Browsing by Author "Avdelidis, Nicolas P."

Now showing 1 - 11 of 11
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    A review of simulation modelling approaches in aviation spare parts inventory optimisation
    (Cranfield University, 2024-06-07) bin Mohammud, Zaki; Fan, Ip-Shing; Avdelidis, Nicolas P.
    Aviation spare parts are expensive and are being kept as a buffer for unscheduled and scheduled maintenance activities. Apart from cash flow being locked in the inventory, spare parts for aircraft or helicopters are also critical in the continuous operations of air assets. In addition, the holding cost is roughly 20 per cent of the total inventory value. Holding costs are costs such as insurance, utilities and manpower. Minimising the total inventory value could be done by adopting a lower inventory count through various methods, such as the provision of spare parts, which can be done either by forecasting the failure of components or by using new maintenance methodologies, such as predictive maintenance. The methods have been used widely in the aviation industry for a long time. The upward trend of papers published from 1963 to 2023 shows that aviation spare parts optimisation is still being discussed. This paper reviews the simulation modelling approaches to optimise aviation spare inventory. 221 papers were reviewed from Scopus and Web of Science (WoS) literature databases, and 17 papers from 1982 to 2023 were chosen based on the simulation modelling approach, such as System Dynamics and Discrete-Event Simulation. The papers were classified according to simulation modelling techniques, spare parts and operations classification, and challenges and opportunities.
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    Advancements in learning-based navigation systems for robotic applications in MRO hangar: review
    (MDPI, 2024-02-21) Adiuku, Ndidiamaka; Avdelidis, Nicolas P.; Tang, Gilbert; Plastropoulos, Angelos
    The field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain.
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    Aircraft skin machine learning-based defect detection and size estimation in visual inspections
    (MDPI , 2024-09-10) Plastropoulos, Angelos; Bardis, Kostas; Yazigi, George; Avdelidis, Nicolas P.; Droznika, Mark
    Aircraft maintenance is a complex process that requires a highly trained, qualified, and experienced team. The most frequent task in this process is the visual inspection of the airframe structure and engine for surface and sub-surface cracks, impact damage, corrosion, and other irregularities. Automated defect detection is a valuable tool for maintenance engineers to ensure safety and condition monitoring. The proposed approach is to process the captured feedback using various deep learning architectures to achieve the highest performance defect detections. Additionally, an algorithm is proposed to estimate the size of the detected defect. The team collaborated with TUI’s Airline Maintenance Team at Luton Airport, allowing us to fly a drone inside the hangar and use handheld cameras to collect representative data from their aircraft fleet. After a comprehensive dataset was constructed, multiple deep-learning architectures were developed and evaluated. The models were optimized for detecting various aircraft skin defects, with a focus on the challenging task of dent detection. The size estimation approach was evaluated in both controlled laboratory conditions and real-world hangar environments, providing insights into practical implementation challenges.
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    Automatic defect detection in infrared thermal images of ancient polyptychs based on numerical simulation and a new efficient channel attention mechanism aided Faster R-CNN model
    (Springer, 2024-09-16) Wang, Xin; Jiang, Guimin; Hu, Jue; Sfarra, Stefano; Mostacci, Miranda; Kouis, Dimitrios; Yang, Dazhi; Fernandes, Henrique; Avdelidis, Nicolas P.; Maldague, Xavier; Gai, Yonggang; Zhang, Hai
    In recent years, the preservation and conservation of ancient cultural heritage necessitate the advancement of sophisticated non-destructive testing methodologies to minimize potential damage to artworks. Therefore, this study aims to develop an advanced method for detecting defects in ancient polyptychs using infrared thermography. The test subjects are two polyptych samples replicating a 14th-century artwork by Pietro Lorenzetti (1280/85–1348) with varied pigments and artificially induced defects. To address these challenges, an automatic defect detection model is proposed, integrating numerical simulation and image processing within the Faster R-CNN architecture, utilizing VGG16 as the backbone network for feature extraction. Meanwhile, the model innovatively incorporates the efficient channel attention mechanism after the feature extraction stage, which significantly improves the feature characterization performance of the model in identifying small defects in ancient polyptychs. During training, numerical simulation is utilized to augment the infrared thermal image dataset, ensuring the accuracy of subsequent experimental sample testing. Empirical results demonstrate a substantial improvement in detection performance, compared with the original Faster R-CNN model, with the average precision at the intersection over union = 0.5 increasing to 87.3% and the average precision for small objects improving to 54.8%. These results highlight the practicality and effectiveness of the model, marking a significant progress in defect detection capability, providing a strong technical guarantee for the continuous conservation of cultural heritage, and offering directions for future studies.
