The vast network of oil and gas transmission pipelines requires periodic monitoring for maintenance and hazard inspection to avoid equipment failure and potential accidents. The severe COVID-19 pandemic situation forced the companies to shrink the size of their teams. One risk which is faced on-site is represented by the uncontrolled release of flammable oil and gas. Among many inspection methods, the unmanned aerial vehicle system contains flexibility and stability. Unmanned aerial vehicles can transfer data in real-time, while they are doing their monitoring tasks. The current article focuses on unmanned aerial vehicles equipped with optical sensing and artificial intelligence, especially image recognition with deep learning techniques for pipeline surveillance. Unmanned aerial vehicles can be used for regular patrolling duties to identify and capture images and videos of the area of interest. Places that are hard to reach will be accessed faster, cheaper and with less risk. The current paper is based on the idea of capturing video and images of drone-based inspections, which can discover several potential hazardous problems before they become dangerous. Damage can emerge as a weakening of the cladding on the external pipe insulation. There can also be the case when the thickness of piping through external corrosion can occur. The paper describes a survey completed by experts from the oil and gas industry done for finding the functional and non-functional requirements of the proposed system.
翻译:巨大的石油和天然气输送管道网络要求定期监测保养和危险检查,以避免设备故障和潜在事故。COVID-19大流行情况迫使各公司缩小其团队的规模。现场面临的一个风险是易燃油气的无节制释放。在许多检查方法中,无人驾驶飞行器系统具有灵活性和稳定性。无人驾驶航空器系统可以实时传输数据,而无人驾驶飞行器则在履行其监测任务时可以发现若干潜在的危险问题。目前的文章侧重于配备有光学感应和人工智能的无人驾驶飞行器,特别是具有管道监测深层学习技术的图像识别。无人驾驶航空器可用于定期巡逻,以查明和捕捉利益区图像和视频。难以到达的地点将更快、更便宜、风险较小。目前的文件基于采集视频和无人驾驶飞行器图像的想法,在无人驾驶飞行器开展监测任务之前可以发现若干潜在的危险问题。损害可能随着外部管道隔断的减弱而出现。还可能出现这种情况:通过外部凝固系统进行透析的厚度,从而能够从外部凝固和功能化系统找到石油的功能性要求。