STRUCTURAL HEALTH MONITORING BASED ON CORRELATION FUNCTION MATRIX AND CONVOLUTIONAL NEURAL NETWORK

WANG Hui; WANG Le; TIAN Xin-hai

doi:10.6052/j.issn.1000-4750.2022.01.0016

Volume 40 Issue 5

May 2023

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Engineering Mechanics > 2023 > 40(5): 217-227. > DOI: 10.6052/j.issn.1000-4750.2022.01.0016

WANG Hui, WANG Le, TIAN Xin-hai. STRUCTURAL HEALTH MONITORING BASED ON CORRELATION FUNCTION MATRIX AND CONVOLUTIONAL NEURAL NETWORK[J]. Engineering Mechanics, 2023, 40(5): 217-227. DOI: 10.6052/j.issn.1000-4750.2022.01.0016

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STRUCTURAL HEALTH MONITORING BASED ON CORRELATION FUNCTION MATRIX AND CONVOLUTIONAL NEURAL NETWORK

1.
School of Science, Chang’an University, Xi’an, Shaanxi 710064, China
2.
School of Aeronautics, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China

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Received Date: January 03, 2022
Revised Date: June 01, 2022
Available Online: June 16, 2022

Abstract

Abstract

The inner product matrix constructed by time domain vibration response under ambient excitation is a good structural characteristic parameter in structural health monitoring. In order to improve the identification accuracy of the structural health monitoring method using inner product matrix, more vibration response measurement points are often needed, which will directly affect the engineering practicability of the method. Based on the correlation analysis theory of time domain vibration responses, the inner product matrix is extended to the correlation function matrix to obtain more structural health characteristic information from a small number of vibration response measurement points, and the requirement of the number of measurement points for the structural health monitoring method will be reduced. Furthermore, combining with the excellent data feature extraction capability of convolutional neural network, a structural health monitoring method based on correlation function matrix and convolutional neural network is proposed with correlation function matrix as input and structural health status as output. The experimental results of the bolt loosening monitoring of a typical aeronautical stiffened panel show that the identification accuracy of the proposed method for bolt loose position can reach more than 99% by using only the time domain vibration responses of any two measurement points.

