The need for safe operation and effective maintenance of pipelines grows as oil and gas demand rises.Thereby,it is increasingly imperative to monitor and inspect the pipeline system,detect causes contributing to devel...The need for safe operation and effective maintenance of pipelines grows as oil and gas demand rises.Thereby,it is increasingly imperative to monitor and inspect the pipeline system,detect causes contributing to developing pipeline damage,and perform preventive maintenance in a timely manner.Currently,pipeline inspection is performed at pre-determined intervals of several months,which is not sufficiently robust in terms of timeliness.This research proposes a drone and artificial intelligence reconsolidated technological solution(DARTS) by integrating drone technology and deep learning technique.This solution is aimed to detect the targeted potential root problems-pipes out of alignment and deterioration of pipe support system-that can cause critical pipeline failures and predict the progress of the detected problems by collecting and analyzing image data periodically.The test results show that DARTS can be effectively used to support decision making for preventive pipeline maintenance to increase pipeline system s afety and resilience.展开更多
基金This project was partially supported by the Center for Midstream and Management Science at Lamar University,Beaumont,Texas,USA.
文摘The need for safe operation and effective maintenance of pipelines grows as oil and gas demand rises.Thereby,it is increasingly imperative to monitor and inspect the pipeline system,detect causes contributing to developing pipeline damage,and perform preventive maintenance in a timely manner.Currently,pipeline inspection is performed at pre-determined intervals of several months,which is not sufficiently robust in terms of timeliness.This research proposes a drone and artificial intelligence reconsolidated technological solution(DARTS) by integrating drone technology and deep learning technique.This solution is aimed to detect the targeted potential root problems-pipes out of alignment and deterioration of pipe support system-that can cause critical pipeline failures and predict the progress of the detected problems by collecting and analyzing image data periodically.The test results show that DARTS can be effectively used to support decision making for preventive pipeline maintenance to increase pipeline system s afety and resilience.
