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

High-throughput screening technologies for identification and expression of functional domains of proteins of biomedical importance

The ability to produce multi-milligram quantities of a recombinant ‘target’ protein in a proteolytically stable, soluble, and functional form is often necessary for subsequent biochemical, biophysical and structure-based analyses. Sub-constructs expressing only part of a large target protein can often be useful. Combinatorial Domain Hunting (CDH) is a methodology that allows the rapid production of sub- constructs via a random DNA fragmentation technique. One particular issue with CDH is that it can be used to identify globular regions or domains of a target protein, but does not take account of the functional properties of such domains; therefore some ‘hits’ are not useful, because they exclude these functional regions. Here, we have attempted to enhance the CDH methodology by including an additional screening step that could specifically identify those constructs expressing functional protein domains. However, whilst rigorous testing of this functionality screen proved it to be successful under selective conditions, it was not considered suitable for inclusion in the CDH method. CDH was also used to identify highly expressed, proteolytically stable regions of a previously largely uncharacterized protein, and to investigate their functionality. Human Claspin is a large, highly charged, S=phase specific ‘molecular scaffold’ protein, with no identifiable sub-domains or enzymatic function(s). However, Claspin is known to make multiple different protein-protein interactions at replication forks during the intertwined processes of DNA replication and DNA replication-coupled repair. CDH successfully identified a number of N-terminal expression constructs that could be expressed and purified to a high degree of homogeneity. Structural and functional analyses of these protein fragments indicated that the N-terminus of human Claspin is intrinsically disordered, and elongated in nature. However, these regions may become ordered upon binding to their respective protein or macromolecular partner(s). Furthermore, several N-terminal fragments were found to be able to bind to both single- or double-stranded DNA when longer than 16 nucleotides/base-pairs in length. Additionally, the phospho-specific protein-protein interaction made by human Claspin, with the checkpoint kinase Chk1 was further investigated

Application of Nanomaterials in Biomedical Imaging and Cancer Therapy

To mark the recent advances in nanomaterials and nanotechnology in biomedical imaging and cancer therapy, this book, entitled Application of Nanomaterials in Biomedical Imaging and Cancer Therapy includes a collection of important nanomaterial studies on medical imaging and therapy. The book covers recent works on hyperthermia, external beam radiotherapy, MRI-guided radiotherapy, immunotherapy, photothermal therapy, and photodynamic therapy, as well as medical imaging, including high-contrast and deep-tissue imaging, quantum sensing, super-resolution microscopy, and three-dimensional correlative light and electron microscopy. The significant research results and findings explored in this work are expected to help students, researchers and teachers working in the field of nanomaterials and nanotechnology in biomedical physics, to keep pace with the rapid development and the applications of nanomaterials in precise imaging and targeted therapy