Online Dynamic Reliability Evaluation of Wind Turbines based on Drone-assisted Monitoring

The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach.Comment: A modified version of this work has been published in the 2022 International Conference on Computing, Electronics & Communications Engineering (iCCECE). This work is a draft author versio

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well

A review of non-destructive testing on wind turbines blades

Wind energy, with an exponential growth in the last years, is nowadays one of the most important renewable energy sources. Modern wind turbines are bigger and complex to produce more energy. This industry requires to reduce its operating and maintenance costs and to increase its reliability, safety, maintainability and availability. Condition monitoring systems are beginning to be employed for this purpose. They must be reliable and cost-effective to reduce the long periods of downtimes and high maintenance costs, and to avoid catastrophic scenarios caused by undetected failures. This paper presents a survey about the most important and updated condition monitoring techniques based on non-destructive testing and methods applied to wind turbine blades. In addition, it analyses the future trends and challenges of structural health monitoring systems in wind turbine blades.Wind energy, with an exponential growth in the last years, is nowadays one of the most important renewable energy sources. Modern wind turbines are bigger and complex to produce more energy. This industry requires to reduce its operating and maintenance costs and to increase its reliability, safety, maintainability and availability. Condition monitoring systems are beginning to be employed for this purpose. They must be reliable and cost-effective to reduce the long periods of downtimes and high maintenance costs, and to avoid catastrophic scenarios caused by undetected failures. This paper presents a survey about the most important and updated condition monitoring techniques based on non-destructive testing and methods applied to wind turbine blades. In addition, it analyses the future trends and challenges of structural health monitoring systems in wind turbine blades

Computer Vision in Wind Turbine Blade Inspections: An Analysis of Resolution Impact on Detection and Classification of Leading-Edge Erosion

Wind turbines, as critical components of the renewable energy industry, present unique maintenance challenges, particularly in remote or challenging locations such as offshore wind farms. These are amplified in the inspection of leading-edge erosion on wind turbine blades, a task still largely reliant on traditional methods. Emerging technologies like computer vision and object detection offer promising avenues for enhancing inspections, potentially reducing operational costs and human-associated risks. However, variability in image resolution, a critical factor for these technologies, remains a largely underexplored aspect in the wind energy context. This study explores the application of machine learning in detecting and categorizing leading edge erosion damage on wind turbine blades. YOLOv7, a state-of-the-art object detection model, is trained with a custom dataset consisting of images displaying various forms of leading edge erosion, representing multiple categories of damage severity. Trained model is tested on images acquired with three different tools, each providing images with a different resolution. The effect of image resolution on the performance of the custom object detection model is examined. The research affirms that the YOLOv7 model performs exceptionally well in identifying the most severe types of LEE damage, usually classified as Category 3, characterized by distinct visual features. However, the model's ability to detect less severe damage, namely Category 1 and 2, which are crucial for early detection and preventive measures, exhibits room for improvement. The findings point to a potential correlation between input image resolution and detection confidence in the context of wind turbine maintenance. These results stress the need for high-resolution images, leading to a discussion on the selection of appropriate imaging hardware and the creation of machine learning-ready datasets. The study thereby emphasizes the importance of industry-wide efforts to compile standardized image datasets and the potential impact of machine learning techniques on the efficiency of visual inspections and maintenance strategies. Future directions are proposed with the ultimate aim of enhancing the application of artificial intelligence in wind energy maintenance and management, enabling more efficient and effective operational procedures, and driving the industry towards a more sustainable future

Classification, Localization, and Quantification of Structural Damage in Concrete Structures using Convolutional Neural Networks

Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have recently become of great interest owing to their superior ability to detect damage in engineering structures. ML algorithms used in this domain are classified into two major subfields: vibration-based and image-based SHM. Traditional condition survey techniques based on visual inspection have been the most widely used for monitoring concrete structures in service. Inspectors visually evaluate defects based on experience and engineering judgment. However, this process is subjective, time-consuming, and hampered by difficult access to numerous parts of complex structures. Accordingly, the present study proposes a nearly automated inspection model based on image processing, signal processing, and deep learning for detecting defects and identifying damage locations in typically inaccessible areas of concrete structures. The work conducted in this thesis achieved excellent damage localization and classification performance and could offer a nearly automated inspection platform for the colossal backlog of ageing civil engineering structures

Machine learning techniques for robotic and autonomous inspection of mechanical systems and civil infrastructure

Machine learning and in particular deep learning techniques have demonstrated the most efficacy in training, learning, analyzing, and modelling large complex structured and unstructured datasets. These techniques have recently been commonly deployed in different industries to support robotic and autonomous system (RAS) requirements and applications ranging from planning and navigation to machine vision and robot manipulation in complex environments. This paper reviews the state-of-the-art with regard to RAS technologies (including unmanned marine robot systems, unmanned ground robot systems, climbing and crawler robots, unmanned aerial vehicles, and space robot systems) and their application for the inspection and monitoring of mechanical systems and civil infrastructure. We explore various types of data provided by such systems and the analytical techniques being adopted to process and analyze these data. This paper provides a brief overview of machine learning and deep learning techniques, and more importantly, a classification of the literature which have reported the deployment of such techniques for RAS-based inspection and monitoring of utility pipelines, wind turbines, aircrafts, power lines, pressure vessels, bridges, etc. Our research provides documented information on the use of advanced data-driven technologies in the analysis of critical assets and examines the main challenges to the applications of such technologies in the industry

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

Addressing Complexity and Intelligence in Systems Dependability Evaluation

Engineering and computing systems are increasingly complex, intelligent, and open adaptive. When it comes to the dependability evaluation of such systems, there are certain challenges posed by the characteristics of “complexity” and “intelligence”. The first aspect of complexity is the dependability modelling of large systems with many interconnected components and dynamic behaviours such as Priority, Sequencing and Repairs. To address this, the thesis proposes a novel hierarchical solution to dynamic fault tree analysis using Semi-Markov Processes. A second aspect of complexity is the environmental conditions that may impact dependability and their modelling. For instance, weather and logistics can influence maintenance actions and hence dependability of an offshore wind farm. The thesis proposes a semi-Markov-based maintenance model called “Butterfly Maintenance Model (BMM)” to model this complexity and accommodate it in dependability evaluation. A third aspect of complexity is the open nature of system of systems like swarms of drones which makes complete design-time dependability analysis infeasible. To address this aspect, the thesis proposes a dynamic dependability evaluation method using Fault Trees and Markov-Models at runtime.The challenge of “intelligence” arises because Machine Learning (ML) components do not exhibit programmed behaviour; their behaviour is learned from data. However, in traditional dependability analysis, systems are assumed to be programmed or designed. When a system has learned from data, then a distributional shift of operational data from training data may cause ML to behave incorrectly, e.g., misclassify objects. To address this, a new approach called SafeML is developed that uses statistical distance measures for monitoring the performance of ML against such distributional shifts. The thesis develops the proposed models, and evaluates them on case studies, highlighting improvements to the state-of-the-art, limitations and future work