A Damage Identification Method for Transmission Towers Based on Substructure Model Reduction and Data Driving

DENG Mao; YAN Bo; GAO Yingbo; YANG Hanxu; LÜ Zhongbin; ZHANG Bo; LIU Guanghui

doi:10.21656/1000-0887.450052

Volume 45 Issue 7

Jul. 2024

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Article Navigation > Applied Mathematics and Mechanics > 2024 > 45(7): 850-863

DENG Mao, YAN Bo, GAO Yingbo, YANG Hanxu, LÜ Zhongbin, ZHANG Bo, LIU Guanghui. A Damage Identification Method for Transmission Towers Based on Substructure Model Reduction and Data Driving[J]. Applied Mathematics and Mechanics, 2024, 45(7): 850-863. doi: 10.21656/1000-0887.450052

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A Damage Identification Method for Transmission Towers Based on Substructure Model Reduction and Data Driving

doi: 10.21656/1000-0887.450052

1.
College of Aerospace Engineering, Chongqing University, Chongqing 400044, P.R.China
2.
Henan Electric Power Research Institute, Zhengzhou 450052, P.R.China

Received Date: 2024-02-29
Rev Recd Date: 2024-03-19
Publish Date: 2024-07-01

Abstract

Abstract

A damage regression identification method for large and complex transmission tower structures subjected to static loads was proposed based on the substructure model reduction and data-driven method. According to the structural features of the transmission tower and its deformation under self-weight and ice loading, the full finite element model for the tower was reduced by means of the sub-structure method, the possible damage modes were predicted and the damage indexes defined. The substructure modeling method was used to reduce the orders of the structure with different damage states, and the order reduction model library was established. The calibration load was determined based on the loading characteristics of the tower, and the strain sensor layout was designed according to the deformation and failure modes. The deformations of all the reduced-order models under calibration loads were numerically simulated with the finite element method, and a dataset was then created. With the data measured by the strain sensors as input and the damage indexes as output, a damage regression identification model was built by the BP neural network algorithm. With the identification model, the damage locations can be recognized and the damage indexes can be quantified. This work lays a foundation for real-time health monitoring of transmission tower structures.
- substructure model reduction,
- transmission tower,
- data-driven method,
- damage identification,
- regression model

(Contributed by YAN Bo, M.AMM Editorial Board)

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References(27)