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    A fault detection approach based on one-sided domain adaptation and generative adversarial networks for railway door systems
    (MDPI, 2023-12-07) Shimizu, Minoru; Zhao, Yifan; Avdelidis, Nicolas P.
    Fault detection using the domain adaptation technique is one of the more promising methods of solving the domain shift problem, and has therefore been intensively investigated in recent years. However, the domain adaptation method still has elements of impracticality: firstly, domain-specific decision boundaries are not taken into consideration, which often results in poor performance near the class boundary; and secondly, information on the source domain needs to be exploited with priority over information on the target domain, as the source domain can provide a rich dataset. Thus, the real-world implementations of this approach are still scarce. In order to address these issues, a novel fault detection approach based on one-sided domain adaptation for real-world railway door systems is proposed. An anomaly detector created using label-rich source domain data is used to generate distinctive source latent features, and the target domain features are then aligned toward the source latent features in a one-sided way. The performance and sensitivity analyses show that the proposed method is more accurate than alternative methods, with an F1 score of 97.9%, and is the most robust against variation in the input features. The proposed method also bridges the gap between theoretical domain adaptation research and tangible industrial applications. Furthermore, the proposed approach can be applied to conventional railway components and various electro-mechanical actuators. This is because the motor current signals used in this study are primarily obtained from the controller or motor drive, which eliminates the need for extra sensors.
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    Fusion and comparison of prognostic models for remaining useful life of aircraft systems
    (PHM Society, 2023-10-26) Fu, Shuai; Avdelidis, Nicolas P.; Plastropoulos, Angelos; Fan, Ip-Shing
    Changes in the performance of an aircraft system will straightforwardly affect the safe operation of the aircraft, and the technical requirements of Prognostics and Health Management (PHM) are highly relevant. Remaining Useful Life (RUL) prediction, part of the core technologies of PHM, is a cutting-edge innovation being worked on lately and an effective means to advance the change of upkeep support mode and work on the framework's security, unwavering quality, and economic reasonableness. This paper summarizes a detailed preliminary literature review and comparison of different prognostic approaches and the forecasting methods' taxonomy, the methodology's details, and provides its application to aircraft systems. It also provides a brief introduction to the predictive maintenance concept and condition-based maintenance (CBM). This article uses several predictive models to predict RUL and classifies conventional regression algorithms according to the similarity in function and form of the algorithms. More classical algorithms in each category are selected to compare the prediction results, and finally, the combined effects of the RUL prediction are obtained by weighted fusion, accuracy, and compatibility. The performance of the proposed models is assessed based on evaluations of RUL acquired from the hybrid and individual predictive models. This correlation depends on the most current prognostic metrics. The outcomes show that the proposed strategy develops precision, robustness, and adaptability. Hence, the work in this paper shall enrich the advancement of predictive maintenance and modern innovation of prognostic development.
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    Improved hybrid model for obstacle detection and avoidance in robot operating system framework (rapidly exploring random tree and dynamic windows approach)
    (MDPI, 2024-04-02) Adiuku, Ndidiamaka; Avdelidis, Nicolas P.; Tang, Gilbert; Plastropoulos, Angelos
    The integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines advanced sensing, robust obstacle detection, and avoidance mechanisms for an effective robot navigation experience. While hybrid methods with default robot operating system (ROS) navigation stack have demonstrated significant results, their performance in real time and highly dynamic environments remains a challenge. These environments are characterized by continuously changing conditions, which can impact the precision of obstacle detection systems and efficient avoidance control decision-making processes. In response to these challenges, this paper presents a novel solution that combines a rapidly exploring random tree (RRT)-integrated ROS navigation stack and a pre-trained YOLOv7 object detection model to enhance the capability of the developed work on the NAV-YOLO system. The proposed approach leveraged the high accuracy of YOLOv7 obstacle detection and the efficient path-planning capabilities of RRT and dynamic windows approach (DWA) to improve the navigation performance of mobile robots in real-world complex and dynamically changing settings. Extensive simulation and real-world robot platform experiments were conducted to evaluate the efficiency of the proposed solution. The result demonstrated a high-level obstacle avoidance capability, ensuring the safety and efficiency of mobile robot navigation operations in aviation environments.
