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[1]石彤 刘啸奔 张琳 王军 李睿 谢婷 冯庆善 黄启玉.管道IMU弯曲应变解算算法优化与全尺寸试验验证[J].油气储运,2024,43(09):1-10.
　SHI Tong,LIU Xiaoben,ZHANG Lin,et al.Optimization of IMU-Based Bending Strain Calculation Algorithm and Full-Scale Experimental Validation for Pipelines[J].Oil & Gas Storage and Transportation,2024,43(09):1-10.
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管道IMU弯曲应变解算算法优化与全尺寸试验验证

《油气储运》[ISSN:1000-8241/CN:13-1093/TE] 卷: 43 期数: 2024年09期 页码: 1-10 栏目: 出版日期: 2024-09-25
Title:
Optimization of IMU-Based Bending Strain Calculation Algorithm and Full-Scale Experimental Validation for Pipelines
作者:
石彤  刘啸奔  张琳  王军  李睿  谢婷  冯庆善  黄启玉
Author(s):
SHI Tong; LIU Xiaoben; ZHANG Lin; WANG Jun; LI Rui; FENG Qingshan; HUANG Qiyu
关键词:
惯性导航; 真实弯曲应变; 仿真重构; 全尺寸试验; 误差分析; 解算算法
Keywords:
Inertial Mapping Unit;  True bending strain;  Simulation reconstruction;  Full-scale test;  Error analysis;  Solving algorithm
分类号:
TE88
文献标志码:
A
摘要:
基于惯性导航（Inertial Mapping Unit，IMU）内检测器的管道弯曲应变识别技术，目前已被国内外广泛应用。【目的】IMU通过陀螺仪获取的俯仰角、航向角等进行解算并获取管道全线弯曲应变，但由于内检测器实际运行时存在因自身尺寸等原因造成的检测轨迹线偏移，导致IMU检测弯曲应变与管道真实弯曲应变之间存在一定误差。【方法】基于实际内检测器尺寸建立无异常振动等干扰的IMU内检测器仿真模型，构建管径508 mm管道不同弯曲应变条件下的仿真数据库，提出基于神经网络-决策树（ANN-ExtraTree）深度学习模型的解算优化算法，进行误差分析与弯曲应变解算算法研究。开展全尺寸管道弯曲应变牵拉试验，验证解算优化算法的可行性与准确性。【结果】研究结果表明：随着管道弯曲应变增大，IMU检测弯曲应变与管道真实弯曲应变之间的误差随之增大。解算后弯曲应变数据的误差协方差、均方误差、误差标准差分别由0.007 0、0.004 9、0.070 2降低至0.001 2、0.001 6、0.012 6，而表示相关性的决定系数由0.443升高至0.981。采用全尺寸牵拉试验验证该算法，弯曲应变与应变片检测真弯曲应变之间误差分别降低了79.6%、79.4%、76.0%。【结论】通过有限元模拟与全尺寸牵拉试验数据验证了所提出解算优化算法的可行性与准确性，为基于IMU内检测的管道真弯曲应变获取提供技术支撑与指导。
Abstract:
The technology for identifying bending strain of pipelines based on the Inertial Mapping Unit (IMU) inertial Mapping Unit (IMU) detector has been widely used at home and abroad.[Objective] The IMU calculates the bending strain of the pipeline through the pitch Angle and heading Angle obtained by the gyroscope and obtains the bending strain of the pipeline. However, due to the deviation of the detection track line caused by the internal detector's own size and other reasons during actual operation, there is a certain error between the detected strain of the IMU and the real strain of the pipeline.[Methods] In this paper, a simulation model of IMU internal detector without abnormal vibration and other interference is established based on the actual internal detector size, and the detection track database of the inertial navigation internal detector for different pipeline bending strains is constructed, and the error analysis and bending strain solution algorithm are studied. Finally, the feasibility and accuracy of the solution optimization algorithm are verified based on the full-scale pipeline bending strain pulling test.[Results] The results show that with the increase of bending strain, the error between the measured strain and the real strain of the pipeline increases. The simulation database of 508mm diameter pipeline under different bending strain conditions was established, and the solution optimization algorithm based on the ANN-ExtraTree deep learning model was proposed. The inertial navigation detection simulation database was used as the test set and training set. It is obtained that the,error covariance mean square error and error standard deviation of the bending strain data after calculation by the previous algorithm are reduced from 0.0049, 0.00703 and 0.0702 to 0.0016 ,0.0012 and 0.0126, respectively, while the coefficient of determination indicating the correlation with true strain is increased from 0.443 to 0.981. The results show that the error between the bending strain and the true strain measured by the strain gauge is reduced by 79.6%, 79.4% and 76.0%, respectively.[Conclusion] The feasibility and accuracy of the proposed solution optimization algorithm are verified based on the finite element simulation and full-scale pulling test data. This method provides technical support and guidance for pipeline true strain acquisition based on IMU

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更新日期/Last Update: 2024-07-03