A Smart Autonomous Pipeline Inspection Robot

智能自主管道检测机器人

• 批准号: RGPIN-2014-06512
• 负责人: Gueaieb, Wail
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=589058
• 关键词: Smart; Autonomous; Pipeline; Inspection; Robot

Currently, very little internal inspection is conducted on gas pipe segments that are known to leak. The maintenance of commercial gas distribution networks is often based on statistical information, such as previous maintenance record, leak surveys, and corrosion data, more than scientific evidence. To date, the state-of-the-art of internal gas pipe inspection systems is based on tethered robot-like vehicles that are inserted in the pipes and operated manually through joysticks. They collect sensory data and store it locally so that it can be processed offline later by trained technicians to identify any signs of defects in the pipe. This process is very costly and time consuming. Typically, it takes a crew of technicians a full day to collect data for a pipe segment of 1 mile, and a week to process it. At this pace, it would be impossible to cover gas and oil distribution networks, which already passed 115,000 in Canada. In 2012, it costed Canadian gas and oil distribution companies $1.1 billion to inspect and maintain their mains. Finding more cost- and time-efficient ways to monitor and inspect distribution and delivery systems has become an urgent necessity, especially with the aging of the mains and the rapid growth of the network driven by high demand on energy sources. The goal of this research is to design and develop a fully autonomous untethered mobile robot capable of long-range navigation inside gas pipes of various sizes and structural configurations to perform in-situ pipe inspection. The robot will automatically diagnose pipes for different types of defects, including cracks, dents, corrosion, accumulated debris, and water infiltration. It will process and analyze its sensory measurements online and wirelessly communicate detailed reports back to a maintenance unit. This would facilitate identifying the exact locations of the defects, generate early warnings for potential anomalous segments, help deciding on the proper repair method (spot, relining, replacement, etc.), and decrease the response time in case of an emergency. The research will help insuring the reliability of Canadian gas distribution networks, which transport about 23% of Canadian exports (including oil exports). It will minimizes the chances of gas leaks, especially in remote areas where technicians cannot reach easily (eg., offshore), and so limit their side effects on the environment. Advancing the state of knowledge in this area will have a positive impact on other economic sectors by paving the road for the development of similar robot inspection systems for oil, water, and sewage distribution networks.

当前，对已知会泄漏的燃气管段进行的内部检查很少。商业气体分销网络的维护通常基于统计信息，例如先前的维护记录，泄漏调查和腐蚀数据，而不是科学证据。迄今为止，内部燃气管检查系统的最先进是基于绑扎机器人的车辆，这些车辆被插入管道并通过操纵杆手动操作。他们收集感官数据并在本地存储它，以便稍后可以通过训练有素的技术人员离线处理，以确定管道中的任何缺陷迹象。这个过程非常昂贵且耗时。通常，需要一整天的技术人员来收集1英里的管段的数据，并需要一周的时间进行处理。按照这种速度，不可能覆盖加拿大已经通过了115,000人的天然气和石油分销网络。 2012年，它花费了加拿大天然气和石油分销公司11亿加元来检查和维护其电源。寻找更具成本和时间效率的方法来监视和检查分销和交付系统已成为迫切的必要性，尤其是随着电源的老化以及对能源需求的高需求驱动的网络的快速增长。 这项研究的目的是设计和开发一个完全自主的无限制的移动机器人，能够在各种尺寸和结构配置的气管内进行远程导航，以执行原位管道检查。机器人将自动诊断出不同类型的缺陷的管道，包括裂缝，凹痕，腐蚀，累积的碎屑和水渗透。它将在线处理和分析其感官测量结果，并无线地将详细报告返回到维护单元。这将有助于识别缺陷的确切位置，为潜在的异常部门发出早期警告，有助于确定适当的维修方法（点，重新安装，替换等），并减少紧急情况下的响应时间。这项研究将有助于确保加拿大天然气分销网络的可靠性，该网络的可靠性约为加拿大出口（包括石油出口）。它将最小化气体泄漏的机会，尤其是在技术人员无法轻易到达的偏远地区（例如，离岸），因此限制了它们对环境的副作用。通过为开发类似的机器人检查系统开发用于石油，水和污水处理网络的机器人检查系统的道路，推进该领域的知识状态将对其他经济部门产生积极影响。