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    Mobile robot obstacle detection and avoidance with NAV-YOLO
    (EJournal Publishing, 2024-03-22) Adiuku, Ndidiamaka; Avdelidis, Nicolas P.; Tang, Gilbert; Plastropoulos, Angelos; Diallo, Yanis
    Intelligent robotics is gaining significance in Maintenance, Repair, and Overhaul (MRO) hangar operations, where mobile robots navigate complex and dynamic environments for Aircraft visual inspection. Aircraft hangars are usually busy and changing, with objects of varying shapes and sizes presenting harsh obstacles and conditions that can lead to potential collisions and safety hazards. This makes Obstacle detection and avoidance critical for safe and efficient robot navigation tasks. Conventional methods have been applied with computational issues, while learning-based approaches are limited in detection accuracy. This paper proposes a vision-based navigation model that integrates a pre-trained Yolov5 object detection model into a Robot Operating System (ROS) navigation stack to optimise obstacle detection and avoidance in a complex environment. The experiment is validated and evaluated in ROS-Gazebo simulation and turtlebot3 waffle-pi robot platform. The results showed that the robot can increasingly detect and avoid obstacles without colliding while navigating through different checkpoints to the target location.
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    Non-invasive inspection for a hand-bound book of the 19th century: numerical simulations and experimental analysis of infrared, terahertz, and ultrasonic methods
    (Elsevier, 2024-05-24) Jiang, Guimin; Zhu, Pengfei; Gai, Yonggang; Jiang, Tingyi; Yang, Dazhi; Sfarra, Stefano; Waschkies, Thomas; Osman, Ahmad; Fernandes, Henrique; Avdelidis, Nicolas P.; Maldague, Xavier; Zhang, Hai
    Due to fungal growth and mishandling in the book, there are various types of defects as they age such as foxing, tears, and creases. It is important to develop novel non-invasive inspection techniques and defect recognition algorithms. In this work, three non-invasive inspection techniques, including infrared thermography (IRT), terahertz time-domain spectroscopy (THz-TDS), and air-coupled ultrasound (ACU), were employed for the detection of defects in an ancient book cover. To improve the image quality and defect contrast, principal component analysis, fast Fourier transform, and partial least squares regression algorithms are used as the post-processing methods. Furthermore, the YOLOv7 network is deployed for defect automatic detection. Finite element analysis and finite-difference time-domain methods were employed for generating training dataset of YOLOv7 network. Experimental results demonstrate that IRT and THz-TDS has excellent detection capability for surface and subsurface defects, respectively. By employing YOLOv7 network with simulation datasets, defects can be effectively identified.
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    Novel prognostic methodology of bootstrap forest and hyperbolic tangent boosted neural network for aircraft system
    (MDPI, 2024-06-10) Fu, Shuai; Avdelidis, Nicolas P.
    Complex aviation systems’ integrity deteriorates over time due to operational factors; hence, the ability to forecast component remaining useful life (RUL) is vital to their optimal operation. Data-driven prognostic models are essential for system RUL prediction. These models benefit run-to-failure datasets the most. Thus, significant factors that could affect systematic integrity must be examined to quantify the operational component of RUL. To expand predictive approaches, the authors of this research developed a novel method for calculating the RUL of a group of aircraft engines using the N-CMAPSS dataset, which provides simulated degradation trajectories under real flight conditions. They offered bootstrap trees and hyperbolic tangent NtanH(3)Boost(20) neural networks as prognostic alternatives. The hyperbolic tangent boosted neural network uses damage propagation modelling based on earlier research and adds two accuracy levels. The suggested neural network architecture activates with the hyperbolic tangent function. This extension links the deterioration process to its operating history, improving degradation modelling. During validation, models accurately predicted observed flight cycles with 95–97% accuracy. We can use this work to combine prognostic approaches to extend the lifespan of critical aircraft systems and assist maintenance approaches in reducing operational and environmental hazards, all while maintaining normal operation. The proposed methodology yields promising results, making it suitable for adoption due to its relevance to prognostic difficulties.
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    A prognostic approach to improve system reliability for aircraft system
    (IEEE, 2023-01-08) Fu, Shuai; Avdelidis, Nicolas P.; Jennions, Ian K.
    The primary aims of prognostics encompass the timely detection of potential failures, mitigation or elimination of unscheduled maintenance, prediction of the most suitable timing for preventive maintenance replacement, optimization of maintenance cycles and operational readiness, and enhancement of system reliability by improving design and logistical support for existing systems. In order to facilitate the progress of these approaches, currently available datasets provide a unique and reliable compilation of flight-to-failure trajectories linked to small aircraft engines that have been observed in actual flight conditions. Furthermore, the paper offered an improved neural network that utilized the TanH hyperbolic tangent function. This neural network was enhanced later by integrating it with the TanH, linear, and Gaussian functions. Additionally, a random holdback validation approach was employed in the paper. The results suggest that the NN TanH technique, when implemented, has the potential to significantly enhance the reliability of an aircraft component. This is achieved through accurate estimates of the remaining useful life (RUL) and a proactive understanding of the failure system.