Tidal stream generators, current state and potential opportunities for condition monitoring

Tidal power industry has made significant progress towards commercialization over the past decade. Significant investments from sector leaders, strong technical progress and positive media coverage have established the credibility of this specific renewable energy source. However, its progress is being retarded by operation and maintenance problems, which results in very low operational availability times, as low as 25 %. This paper presents a literature review of the current state of tidal device operators as well as some commercial tidal turbine condition monitoring solutions. Furthermore, an overview is given of the global tidal activity status (tidal energy market size and geography), the key industry activity and the regulations-standards related with tidal energy industry. Therefore, the main goal of this paper is to provide a bird’s view of the current status of the tidal power industry to serve as a roadmap for the academia regarding the real needs of the tidal power industry

Thermographic non-destructive evaluation for natural fiber-reinforced composite laminates

Natural fibers, including mineral and plant fibers, are increasingly used for polymer composite materials due to their low environmental impact. In this paper, thermographic non-destructive inspection techniques were used to evaluate and characterize basalt, jute/hemp and bagasse fibers composite panels. Different defects were analyzed in terms of impact damage, delaminations and resin abnormalities. Of particular interest, homogeneous particleboards of sugarcane bagasse, a new plant fiber material, were studied. Pulsed phase thermography and principal component thermography were used as the post-processing methods. In addition, ultrasonic C-scan and continuous wave terahertz imaging were also carried out on the mineral fiber laminates for comparative purposes. Finally, an analytical comparison of different methods was give

Thermography data fusion and non-negative matrix factorization for the evaluation of cultural heritage objects and buildings

The application of the thermal and infrared technology in different areas of research is considerably increasing. These applications involve nondestructive testing, medical analysis (computer aid diagnosis/detection—CAD), and arts and archeology, among many others. In the arts and archeology field, infrared technology provides significant contributions in terms of finding defects of possible impaired regions. This has been done through a wide range of different thermographic experiments and infrared methods. The proposed approach here focuses on application of some known factor analysis methods such as standard nonnegative matrix factorization (NMF) optimized by gradient-descent-based multiplicative rules (SNMF1) and standard NMF optimized by nonnegative least squares active-set algorithm (SNMF2) and eigen-decomposition approaches such as principal component analysis (PCA) in thermography, and candid covariance-free incremental principal component analysis in thermography to obtain the thermal features. On the one hand, these methods are usually applied as preprocessing before clustering for the purpose of segmentation of possible defects. On the other hand, a wavelet-based data fusion combines the data of each method with PCA to increase the accuracy of the algorithm. The quantitative assessment of these approaches indicates considerable segmentation along with the reasonable computational complexity. It shows the promising performance and demonstrated a confirmation for the outlined properties. In particular, a polychromatic wooden statue, a fresco, a painting on canvas, and a building were analyzed using the above-mentioned methods, and the accuracy of defect (or targeted) region segmentation up to 71.98%, 57.10%, 49.27%, and 68.53% was obtained, respectively

A prognostic approach to improve system reliability for aircraft system

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.European Commission: Grant Number 95568

A fault detection approach based on one-sided domain adaptation and generative adversarial networks for railway door systems

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

Automated aircraft dent inspection via a modified Fourier transform profilometry algorithm

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.Cranfield IVHM Centr

Improved hybrid model for obstacle detection and avoidance in robot operating system framework (rapidly exploring random tree and dynamic windows approach)

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

Advancements in learning-based navigation systems for robotic applications in MRO hangar: review

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

Fusion and comparison of prognostic models for remaining useful life of aircraft systems

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

Mobile robot obstacle detection and avoidance with NAV-YOLO

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