|本期目录/Table of Contents|

[1]吴远灯,刘少胡,马卫国.基于GRA-PSO-BP的连续管疲劳寿命预测研究*[J].中国安全生产科学技术,2023,19(6):135-142.[doi:10.11731/j.issn.1673-193x.2023.06.019]
　WU Yuandeng,LIU Shaohu,MA Weiguo.Study on fatigue life prediction of coiled tubing based on GRA-PSO-BP[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(6):135-142.[doi:10.11731/j.issn.1673-193x.2023.06.019]
点击复制

基于GRA-PSO-BP的连续管疲劳寿命预测研究*
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
19
期数:
2023年6期
页码:
135-142
栏目:
职业安全卫生管理与技术
出版日期:
2023-06-30

文章信息/Info

Title:
Study on fatigue life prediction of coiled tubing based on GRA-PSO-BP
文章编号:
1673-193X(2023)-06-0135-08
作者:
吴远灯; 刘少胡; 马卫国
(长江大学 机械工程学院，湖北 荆州 434023)
Author(s):
WU Yuandeng;  LIU Shaohu;  MA Weiguo
(School of Mechanical Engineering,Yangtze University,Jingzhou Hubei 434023,China)
关键词:
连续管; 疲劳寿命预测; 灰度关联法; PSO-BP模型
Keywords:
coiled tubing;  fatigue life prediction;  gray correlation method;  PSO-BP model
分类号:
X941
DOI:
10.11731/j.issn.1673-193x.2023.06.019
文献标志码:
A
摘要:
为准确预测连续管的多轴疲劳寿命，分析其疲劳寿命的影响因素，建立基于GRA-PSO-BP的连续管疲劳寿命预测模型。采用灰度关联法（GRA）分析连续管疲劳寿命的影响因素，建立经过粒子群算法（PSO）优化后的BP神经网络预测模型即PSO-BP模型，分析连续管直径、壁厚、弯曲半径以及内压与连续管疲劳寿命的关系并进行预测。研究结果表明：基于GRA分析结果可知，弯曲半径对连续管的疲劳寿命影响最大；通过与5种机器学习模型进行对比可知，GRA-PSO-BP模型具有较高的预测精度和较强的泛化性能。
Abstract:
In order to accurately predict the multi-axial fatigue life of coiled tubing and analyze the influencing factors of its fatigue life, a fatigue life prediction model of coiled tubing based on GRA-PSO-BP was established.Firstly,the gray correlation method (GRA) was used to analyze the factors affecting the fatigue life of CT.Then,a PSO-BP prediction model,namely the BP neural network optimized by particle swarm optimization (PSO),was established,and the relationship between the diameter,wall thickness,bending radius,internal pressure of CT and the fatigue life of CT was analyzed to carry out the related prediction.The results show that the bending radius has the greatest influence on the fatigue of CT by GRA analysis.The established GRA-PSO-BP model has high prediction accuracy and strong generalization performance,and the prediction results of this model are reliable and accurate compared with multiple models.

参考文献/References:

