摘要
途经地质灾害严重地区的油气长输管道易受土壤外部载荷作用而发生弯曲变形,对管道安全运营造成严重威胁。基于惯性测量单元(Inertial Measurement Unit,IMU)的内检测技术是目前检测管道局部变形的主要手段,给出了埋地管道弯头、管道凹陷、管道弯曲变形、环焊缝异常4类典型的局部变形管段的IMU数据特征,提出了基于小波降噪的IMU数据预处理方法,建立了识别4类典型局部变形管段IMU数据热力图的深层神经网络模型,构建了一套基于IMU数据的管道弯曲变形段识别方法。采用新建方法对中俄原油管道6年的IMU数据开展分析,形成了33177份样本管段数据,建立了中国IMU弯曲应变特征数据库。实例应用结果表明:基于该数据库建立的深层神经网络模型对管道弯曲变形段识别准确率超过了90%,识别效率达0.02 min/km。基于IMU数据的管道弯曲变形段识别方法为管道完整性评价中弯曲应变大于0.125%变形段的识别提供了有效的技术手段。
Long-distance oil and gas pipelines passing through the areas with serious geological hazards are prone to bending deformation due to the external soil loads,which will threaten the safe operation of pipelines.The inline inspection technology based on the inertial measurement unit(IMU)is the main means to inspect the local deformation of pipelines at present.Herein,the characteristics of IMU data of the four typical locally-deformed pipeline sections,i.e.the buried pipeline elbows,the dented pipelines,the pipelines with bending deformation and the abnormal girth welds,were provided.Meanwhile,a IMU data pre-processing method based on wavelet denoising was put forward,a deep neural network model was established to identify the IMU data thermal map of the 4 types of typical locally-deformed pipeline sections,and a set of method was developed to identify the pipeline sections with bending deformation based on IMU data.By analyzing the 6-year IMU data of China-Russia Crude Oil Pipeline with the new method,totally 33177 data of sample pipeline sections were formed,and an IMU bending strain database was set in China.The results of application example show that:the accuracy of the deep neural network model established based on the database to identify the pipeline sections with bending deformation is more than 90%,and the identification efficiency is up to 0.02 min/km.Hence,the method for identification of pipeline sections with bending deformation based on IMU data provides an effective technical means to identify the deformed pipeline sections with bending strain greater than 0.125%in the integrity assessment of pipelines.
作者
刘啸奔
刘燊
季蓓蕾
陈朋超
赵晓利
李睿
张宏
LIU Xiaoben;LIU Shen;JI Beilei;CHEN Pengchao;ZHAO Xiaoli;LI Rui;ZHANG Hong(College of Mechanical and Transportation Engineering,China University of Petroleum(Beijing)//National Engineering Laboratory for Pipeline Safety//MOE Key Laboratory of Petroleum Engineering//Beijing Key Laboratory of Urban Oil and Gas Distribution Technology;PipeChina North Pipeline Company)
出处
《油气储运》
CAS
北大核心
2021年第11期1228-1235,共8页
Oil & Gas Storage and Transportation
基金
国家自然科学基金资助项目“逆断层作用下X80管道屈曲演化与韧性破损机理研究”,52004314
北京市自然科学基金资助项目“时变温压荷载作用下大口径直埋热水管道-土体耦合机制与失效机理研究”,8214053
新疆自治区天山青年计划项目“复杂载荷作用下高钢级管道韧性断裂与后屈曲失效行为”,2019Q088
中国石油大学(北京)青年拔尖人才科研基金资助项目“断层作用下高强钢管道失效机理与可靠性评价”,2462018YJRC019
中国石油大学(北京)科研基金资助项目“基于大数据的天然气管网智能运行与控制研究”,2462020YXZZ045。
关键词
油气长输管道
地质灾害
弯曲变形
惯性测量单元
机器学习
智能识别
long-distance oil and gas pipelines
geological hazard
bending deformation
inertial measurement unit
machine learning
intelligent identification
