基于BP神经网络的TBM刀盘关键位置应变重构

doi:10.12068/j.issn.1005-3026.2023.10.012

东北大学学报（自然科学版） ›› 2023, Vol. 44 ›› Issue (10): 1455-1463.DOI: 10.12068/j.issn.1005-3026.2023.10.012

基于BP神经网络的TBM刀盘关键位置应变重构

霍军周，葛利涵，张占葛，李高瑞

(大连理工大学机械工程学院，辽宁大连116024)

发布日期:2023-10-27
通讯作者: 霍军周
作者简介:霍军周(1979-)，男，山西运城人，大连理工大学教授，博士生导师.
基金资助:
国家重点研发计划项目(2018YFB1306700)；辽宁省自然科学基金资助项目(2021-MS-300)；辽宁省教育厅科研基金资助项目(LJKZ0526).

Strain Reconstruction of TBM Cutterhead at Key Positions Based on BP Neural Network

HUO Jun-zhou， GE Li-han， ZHANG Zhan-ge， LI Gao-rui

School of Mechanical Engineering， Dalian University of Technology， Dalian 116024， China.

Published:2023-10-27
Contact: ZHANG Zhan-ge
About author:-
Supported by:
-

摘要/Abstract

摘要： 为解决恶劣工况下全断面硬岩隧道掘进机(tunnel boring machine，TBM)关键主承力结构应变监测难、监测不准的难题，提出了一种基于BP神经网络和有限元分析的TBM刀盘关键位置应变重构方法.首先，通过静、动力学有限元分析确定了TBM刀盘关键位置，并提取了刀盘典型易损特征子结构.其次，基于有限元技术和实验设计(design of experiments，DOE)方法，分别进行了标准件和特征子结构多载荷下静力学有限元分析，并构建了载荷-应变数据库.最后，运用BP神经网络建立了标准样件和刀盘特征子结构的应变重构模型，并进行了标准样件的实验验证.结果表明，重构应变的平均误差为10%，验证了方法的可行性，为TBM刀盘复杂结构的应变重构提供了一种可行的方法.

关键词: TBM刀盘；特征子结构；应变重构；BP神经网络；实验设计

Abstract: To solve the problem of difficult and inaccurate strain monitoring of the key main load-bearing structures of the full face hard rock tunnel boring machine(TBM)under harsh working conditions， a strain reconstruction method for key positions of the TBM cutterhead based on BP neural network and finite element analysis was proposed. Firstly， the key vulnerable positions of the TBM cutterhead were determined through static and dynamic finite element analysis， and the typical vulnerable feature substructures of the cutterhead were extracted. Secondly， the static finite element analysis of standard parts and feature substructures under multiple loads was carried out based on the design of experiments(DOE)， and the load-strain database was constructed.Finally， a strain reconstruction model for the standard sample and cutterhead feature substructure was established using BP neural network， and experimental verification of the standard samples were conducted.The results showed that the average error of the reconstructed strains is 10%， which verifies the feasibility of the method and provides a feasible method for strain reconstruction of complex TBM cutterhead.

Key words: TBM cutterhead; feature substructures; strain reconstruction; BP neural network; design of experiments

中图分类号:

U467.4

霍军周，葛利涵，张占葛，李高瑞. 基于BP神经网络的TBM刀盘关键位置应变重构[J]. 东北大学学报（自然科学版）, 2023, 44(10): 1455-1463.

HUO Jun-zhou， GE Li-han， ZHANG Zhan-ge， LI Gao-rui. Strain Reconstruction of TBM Cutterhead at Key Positions Based on BP Neural Network[J]. Journal of Northeastern University(Natural Science), 2023, 44(10): 1455-1463.

