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

被引：0
|
作者：
Huo J.-Z. [1 ]
Ge L.-H. [1 ]
Zhang Z.-G. [1 ]
Li G.-R. [1 ]
机构：
[1] School of Mechanical Engineering, Dalian University of Technology, Dalian
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2023年 / 44卷 / 10期
关键词：
BP neural network; design of experiments; feature substructures; strain reconstruction; TBM cutterhead;
D O I：
10.12068/j.issn.1005-3026.2023.10.012
中图分类号：
学科分类号：
摘要：
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. © 2023 Northeastern University. All rights reserved.
引用
收藏
页码：1455 / 1463
页数：8
相关论文
共 15 条
  • [1] Yang Bo-wen, Huo Jun-zhou, Zhang Wei, Et al., Research on real-time overload prediction method of in-service structures, Journal of Northeastern University(Natural Science), 43, 4, pp. 541-550, (2022)
  • [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, 120, pp. 584-607, (2019)
  • [3] 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, 65, 6, pp. 107-114, (2022)
  • [4] 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, 38, 2, pp. 360-364, (2018)
  • [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, 23, 3, pp. 1028-1037, (2018)
  • [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, 68, 7, pp. 84-104, (2016)
  • [7] Ao Chun-yan, Qiao Bai-jie, Liu Mei-ru, Et al., Dynamic strain reconstruction method of rotating blades based on no-contact measurement, Journal of Aerospace Power, 35, 3, pp. 569-580, (2020)
  • [8] Feng Di, Liang Bing, Liu Kun, Deformation reconstruction method of elevator assembly based on strain monitoring, Metrology & Measurement Technology, 40, 1, pp. 12-18, (2020)
  • [9] Hu Ming-yue, Wu Shao-qing, Dong E-liang, Real-time displacement field reconstruction of typical thermal protection structures based on measured strain, Journal of Southeast University(Natural Science Edition), 51, 4, pp. 664-671, (2021)
  • [10] 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, 56, 20, pp. 1-11, (2020)
12