Crawler-Based Automated Non-Contact Ultrasonic Inspection of Large Structural Assets

Ross McMillan, M. Tabatabaeipour, K. Tzaferis, William Jackson, Rachel S. Edwards, O. Trushkevych, C. Macleod, G. Dobie, A. Gachagan
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Abstract

This paper presents an update on the progress of developing a crawler-based automated non-contact ultrasonic inspection system for the evaluation of large structural assets. The system presented is a significant improvement on current robotic NDT crawlers and aims to greatly reduce the time of inspection by creating an internal feature map of the subject in a Simultaneous Localisation And Mapping (SLAM) style method instead of using a lawnmower scanning style where all areas are scanned regardless if they contain features or are featureless. This map will be generated through rapid automated path planning and scanning and will show the location of potential areas of interest, where then, the appropriate method of inspection can be used for a high detailed evaluation. Current and ongoing work presented is as follows; the use of guided waves as the sensory input of an occupancy grid map; evaluating guided wave modes to find the mode most appropriate for this system; minimum thickness estimation using machine learning; improving the transducer setup using a unidirectional transmitter.
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基于履带的大型结构资产自动非接触式超声检测
本文介绍了用于大型结构资产评估的履带式自动非接触式超声检测系统的最新进展。提出的系统是对当前机器人无损检测爬行器的重大改进,旨在通过以同步定位和映射(SLAM)风格的方法创建主题的内部特征地图,而不是使用割草机扫描风格,对所有区域进行扫描,无论它们是否包含特征或无特征,从而大大减少检查时间。该地图将通过快速自动路径规划和扫描生成,并将显示潜在感兴趣区域的位置,然后,可以使用适当的检查方法进行高度详细的评估。目前和正在进行的工作如下:使用导波作为占用网格图的感官输入;对导波模态进行评估,找出最适合该系统的模态;使用机器学习进行最小厚度估计;改进使用单向发射器的换能器设置。
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