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References

[1]	FANG F, QIU L, YUAN S F, et al. Dynamic probability modeling-based aircraft structural health monitoring framework under time-varying conditions: Validation in an in-flight test simulated on ground [J]. Aerospace Science and Technology, 2019, 95: 105467. doi: 10.1016/j.ast.2019.105467
[2]	翁顺, 朱宏平. 基于有限元模型修正的土木结构损伤识别方法[J]. 工程力学, 2021, 38(3): 1 − 16. doi: 10.6052/j.issn.1000-4750.2020.06.ST02 WENG Shun, ZHU Hongping. Damage identification of civil structures based on finite element model updating [J]. Engineering Mechanics, 2021, 38(3): 1 − 16. (in Chinese) doi: 10.6052/j.issn.1000-4750.2020.06.ST02
[3]	侯博文, 李佳静, 高亮, 等. 基于脉冲激励下钢轨振动响应的扣件失效识别方法[J]. 工程力学, 2021, 38(2): 122 − 133. doi: 10.6052/j.issn.1000-4750.2020.04.0210 HOU Bowen, LI Jiajing, GAO Liang, et al. Failure identification method of fastening system based on rail response under pulse excitation [J]. Engineering Mechanics, 2021, 38(2): 122 − 133. (in Chinese) doi: 10.6052/j.issn.1000-4750.2020.04.0210
[4]	YANG Z C, WANG L, WANG H, et al. Damage detection in composite structures using vibration response under stochastic excitation [J]. Journal of Sound and Vibration, 2009, 325(4-5): 755 − 768. doi: 10.1016/j.jsv.2009.03.040
[5]	BAO Y Q, CHEN Z C, WEI S Y, 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
[6]	韩小雷, 冯润平, 季静, 等. 基于深度学习的RC梁集中塑性铰模型参数研究[J]. 工程力学, 2021, 38(11): 160 − 169. doi: 10.6052/j.issn.1000-4750.2020.11.0793 HAN Xiaolei, FENG Runping, JI Jing, et al. Research on parameters of the RC beam lumped plastic hinge model based on deep learning [J]. Engineering Mechanics, 2021, 38(11): 160 − 169. (in Chinese) doi: 10.6052/j.issn.1000-4750.2020.11.0793
[7]	陈海昕, 邓凯文, 李润泽. 机器学习技术在气动优化中的应用[J]. 航空学报, 2019, 40(1): 522480. CHEN Haixin, DENG Kaiwen, LI Runze. Utilization of machine learning technology in aerodynamic optimization [J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(1): 522480. (in Chinese)
[8]	战庆亮, 葛耀君, 白春锦. 流场特征识别的无量纲时程深度学习方法[J]. 工程力学, 2023, 40(2): 17 − 24. doi: 10.6052/j.issn.1000-4750.2021.08.0638 ZHAN Qingliang, GE Yaojun, BAI Chunjin. Deep learning method for flow field feature recognition based on dimensionless time history [J]. Engineering Mechanics, 2023, 40(2): 17 − 24. (in Chinese) doi: 10.6052/j.issn.1000-4750.2021.08.0638
[9]	何定桥, 王鹏军, 杨军. 深度神经网络在EMD虚假分量识别中的应用[J]. 工程力学, 2021, 38(增刊): 195 − 201. doi: 10.6052/j.issn.1000-4750.2020.04.S036 HE Dingqiao, WANG Pengjun, YANG Jun. Application of deep neural networks in EMD false component identification [J]. Engineering Mechanics, 2021, 38(Suppl): 195 − 201. (in Chinese) doi: 10.6052/j.issn.1000-4750.2020.04.S036
[10]	ZHAO R, YAN R Q, CHEN Z H, et al. Deep learning and its applications to machine health monitoring [J]. Mechanical Systems and Signal Processing, 2019, 115(1): 213 − 237.
[11]	杨娜, 张翀, 李天昊. 基于无人机与计算机视觉的中国古建筑木结构裂缝监测系统设计[J]. 工程力学, 2021, 38(3): 27 − 39. doi: 10.6052/j.issn.1000-4750.2020.04.0263 YANG Na, ZHANG Chong, LI Tianhao. Design of crack monitoring system for Chinese ancient wooden buildings based on UAV and CV [J]. Engineering Mechanics, 2021, 38(3): 27 − 39. (in Chinese) doi: 10.6052/j.issn.1000-4750.2020.04.0263
[12]	RUBIO J J, KASHIWA T, LAITEERAPONG T, et al. Multi-class structural damage segmentation using fully convolutional networks [J]. Computers in Industry, 2019, 112: 103121. doi: 10.1016/j.compind.2019.08.002
[13]	BECKMAN G H, POLYZOIS D, CHA Y J. Deep learning-based automatic volumetric damage quantification using depth camera [J]. Automation in Construction, 2019, 99: 114 − 124. doi: 10.1016/j.autcon.2018.12.006
[14]	XU Y, WEI S Y, BAO Y Q, et al. Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network [J]. Structural Control and Health Monitoring, 2019, 26: e2313. doi: 10.1002/stc.2313
[15]	MEI Q P, GÜL M, AZIM M R. Densely connected deep neural network considering connectivity of pixels for automatic crack detection [J]. Automation in Construction, 2020, 110: 103018. doi: 10.1016/j.autcon.2019.103018
[16]	JANG K, KIM N, AN Y K. Deep learning-based autonomous concrete crack evaluation through hybrid image scanning [J]. Structural Health Monitoring, 2019, 18: 1722 − 1737. doi: 10.1177/1475921718821719
[17]	DENG W L, MOU Y L, KASHIWA T, et al. Vision based pixel-level bridge structural damage detection using a link ASPP network [J]. Automation in Construction, 2020, 110: 102973. doi: 10.1016/j.autcon.2019.102973