［1］周浩,刘少胡,管峰.内压、弯扭耦合载荷下连续管疲劳寿命评估［J］.高压物理学报,2019,(4):92-98. ZHOU Hao,LIU Shaohu,GUAN Feng.Fatigue life evaluation of coiledtubing under coupled load of internal pressure,bending and torsion［J］.Chinese Journal of High Pressure Physics,2019,(4):92-98.
［2］吴远灯,刘少胡,阳婷.动力钻具反扭矩对连续管疲劳寿命的影响［J］.塑性工程学报,2021,28(3):171-176. WU Yuandeng,LIU Shaohu,YANG Ting.Research on influence of power drill reverse torque on fatigue life of coiled tubing［J］.Journal of Plasticity Engineering,2021,28(3):171-176.
［3］袁发勇,马卫国.连续管水平井工程技术［M］.北京:科学出版社,2018.
［4］AVAKOV V A,FOSTER J C,SMITH E J.Coiled tubing life prediction［C］//Offshore Technology Conference.Houston,OTC,1993.
［5］NEWMAN K R.Coiled-tubing pressure and tension limits［C］//SPE Offshore Europe.Aberdeen,OnePetro,1991.
［6］NEWMAN K R,NEWBURN D A.Coiled-tubing-life modeling［C］//SPE Annual Technical Conference and Exhibition.Dallas,SPE,1991:SPE-22820-MS.
［7］TIPTON S M,MITCHELL M R,LANDGRAF R W.Multiaxial plasticity and fatigue life prediction in coiled tubing［J］.ASTM Special Technical Publication,1996,1:283-304.
［8］LIU S H,HAO Z,HONG Z,et al.Experimental and numerical simulation study on fatigue life of coiled tubing with typical defects［J］.Journal of Petroleum Science and Engineering,2021,198:108212.
［9］刘少胡,吴远灯,周浩,等.凹坑缺陷参数对连续管疲劳失效的影响研究［J］.石油机械,2020,48(4):119-124. LIU Shaohu,WU Yuandeng,ZHOU Hao,et al.The effect of pit defect parameters on fatigue failure of coiled tubing［J］.China Petroleum Machinery,2020,48(4):119-124.
［10］PENG S,XIAO J,TIAN Y,et al.Coiled tubing working life prediction based on BP algorithm of artificial neural network［C］//IADC SPE Asia Pacific Drilling Technology Conference and Exhibition.Tianjin,One Petro,2012.
［11］彭嵩,张全立,王宏伟,等.基于LMBP神经网络的连续油管疲劳寿命预测方法［J］.石油管材与仪器,2018,4(6):36-40. PENG Song,ZHANG Quanli,WANG Hongwei,et al.Coiled tubing fatigue life prediction methodbased on LMBP algorithm of neural network［J］.Petroleum Tubular Goods &Instruments,2018,4(6):36-40.
［12］于桂杰,赵崇,迟建伟,等.基于人工神经网络的连续油管疲劳寿命预测［J］.中国石油大学学报(自然科学版),2018,42(3):131-136. YU Guijie,ZHAO Chong,CHI Jianwei,et al.Fatigue life prediction of coiled tubings based on artificial neural network［J］.Journal of China University of Petroleum,2018,42(3):131-136.
［13］NASIRI S,KHOSRAVANI M R,WEINBERG K.Fracture mechanics and mechanical fault detection by artificial intelligence methods:a review［J］.Engineering Failure Analysis,2017,81:270-293.
［14］HAN Y L.Artificial neural network technology as a method to evaluate the fatigue life of weldments with welding defects［J］.International Journal of Pressure Vessels and Piping,1995,63(2):205-209.
［15］LUO H,LI Z,XIONG Q.Study on wind-induced fatigue of heliostat based on artificial neural network［J］.Journal of Wind Engineering and Industrial Aerodynamics,2021,217:104750.
［16］MORTAZAVI S N S,INCE A.An artificial neural network modeling approach for short and long fatigue crack propagation［J］.Computational Materials Science,2020,185:109962.
［17］马磊,陆卫东,魏国营.基于GASA-BP神经网络的煤层瓦斯含量预测方法研究［J］.中国安全生产科学技术,2022,18(8):59-65. MA Lei,LU Weidong，WEI Guoying.Study on prediction method of coal seam gas content based on GASA-BP neural network ［J］.Journal of Safety Science and Technology,2022,18(8):59-65.
［18］管志川,胜亚楠,许玉强,等.基于PSO优化BP神经网络的钻井动态风险评估方法［J］.中国安全生产科学技术,2017,13(8):5-11. GUAN Zhichuan,SHENG Yanan,XU Yuqiang,et al.Dynamic risk assessment method of drilling based on PSO optimized BP neural network ［J］.Journal of Safety Science and Technology,2017，13（8）：5-11.
［19］范勇,裴勇,杨广栋,等.基于改进PSO-BP神经网络的爆破振动速度峰值预测［J］.振动与冲击,2022,41(16):194-203，302. FAN Yong,PEI Yong,YANG Guangdong,et al.Prediction of blasting vibration velocity peak based on an improved PSO-BP neural network ［J］.2022,41(16):194-203，302.
［20］赵丽娟,张波,张雯.基于改进PSO-BP的采煤机截割部行星架疲劳寿命分析及预测［J］.机械强度,2021,43(4):977-981. ZHAO Lijuan,ZHANG Bo,ZHANG Wen.Fatigue life analysis and prediction of planet carrier in cutting part of shearer based on improved PSO-BP［J］.Journal of Mechanical Strength,2021,43(4):977-981.
［21］时强胜,张小俭,陈巍,等.基于灰色关联度分析-响应面法的橡胶软模端面抛磨表面粗糙度预测［J］.中国机械工程,2021,32(24):2967-2974. SHI Shengqiang,ZHANG Xiaojian,CHEN Wei,et al.Prediction of surface roughness of rubber soft die end face polishing based on GRA-RSM［J］.China Mechanical Engineering,2021,32(24):2967-2974.

