring they are more cost-efficient to do this the weight of the aircraft needs to be reduced, which results in less fuel required to power the aircraft. This has led to an increased use of composite materials within an aircraft. Carbon fibre reinforced polymer (CFRP) composite is used in industries where high strength and rigidity are required in relation to weight. e.g. in aviation – transport. The fibre-reinforced matrix systems are extremely strong (i.e. have excellent mechanical properties and high resistance to corrosion). However, because of the nature of the CFRP, it does not dint or bend, as aluminium would do when damaged, it makes it difficult to locate structural damage, especially subsurface. Non Destructive Testing (NDT) is a wide group of analysis techniques used to evaluate the properties of a material, component or system without causing damage to the operator or material. Active Thermography is one of the NDT risk-free methods used successfully in the evaluation of composite materials. This approach has the ability to provide both qualitative and quantitative information about hidden defects or features in a composite structure. Aircraft has to undergo routine maintenance – inspection to check for any critical damage and thus to ensure its safety. This work aims to address the challenge of NDT automated inspection and improve the defects’ detection by performing automated aerial inspection using a Unmanned Aerial Vehicle (UAV) thermographic imaging system. The concept of active thermography is discussed and presented in the inspection of aircraft’s CFRP panels along with the mission planning for aerial inspection using the UAV for real time inspection. Results indicate that this inspection approach could significantly reduce the inspection time, cost, and workload, whilst potentially increasing the probability of detection.
Open Access
Autonomous systems thermographic NDT of composite structures
(SPIE, 2019-05-02) Deane, Shakeb; Avdelidis, Nicolas Peter; Yazdani Nezhad, Hamed; Williamson, A.; Zhang, H.; Tzitzilonis, Vasileios; Maldague, Xavier P. V.; Tsourdos, Antonios
Transient thermography is a method used successfully in the evaluation of composite materials and aerospace structures. It has the capacity to deliver both qualitative and quantitative results about hidden defects or features in a composite structure. Aircraft must undergo routine maintenance – inspection to check for any critical damage and thus to ensure its safety. This work aims to address the challenge of NDT automated inspection and improve the defects’ detection by suggesting an autonomous thermographic imaging approach using a UAV (Unmanned Aerial Vehicle) active thermographic system. The concept of active thermography is discussed and presented in the inspection of aircraft CFRP panels along with the mission planning for aerial inspection using the UAV for real time inspection. Results indicate that the suggested approach could significantly reduce the inspection time, cost, and workload, whilst potentially increase the probability of detection of defects on aircraft composites.
Open Access
CNN-fusion architecture with visual and thermographic images for object detection
(SPIE, 2023-06-12) Adiuku, Amaka; Avdelidis, Nicolas Peter; Tang, Gilbert; Plastropoulos, Angelos; Perinpanayagam, Suresh
Mobile robots performing aircraft visual inspection play a vital role in the future automated aircraft maintenance, repair and overhaul (MRO) operations. Autonomous navigation requires understanding the surroundings to automate and enhance the visual inspection process. The current state of neural network (NN) based obstacle detection and collision avoidance techniques are suitable for well-structured objects. However, their ability to distinguish between solid obstacles and low-density moving objects is limited, and their performance degrades in low-light scenarios. Thermal images can be used to complement the low-light visual image limitations in many applications, including inspections. This work proposes a Convolutional Neural Network (CNN) fusion architecture that enables the adaptive fusion of visual and thermographic images. The aim is to enhance autonomous robotic systems’ perception and collision avoidance in dynamic environments. The model has been tested with RGB and thermographic images acquired in Cranfield’s University hangar, which hosts a Boeing 737-400 and TUI hangar. The experimental results prove that the fusion-based CNN framework increases object detection accuracy compared to conventional models.
Open Access
Comparison of cooled and uncooled IR sensors by means of signal-to-noise ratio for NDT diagnostics of aerospace grade composites
(MDPI, 2020-06-15) Shakeb, Deane; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Zhang, Hai; Nezhad, Hamed Yazdani; Williamson, Alex A.; Mackley, Tim; Maldague, Xavier P. V.; Tsourdos, Antonios; Nooralishahi, Parham
This work aims to address the effectiveness and challenges of non-destructive testing (NDT) by active infrared thermography (IRT) for the inspection of aerospace-grade composite samples and seeks to compare uncooled and cooled thermal cameras using the signal-to-noise ratio (SNR) as a performance parameter. It focuses on locating impact damages and optimising the results using several signal processing techniques. The work successfully compares both types of cameras using seven different SNR definitions, to understand if a lower-resolution uncooled IR camera can achieve an acceptable NDT standard. Due to most uncooled cameras being small, lightweight, and cheap, they are more accessible to use on an unmanned aerial vehicle (UAV). The concept of using a UAV for NDT on a composite wing is explored, and the UAV is also tracked using a localisation system to observe the exact movement in millimetres and how it affects the thermal data. It was observed that an NDT UAV can access difficult areas and, therefore, can be suggested for significant reduction of time and cost.