[18]	CHEN L T, XU G H, ZHANG S C, et al. Health indicator construction of machinery based on end-to-end trainable convolution recurrent neural networks [J]. Journal of Manufacturing Systems, 2020, 54: 1 − 11. doi: 10.1016/j.jmsy.2019.11.008
[19]	MARTIN G S, DROGUETT E L, MERUANEM V, et al. Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis [J]. Structural Health Monitoring, 2018, 18: 1092 − 1128.
[20]	ABDELJABER O, AVCI O, KIRANYAZ S, et al. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks [J]. Journal of Sound and Vibration, 2017, 388: 154 − 170. doi: 10.1016/j.jsv.2016.10.043
[21]	ABDELJABER O, AVCI O, KIRANYAZ M S, et al. 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data [J]. Neurocomputing, 2018, 275: 1308 − 1317. doi: 10.1016/j.neucom.2017.09.069
[22]	ZHANG Y Q, MIYAMORI Y, MIKAMI S, et al. Vibration‐based structural state identification by a 1‐dimensional convolutional neural network [J]. Computer‐Aided Civil and Infrastructure Engineering, 2019, 34: 822 − 839. doi: 10.1111/mice.12447
[23]	LIU J X, CHEN S H, BERGES M, et al. Diagnosis algorithms for indirect structural health monitoring of a bridge model via dimensionality reduction [J]. Mechanical Systems and Signal Processing, 2020, 1368: 106454.
[24]	ZHANG X, WANG H, HOU B R, et al. 1D-CNN-based damage identification method based on piezoelectric impedance using adjustable inductive shunt circuitry for data enrichment [J]. Structural Health Monitoring, 2022, doi: 10.1177/14759217211049720
[25]	LIN M X, GOU S J, HE S, et al. Structure health monitoring of a composite wing based on flight load and strain data using deep learning method [J]. Composite Structures, 2022, 286: 115305. doi: 10.1016/j.compstruct.2022.115305
[26]	SEVENTEKIDIS P, GIAGOPOULOS D. A combined finite element and hierarchical deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure [J]. Mechanical Systems and Signal Processing, 2021, 157: 107735. doi: 10.1016/j.ymssp.2021.107735
[27]	WANG L, YANG Z C, WATERS T P. Structural damage detection using cross correlation functions of vibration response [J]. Journal of Sound and Vibration, 2010, 329(24): 5070 − 5086. doi: 10.1016/j.jsv.2010.06.020
[28]	王慧, 郭晨林, 王乐, 等. 基于内积矩阵及深度学习的结构健康监测研究[J]. 工程力学, 2022, 39(2): 14 − 22. doi: 10.6052/j.issn.1000-4750.2020.12.0935 WANG Hui, GUO Chenlin, WANG Le, et al. Structural health monitoring based inner product matrix and deep learning [J]. Engineering Mechanics, 2022, 39(2): 14 − 22. (in Chinese) doi: 10.6052/j.issn.1000-4750.2020.12.0935
[29]	张敏照, 王乐, 田鑫海. 基于内积矩阵及卷积自编码器的螺栓松动状态监测[J]. 工程力学, 2022, 39(12): 222 − 231. doi: 10.6052/j.issn.1000-4750.2021.07.0583 ZHANG Minzhao, WANG Le, TIAN Xinhai. Bolt loosening state monitoring based on inner product matrix and convolutional autoencoder [J]. Engineering Mechanics, 2022, 39(12): 222 − 231. (in Chinese) doi: 10.6052/j.issn.1000-4750.2021.07.0583
[30]	王慧, 王乐, 田润泽. 基于时域响应相关性分析及数据融合的结构损伤检测研究[J]. 工程力学, 2020, 37(9): 30 − 37, 111. doi: 10.6052/j.issn.1000-4750.2019.10.0588 WANG Hui, WANG Le, TIAN Runze. Structural damage detection using correlation functions of time domain vibration responses and data fusion [J]. Engineering Mechanics, 2020, 37(9): 30 − 37, 111. (in Chinese) doi: 10.6052/j.issn.1000-4750.2019.10.0588
[31]	JAMES III G H, CARNE T G, LAUFFER J P, et al. The natural excitation technique (NExT) for modal parameter extraction from operating structures [J]. Modal Analysis: The International Journal of Analytical and Experimental Modal Analysis, 1995, 10(4): 260 − 277.
[32]	CHA Y J, CHOI W, BÜYÜKÖZTÜRK O. Deep learning - based crack damage detection using convolutional neural networks [J]. Computer - Aided Civil and Infrastructure Engineering, 2017, 32(5): 361 − 378. doi: 10.1111/mice.12263
[33]	CHANG Y H, CHEN J L, QU C, et al. Intelligent fault diagnosis of wind turbines via a deep learning network using parallel convolution layers with multi-scale kernels [J]. Renewable Energy, 2020, 153(1): 205 − 213.
[34]	唐和生, 何展朋, 廖洋洋, 等. 基于物理驱动深度迁移学习的薄板力学正反问题 [J]. 工程力学. doi: 10.6052/j.issn.1000-4750.2021.12.0977. TANG Hesheng, HE Zhanpeng, LIAO Yangyang, et al. Forward and inverse problems of thin plate mechanics based on physics-informed deep transfer learning [J]. Engineering Mechanics. doi: 10.6052/j.issn.1000-4750.2021.12.0977. (in Chinese)

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STRUCTURAL HEALTH MONITORING BASED ON CORRELATION FUNCTION MATRIX AND CONVOLUTIONAL NEURAL NETWORK

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