In-service X-ray radiography of offshore wind blades (RADBLAD)

海上风电叶片的在役 X 射线照相 (RADBLAD)

• 批准号: 104076
• 金额: $ 50.13万
• 依托单位: INNVOTEK LTD
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=104076
• 关键词: service; ray; radiography; offshore; wind

"The first phase of the project will demonstrate the feasibility of using agile mobile wall-climbing robots, portable radiography equipment and artificial intelligence (AI) technologies to perform asset management of off-shore wind farms located in extreme and challenging environments. Gaining access to off-shore wind turbine blades is difficult and expensive e.g. with large cranes on ships or climbing with rope/platform systems. Because of the difficulty of deploying large systems off-shore, a modular approach is required to easily transport lightweight systems to test sites and assemble them quickly. A modular wall climbing robot, assembled on site, will carry the radiography deployment system up the wind tower. Two arms will extend on either side of a blade positioned in a 90 degree configuration so that the source and detector are on either side of the blade. An intelligent positioning system will keep the two aligned and normal to each other as the complex aerodynamic shape of the blade is inspected along the width of the blade. The climbing robot will then move a small distance up the tower and repeat coverage of the blade width. Allowing a full length of the blade, with inspection starting from the tip of the blade up to the blade root.An intelligent control positioning system will be required since the blade shape and curvature will be different in each horizontal plane and the source/detector will need to adapt to the shape for optimum imaging. To cope with blade in-plane and out-of-plane vibrations, the X-ray source and detector will be kept stationary with respect to a blade by designing passive compliance into the deployment arm.Embedded intelligence will carry out automated defect detection and classification and enable autonomous decision making for further inspection and intervention."

“该项目的第一阶段将展示使用敏捷的移动的爬壁机器人，便携式X射线摄影设备和人工智能（AI）技术对位于极端和具有挑战性环境中的海上风电场进行资产管理的可行性。接近离岸风力涡轮机叶片是困难且昂贵的，例如，使用船上的大型起重机或使用绳索/平台系统攀爬。由于在海上部署大型系统的困难，需要采用模块化方法将轻型系统轻松运输到测试现场并快速组装。一个模块化的爬壁机器人，在现场组装，将携带射线照相部署系统的风塔。两个臂将在以90度配置定位的叶片的任一侧上延伸，使得源和检测器在叶片的任一侧上。智能定位系统将保持这两个对齐和相互垂直的复杂的空气动力学形状的叶片被检查沿着叶片的宽度。然后，攀爬机器人将沿塔架向上移动一小段距离，并重复覆盖叶片宽度。允许对叶片的全长进行检查，从叶片尖端到叶片根部。需要智能控制定位系统，因为叶片形状和曲率在每个水平面上都不同，并且源/探测器需要适应形状以获得最佳成像。为了科普叶片的面内和面外振动，X射线源和探测器将通过在部署臂中设计被动顺应性来保持相对于叶片的固定。嵌入式智能将执行自动缺陷检测和分类，并实现进一步检查和干预的自主决策。"