Open Access
Defects recognition algorithm development from visual UAV inspections
(MDPI, 2022-06-21) Avdelidis, Nicolas Peter; Tsourdos, Antonios; Lafiosca, Pasquale; Plaster, Richard; Plaster, Anna; Droznika, Mark
Aircraft maintenance plays a key role in the safety of air transport. One of its most significant procedures is the visual inspection of the aircraft skin for defects. This is mainly carried out manually and involves a high skilled human walking around the aircraft. It is very time consuming, costly, stressful and the outcome heavily depends on the skills of the inspector. In this paper, we propose a two-step process for automating the defect recognition and classification from visual images. The visual inspection can be carried out with the use of an unmanned aerial vehicle (UAV) carrying an image sensor to fully automate the procedure and eliminate any human error. With our proposed method in the first step, we perform the crucial part of recognizing the defect. If a defect is found, the image is fed to an ensemble of classifiers for identifying the type. The classifiers are a combination of different pretrained convolution neural network (CNN) models, which we retrained to fit our problem. For achieving our goal, we created our own dataset with defect images captured from aircrafts during inspection in TUI’s maintenance hangar. The images were preprocessed and used to train different pretrained CNNs with the use of transfer learning. We performed an initial training of 40 different CNN architectures to choose the ones that best fitted our dataset. Then, we chose the best four for fine tuning and further testing. For the first step of defect recognition, the DenseNet201 CNN architecture performed better, with an overall accuracy of 81.82%. For the second step for the defect classification, an ensemble of different CNN models was used. The results show that even with a very small dataset, we can reach an accuracy of around 82% in the defect recognition and even 100% for the classification of the categories of missing or damaged exterior paint and primer and dents.
Open Access
Development of a thermal excitation source used in an active thermographic UAV platform
(Taylor & Francis, 2022-06-03) Deane, Shakeb; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Williamson, Alex A.; Withers, Stephen; Zolotas, Argyrios; Maldague, Xavier P. V.; Ahmadi, Mohammad; Pant, Shashank; Genest, Marc; Rabearivelo, Hobivola A.; Tsourdos, Antonios
This work aims to address the effectiveness and challenges of using active infrared thermography (IRT) onboard an unmanned aerial vehicle (UAV) platform. The work seeks to assess the performance of small low-powered forms of excitation which are suitable for active thermography and the ability to locate subsurface defects on composites. An excitation source in multiple 250 W lamps is mounted onto a UAV and is solely battery powered with a remote trigger to power cycle them. Multiple experiments address the interference from the UAV whilst performing an active IRT inspection. The optimal distances and time required for a UAV inspection using IRT are calculated. Multiple signal processing techniques are used to analyse the composites which help locate the sub-surface defects. It was observed that a UAV can successfully carry the required sensors and equipment for an Active thermographic NDT inspection which can provide access to difficult areas. Most active thermographic inspection equipment is large, heavy, and expensive. Furthermore, using such equipment for the inspection of complex structures is time-consuming. For example, a cherry picker would be required to inspect the tail of an aircraft. This solution looks to assist engineers in inspecting complex composite structures and could potentially significantly reduce the time and cost of a routine inspection.
Open Access
Diagnosis of composite materials in aircraft applications: towards a UAV active thermography inspection approach
(Society of Photo-Optical Instrumentation Engineers (SPIE), 2021-04-12) Alhammad, Muflih; Avdelidis, Nicolas Peter; Deane, Shakeb; Ibarra-Castanedo, Clemente; Pant, Shashank; Nooralishahi, Parham; Ahmadi, Mohammad; Genest, Marc; Zolotas, Argyrios; Zanotti Fragonara, Luca; Valdes, Julio J.; Maldague, Xavier P. V.
Diagnosis and prognosis of failures for aircrafts’ integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. This paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of active thermography techniques in the aerospace industry. Furthermore, as the use of unmanned aerial vehicles (UAVs) for the remote inspection of large and/or difficult access areas has significantly grown in the last few years thanks to their flexibility of flight and to the possibility to carry one or several measuring sensors, the aim to use a UAV active thermography system for the inspection of large composite aeronautical structures in a continuous dynamic mode is proposed.