参考文献

[1]杨博文，霍军周，张伟，等.服役结构超前载荷实时预测方法的研究［J］.东北大学学报(自然科学版)，2022，43(4):541-550.(Yang Bo-wen，Huo Jun-zhou，Zhang Wei，et al.Research on real-time overload prediction method of in-service structures［J］.Journal of Northeastern University(Natural Science)，2022，43(4):541-550.)
[2]Colombo L，Sbarufatti C，Giglio M.Definition of a load adaptive baseline by inverse finite element method for structural damage identification［J］.Mechanical Systems and Signal Processing，2019，120:584-607.
[3]付书山，孙广开，何彦霖，等.基于逆有限元的机翼蒙皮变形监测方法仿真研究［J］.航空制造技术，2022，65(6):107-114.(Fu Shu-shan，Sun Guang-kai，He Yan-lin，et al.Simulation study on wing skin deformation monitoring based on inverse finite element method［J］.Aerospace Manufacturing Technology，2022，65(6):107-114.)
[4]潘兴琳，保宏，张旭东.基于模糊的框架变形重构的应变测量修正［J］.振动·测试与诊断，2018，38(2):360-364，423.(Pan Xing-lin，Bao Hong，Zhang Xu-dong.Updating measurement strain for the elastic deformation of 3-d frame structure using fuzzy method［J］.Journal of Vibration，Measurement &.Diagnosis，2018，38(2):360-364，423.)
[5]Yu M，Guo J J，Lee K M.A modal expansion method for displacement and strain field reconstruction of a thin-wall component during machining［J］.IEEE/ASME Transactions on Mechatronics，2018，23(3):1028-1037.
[6]Alexandros I，Rasoul S，Wout W，et al.A modal decomposition and expansion approach for prediction of dynamic responses on a monopile offshore wind turbine using a limited number of vibration sensors［J］.Mechanical Systems and Signal Processing，2016，68/69(7):84-104.
[7]敖春燕，乔百杰，刘美茹，等.基于非接触式测量的旋转叶片动应变重构方法［J］.航空动力学报，2020，35(3):569-580.(Ao Chun-yan，Qiao Bai-jie，Liu Mei-ru， et al.Dynamic strain reconstruction method of rotating blades based on no-contact measurement［J］.Journal of Aerospace Power，2020，35(3):569-580.)
[8]冯荻，梁冰，刘坤.基于应变监测的升降舵壁板变形重构方法［J］.计测技术，2020，40(1):12-18.(Feng Di，Liang Bing，Liu Kun.Deformation reconstruction method of elevator assembly based on strain monitoring［J］.Metrology & Measurement Technology，2020，40(1):12-18.)
[9]胡明月，吴邵庆，董萼良.基于实测应变的典型热防护结构位移场实时重构［J］.东南大学学报(自然科学版)，2021，51(4):664-671.(Hu Ming-yue，Wu Shao-qing，Dong E-liang.Real-time displacement field reconstruction of typical thermal protection structures based on measured strain［J］.Journal of Southeast University(Natural Science Edition)，2021，51(4):664-671.)
[10]赵飞飞，曹开拓，保宏，等.Timoshenko梁的变形场重构及传感器位置优化［J］.机械工程学报，2020，56(20):1-11.(Zhao Fei-fei，Cao Kai-tuo，Bao Hong，et al.Deformation field reconstruction of Timoshenko beam and optimization of sensor placement［J］.Journal of Mechanical Engineering，2020，56(20):1-11.)
[11]Esposito M，Gherlone M.Composite wing box deformed-shape reconstruction based on measured strains:optimization and comparison of existing approaches［J］.Aerospace Science and Technology，2020，99:14-21.
[12]Wang J，Wang Z F，Sui Q M，et al.Study of FBG strain distribution reconstruction based on improved genetic algorithm dual constraint［J］.Chinese Journal of Lasers，2012，39:12-22.
[13]Huo J Z，Xu Z H，Meng Z C，et al.Coupled modeling and dynamic characteristics of TBM cutter head system under uncertain factors［J］.Mechanical Systems and Signal Processing，2020，140:106-114.
[14]Huo J Z，Wu H Y，Sun W，et al.Electromechanical coupling dynamics of TBM main drive system［J］.Nonlinear Dynamic，2017，90:2687-2710.
[15]曹岩.全断面掘进机滚刀数值模拟及布置规律优化研究［D］.沈阳:东北大学，2011.(Cao Yan.Numerical simulation and cutter layout design study of full-face tunnel boring machine［D］.Shenyang:Northeastern University，2011.)

基于BP神经网络的TBM刀盘关键位置应变重构

Strain Reconstruction of TBM Cutterhead at Key Positions Based on BP Neural Network

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