相似文献/References:

[1]郑华林,张益维,刘少胡.水力压裂冲蚀磨损对连续管剩余寿命影响研究[J].中国安全生产科学技术,2016,12(7):110.[doi:10.11731/j.issn.1673-193x.2016.07.020]
　ZHENG Hualin,ZHANG Yiwei,LIU Shaohu.Study on effect of erosion wear to residual life of coiled tubing for hydraulic fracturing[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2016,12(6):110.[doi:10.11731/j.issn.1673-193x.2016.07.020]
[2]刘少胡,张益维,管锋,等.含缺陷连续管冲蚀规律研究[J].中国安全生产科学技术,2016,12(10):92.[doi:10.11731/j.issn.1673-193x.2016.10.015]
　LIU Shaohu,ZHANG Yiwei,GUAN Feng,et al.Study on erosion laws of coiled tubing containing defect[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2016,12(6):92.[doi:10.11731/j.issn.1673-193x.2016.10.015]
[3]吴晗,刘少胡,刘旭辉,等.焊肉位置对连续管冲蚀磨损规律研究[J].中国安全生产科学技术,2017,13(2):138.[doi:10.11731/j.issn.1673-193x.2017.02.024]
　WU Han,LIU Shaohu,LIU Xuhui,et al.Study on erosion wear laws of coiled tubing affected by weldment position[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2017,13(6):138.[doi:10.11731/j.issn.1673-193x.2017.02.024]
[4]刘少胡,李思行,冯定,等.连续管钻塞水平段铁屑运移机理研究[J].中国安全生产科学技术,2017,13(6):58.[doi:10.11731/j.issn.1673-193x.2017.06.009]
　LIU Shaohu,LI Sixing,FENG Ding,et al.Study on migration mechanism of iron filings in horizontal section during drilling plug with coiled tubing[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2017,13(6):58.[doi:10.11731/j.issn.1673-193x.2017.06.009]
[5]朱克勤,刘少胡.含缺陷连续管极限承载研究及安全分析*[J].中国安全生产科学技术,2024,20(4):172.[doi:10.11731/j.issn.1673-193x.2024.04.024]
　ZHU Keqin,LIU Shaohu.Study on ultimate bearing capacity and safety analysis of coiled tubing with defects[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(6):172.[doi:10.11731/j.issn.1673-193x.2024.04.024]

备注/Memo

备注/Memo:
收稿日期： 2022-11-23
* 基金项目： 国家自然科学基金项目（51974036，51604039）；湖北省高等学校优秀中青年科技创新团队计划项目（T2021035）
作者简介： 吴远灯，博士研究生，主要研究方向为连续管疲劳寿命评估。
通信作者： 刘少胡，博士，教授，主要研究方向为连续管疲劳寿命评估、管柱力学及井下工具研究。
更新日期/Last Update: 2023-07-09