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Digital twin analysis to promote safety and security in autonomous vehicles
(IEEE, 2021-03-31) Almeaibed, Sadeq; Al-Rubaye, Saba; Tsourdos, Antonios; Avdelidis, Nicolas Peter
With the new industrial revolution of digital transformation, more intelligence and autonomous systems can be adopted in the manufacturing transportation processes. Safety and security of autonomous vehicles (AVs) have obvious advantages of reducing accidents and maintaining a cautious environment for drivers and pedestrians. Therefore, the transformation to data-driven vehicles is associated with the concept of digital twin, especially within the context of AV design. This also raises the need to adopt new safety designs to increase the resiliency and security of the whole AV system. To enable secure autonomous systems for smart manufacturing transportation in an end-to-end fashion, this article presents the main challenges and solutions considering safety and security functions. This article aims to identify a standard framework for vehicular digital twins that facilitate the data collection, data processing, and analytics phases. To demonstrate the effectiveness of the proposed approach, a case study for a vehicle follower model is analyzed when radar sensor measurements are manipulated in an attempt to cause a collision. Perceptive findings of this article can pave the way for future research aspects related to employing digital twins in the AV industry.
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Drone-based non-destructive inspection of industrial sites: a review and case studies
(MDPI, 2021-09-29) Nooralishahi, Parham; Ibarra-Castanedo, Clemente; Deane, Shakeb; López, Fernando; Pant, Shashank; Genest, Marc; Avdelidis, Nicolas Peter; Maldague, Xavier P. V.
Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented.
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Enhanced infrared image processing for impacted carbon/glass fiber-reinforced composite evaluation
(MDPI, 2017-12-26) Zhang, Hai; Avdelidis, Nicolas Peter; Osman, Ahmad; Ibarra-Castanedo, Clemente; Sfarra, Stefano; Fernandes, Henrique; Matikas, Theodore E.; Maldague, Xavier P. V.
In this paper, an infrared pre-processing modality is presented. Different from a signal smoothing modality which only uses a polynomial fitting as the pre-processing method, the presented modality instead takes into account the low-order derivatives to pre-process the raw thermal data prior to applying the advanced post-processing techniques such as principal component thermography and pulsed phase thermography. Different cases were studied involving several defects in CFRPs and GFRPs for pulsed thermography and vibrothermography. Ultrasonic testing and signal-to-noise ratio analysis are used for the validation of the thermographic results. Finally, a verification that the presented modality can enhance the thermal image performance effectively is provided.
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Evaluation and selection of video stabilization techniques for UAV-based active infrared thermography application
(MDPI, 2021-02-25) Pant, Shashank; Nooralishahi, Parham; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Genest, Marc; Deane, Shakeb; Valdes, Julio J.; Zolotas, Argyrios; Maldague, Xavier P. V.
nmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.
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Fusion insights from ultrasonic and thermographic inspections for impact damage analysis
(AIAA, 2023-06-08) Torbali, M. Ebubekir; Alhammad, Muflih; Zolotas, Argyrios; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Maldague, Xavier
Low energy impact damage in composite materials may be more concerning than it appears visually, often requiring a detailed examination for accurate assessment to ensure safe and sustainable operation. Non-destructive testing (NDT) methods provide such inspection techniques, and in this paper, NDT-based fusion is explored for enhanced identification of defect size and location compared to indepdently using individual NDT methods separately. Three Carbon Fiber Reinforced Polymer (CFRP) specimens are examined, each with an impact damage of a given energy level, using pulsed thermography (PT) and phased array (PA) ultrasonic methods. Following the extraction of binary defect shapes from source images, a decision-level fusion approach is performed. The results indicate that combining ultrasonic and infrared thermography (IRT) inspections for CFRP composite materials is promising to achieve enhanced and improved detection traceability.
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Impact modelling and a posteriori non-destructive evaluation of homogeneous particleboards of sugarcane bagasse
(2018-01-12) Zhang, Hai; Sfarra, Stefano; Sarasini, Fabrizio; Fiorelli, Juliano; Peeters, Jeroen; Avdelidis, Nicolas Peter; de Lucca Sartori, Diogo; Ibarra-Castanedo, Clemente; Perilli, Stefano; Mokhtari, Yacine; Tirillò, Jacopo; Maldague, Xavier P. V.