References

[1]	李惠, 鲍跃全, 李顺龙, 等. 结构健康监测数据科学与工程[J]. 工程力学, 2015, 32 (8): 1-7. https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX201508002.htm LI Hui, BAO Yuequan, LI Shunlong, et al. Data science and engineering for structural health monitoring[J]. Engineering Mechanics, 2015, 32 (8): 1-7. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX201508002.htm
[2]	BAO Y, CHEN Z, WEI S, et al. The state of the art of data science and engineering in structural health monitoring[J]. Engineering, 2019, 5 (2): 234-242. doi: 10.1016/j.eng.2018.11.027
[3]	郑栋梁, 李中付, 华宏星. 结构早期损伤识别技术的现状和发展趋势[J]. 振动与冲击, 2002, 21 (2): 1-6. doi: 10.3969/j.issn.1000-3835.2002.02.001 ZHENG Dongliang, LI Zhongfu, HUA Hongxing. A summary review of structural initial damage identification methods[J]. Journal of Vibration and Shock, 2002, 21 (2): 1-6. (in Chinese) doi: 10.3969/j.issn.1000-3835.2002.02.001
[4]	LI H, SPENCER B F, BAO Y. Machine learning paradigm for structural health monitoring[J]. Structural Health Monitoring, 2021, 20 (4): 1353-1372. doi: 10.1177/1475921720972416
[5]	FARRAR C R, WORDEN K. Structural Health Monitoring: a Machine Learning Perspective[M]. John Wiley Sons, 2012.
[6]	YING Y, GARRETT J H, OPPENHEIM I J, et al. Toward data-driven structural health monitoring: application of machine learning and signal processing to damage detection[J]. Journal of Computing in Civil Engineering, 2013, 27 (6): 667-680. doi: 10.1061/(ASCE)CP.1943-5487.0000258
[7]	MANSON G, WORDEN K, ALLMAN D. Experimental validation of a structural health monitoring methodology, part Ⅰ: damage location on an aircraft wing[J]. Journal of Sound and Vibration, 2002, 259 (2): 323-343.
[8]	MANSON G, PAPATHEOU E, WORDEN K. Genetic optimization of a neural network damage diagnostic[J]. Aeronautical Journal, 2008, 112 (1131): 267-274. doi: 10.1017/S0001924000002219
[9]	HAKIM S J S, RAZAK H A, RAVANFAR S A. Fault diagnosis on beam-like structures from modal parameters using artificial neural networks[J]. Measurement, 2015, 76 : 45-61. doi: 10.1016/j.measurement.2015.08.021
[10]	SONY S, DUNPHY K, SADHU A, et al. A systematic review of convolutional neural network-based structural condition assessment techniques[J]. Engineering Structures, 2021, 226 : 111347. doi: 10.1016/j.engstruct.2020.111347
[11]	KHODABANDEHLOU H, PEKCAN G, FADALI S M. Vibration-based structural condition assessment using convolution neural networks[J]. Structural Control and Health Monitoring, 2019, 26 (2): e2308.
[12]	黄斌, 纵瑞芳, 杨涛. 基于静力测量数据的随机梁式结构损伤识别[J]. 计算力学学报, 2013, 30 (2): 180-186. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJG201302003.htm HUANG Bin, ZONG Ruifang, YANG Tao. Damage identification of random beam structures based on static measurement data[J]. Chinese Journal of Computational Mechanics, 2013, 30 (2): 180-186. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJG201302003.htm
[13]	LAM H, YANG J. Bayesian structural damage detection of steel towers using measured modal parameters[J]. Earthquakes and Structures, 2015, 8 (4): 935-956. doi: 10.12989/eas.2015.8.4.935
[14]	QU W, SONG W, XIA Y, et al. Two-step method for instability damage detection in tower body of transmission structures[J]. Advances in Structural Engineering, 2013, 16 (1): 219-232. doi: 10.1260/1369-4332.16.1.219
[15]	KARAMI-MOHAMMADI R, MIRTAHERI M, SALKHORDEH M, et al. Vibration anatomy and damage detection in power transmission towers with limited sensors[J]. Sensors, 2020, 20 (6): 1731. doi: 10.3390/s20061731
[16]	滕辉, 康帅, 尹俊红, 等. 基于不同机器学习模型的框架结构损伤识别对比研究[J]. 四川建筑科学研究, 2023, 49 (3): 44-53. https://www.cnki.com.cn/Article/CJFDTOTAL-ACZJ202303005.htm TENG Hui, KANG Shuai, YIN Junhong, et al. Comparative study of frame structure damage identification based on different machine learning models[J]. Sichuan Building Science, 2023, 49 (3): 44-53. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ACZJ202303005.htm
[17]	KOUCHAKI M, SALKHORDEH M, MASHAYEKHI M, et al. Damage detection in power transmission towers using machine learning algorithms[J]. Structures, 2023, 56 : 104980. doi: 10.1016/j.istruc.2023.104980
[18]	魏佳恒, 郭惠勇. 基于贝叶斯优化BiLSTM模型的输电塔损伤识别[J]. 振动与冲击, 2023, 42 (1): 238-248. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202301028.htm WEI Jiaheng, GUO Huiyong. Damage identification of transmission tower based on BO-BiLSTM model[J]. Journal of Vibration and Shock, 2023, 42 (1): 238-248. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202301028.htm
[19]	QUARTERONI A, MANZONI A, NEGRI F. Reduced Basis Methods for Partial Differential Equations: an Introduction[M]. Springer, 2015.
[20]	VALLAGHÉ S, HUYNH P, KNEZEVIC D J, et al. Component-based reduced basis for parametrized symmetric eigenproblems[J]. Advanced Modeling and Simulation in Engineering Sciences, 2015, 2 (1): 1-30. doi: 10.1186/s40323-014-0017-1
[21]	张珺, 李立州, 原梅妮. 径向基函数参数化翼型的气动力降阶模型优化[J]. 应用数学和力学, 2019, 40 (3): 250-258. doi: 10.21656/1000-0887.390187 ZHANG Jun, LI Lizhou, YUAN Meini. Optimization of RBF parameterized airfoils with the aerodynamic ROM[J]. Applied Mathematics and Mechanics, 2019, 40 (3): 250-258. (in Chinese) doi: 10.21656/1000-0887.390187
[22]	赖学方, 王晓龙, 聂玉峰. 基于Mori-Zwanzig格式和偏最小二乘的非线性模型降阶[J]. 应用数学和力学, 2021, 42 (6): 551-561. doi: 10.21656/1000-0887.410230 LAI Xuefang, WANG Xiaolong, NIE Yufeng. Nonlinear model reduction based on the Mori-Zwanzig scheme and partial least squares[J]. Applied Mathematics and Mechanics, 2021, 42 (6): 551-561. (in Chinese) doi: 10.21656/1000-0887.410230
[23]	罗振东, 张博. Sobolev方程基于POD的降阶外推差分算法[J]. 应用数学和力学, 2016, 37 (1): 107-116. doi: 10.3879/j.issn.1000-0887.2016.01.009 LUO Zhendong, ZHAO Bo. A reduced-order extrapolation finite difference algorithm based on the POD method for Sobolev equations[J]. Applied Mathematics and Mechanics, 2016, 37 (1): 107-116. (in Chinese) doi: 10.3879/j.issn.1000-0887.2016.01.009
[24]	GUYAN R J. Reduction of stiffness and mass matrices[J]. AIAA Journal, 1965, 3 (2): 380. doi: 10.2514/3.2874
[25]	BEATTIE C, GUGERCIN S. Model reduction by rational interpolation[M]//Model Reduction and Approximation: Theory and Algorithms. Philadelphia: Society for Industrial and Applied Mathematics, 2017.
[26]	HURTY W. Dynamic analysis of structural systems using component modes[J]. AIAA Journal, 1965, 3 (4): 678-685. doi: 10.2514/3.2947
[27]	刘营, 李鸿光, 李韵, 等. 基于子结构的参数化模型降阶方法[J]. 振动与冲击, 2020, 39 (16): 148-154. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202016021.htm LIU Ying, LI Hongguang, LI Yun, et al. A component-based parametric model order reduction method[J]. Journal of Vibration and Shock, 2020, 39 (16): 148-154. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202016021.htm