A real-time material breakage detection for offshore wind turbines based on improved neural network algorithm
The integrity of offshore wind turbines, pivotal for sustainable energy generation, is often compromised by surface material defects. Despite the availability of various detection techniques, limitations persist regarding cost-effectiveness, efficiency, and applicability. Addressing these shortcomings, this study introduces a novel approach leveraging an advanced version of the YOLOv8 object detection model, supplemented with a Convolutional Block Attention Module (CBAM) for improved feature recognition. The optimized loss function further refines the learning process. Employing a dataset of 5,432 images from the Saemangeum offshore wind farm and a publicly available dataset, our method underwent rigorous testing. The findings reveal a substantial enhancement in defect detection stability, marking a significant stride towards efficient turbine maintenance. This study's contributions illuminate the path for future research, potentially revolutionizing sustainable energy practices.
Code (0)
등록된 구현이 없습니다.
Tasks
Defect Detectionobject-detectionObject DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Tool Breakage Detection using Deep Learning
In manufacture, steel and other metals are mainly cut and shaped during the fabrication process by computer numerical control (CNC) machines. To keep high productivity and efficiency of the fabrication process, engineers…
Deep LearningManagementSINBAD: Saliency-informed detection of breakage caused by ad blocking
Privacy-enhancing blocking tools based on filter-list rules tend to break legitimate functionality. Filter-list maintainers could benefit from automated breakage detection tools that allow them to proactively fix problem…
BlockingAdvanced monitoring of rail breakage in double-track railway lines by means of PCA techniques
This work describes a classifier designed to identify rail breakages in double-track railway lines, completing the electronic equipment carried out by authors. The main objective of this proposal is to guarantee the inte…
Diagnostic Digital Twin for Anomaly Detection in Floating Offshore Wind Energy
The demand for condition-based and predictive maintenance is rising across industries, especially for remote, high-value, and high-risk assets. In this article, the diagnostic digital twin concept is introduced, discusse…
Anomaly DetectionDiagnosticFault DiagnosisLOTUSim-Energy: A Maritime Simulator for Human-Drone Interaction in Autonomous Offshore Operation \& Maintenance
Offshore maintenance requires operations in the air, the surface, and the subsea domain and include human supervision. This paper presents LOTUSim-Energy, a real-time maritime simulator designed for multi-domain human--d…