With a view to gaining an in-depth assessment of the response of particleboards (PBs) to different in-service loading conditions, samples of high-density homogeneous PBs of sugarcane bagasse and castor oil polyurethane resin were manufactured and subjected to low velocity impacts using an instrumented drop weight impact tower and four different energy levels, namely 5, 10, 20 and 30 J. The prediction of the damage modes was assessed using Comsol Multiphysics ® . ®. In particular, the random distribution of the fibres and their lengths were reproduced through a robust model. The experimentally obtained dent depths due to the impactor were compared with the ones numerically simulated showing good agreement. The post-impact damage was evaluated by a simultaneous system of image acquisitions coming from two different sensors. In particular, thermograms were recorded during the heating up and cooling down phases, while the specklegrams were gathered one at room temperature (as reference) and the remaining during the cooling down phase. On one hand, the specklegrams were processed via a new software package named Ncorr v.1.2, which is an open-source subset-based 2D digital image correlation (DIC) package that combines modern DIC algorithms proposed in the literature with additional enhancements. On the other hand, the thermographic results linked to a square pulse were compared with those coming from the laser line thermography technique that heats a line-region on the surface of the sample instead of a spot. Surprisingly, both the vibrothermography and the line scanning thermography methods coupled with a robotized system show substantial advantages in the defect detection around the impacted zone.
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Inspection of aircraft wing panels using unmanned aerial vehicles
(MDPI, 2019-04-17) Tzitzilonis, Vasileios; Malandrakis, Konstantinos; Zanotti Fragonara, Luca; Gonzalez Domingo, Jose Angel; Avdelidis, Nicolas Peter; Tsourdos, Antonios; Forster, Kevin
In large civil aircraft manufacturing, a time-consuming post-production process is the non-destructive inspection of wing panels. This work aims to address this challenge and improve the defects’ detection by performing automated aerial inspection using a small off-the-shelf multirotor. The UAV is equipped with a wide field-of-view camera and an ultraviolet torch for implementing non-invasive imaging inspection. In particular, the UAV is programmed to perform the complete mission and stream video, in real-time, to the ground control station where the defects’ detection algorithm is executed. The proposed platform was mathematically modelled in MATLAB/SIMULINK in order to assess the behaviour of the system using a path following method during the aircraft wing inspection. In addition, two defect detection algorithms were implemented and tested on a dataset containing images obtained during inspection at Airbus facilities. The results show that for the current dataset the proposed methods can identify all the images containing defects.
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Inspection of aircraft wing panels using unmanned aerial vehicles
(IEEE, 2018-09-03) Malandrakis, Konstantinos; Savvaris, Al; Gonzalez Domingo, Jose Angel; Avdelidis, Nicolas Peter; Tsilivis, Panagiotis; Plumacker, Florence; Zanotti Fragonara, Luca; Tsourdos, Antonios
In large civil aircraft manufacturing a time-consuming post-production process is the non-destructive inspection of wing panels. This work aims to address this challenge and improve the defects' detection by performing automated aerial inspection using a small off-the-shelf multirotor. The UAV is equipped with a wide field-of-view camera and an ultraviolet torch for implementing non-invasive imaging inspection. In particular, the UAV is programmed to perform the complete mission and stream video, in real-time, to the ground control station where the defects' detection algorithm is executed. The proposed platform was mathematically modelled in MATLAB/SIMULINK in order to assess the behaviour of the system using a path following method during the aircraft wing inspection. The UAV was tested in the lab where a six-meter-long wing panel was one-side inspected. Initial results indicate that this inspection method could reduce significantly the inspection time, cost, and workload, whilst potentially increasing the probability of detection.
Unknown
Multi-label classification algorithms for composite materials under infrared thermography testing
(Taylor and Francis, 2022-10-14) Alhammad, Muflih; Avdelidis, Nicolas Peter; Ibarra Castanedo, Clemente; Maldague, Xavier; Zolotas, Argyrios; Torbali, M. Ebubekir; Genestc, Marc
The key idea in this paper is to propose multi-labels classification algorithms to handle benchmark thermal datasets that are practically associated with different data characteristics and have only one health condition (damaged composite materials). A suggested alternative approach for extracting the statistical contents from the thermal images, is also employed. This approach offers comparable advantages for classifying multi-labelled datasets over more complex methods. Overall scored accuracy of different methods utilised in this approach showed that Random Forest algorithm has a clear higher performance over the others. This investigation is very unique as there has been no similar work published so far. Finally, the results demonstrated in this work provide a new perspective on the inspection of composite materials using Infrared Pulsed Thermography.