Damage identification of frame structure based on CNN model with&nbsp;dual-attention mechanism and improved Inception module#br#

PDF(2074 KB)

Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (23) : 321-328.

Damage identification of frame structure based on CNN model with dual-attention mechanism and improved Inception module#br#

LIU Jingliang1, L Yulin1, ZHENG Wenting2, LIAO Feiyu1, CHEN Zongyan3

Author information +

History +

Abstract

In order to address the issues of enormous numbers of parameters and long training time in traditional deep learning methods, a new structural damage identification method based on convolutional neural network (CNN) model with dual attention mechanism and improved Inception module is proposed. Firstly, the vibration response signals from engineering structures are firstly transformed into two-dimensional time-frequency spectrograms using local maximum synchrosqueezing transform, and then the obtained time-frequency spectrograms are used as inputs of CNN. Secondly, a two-dimensional CNN is established based on an improved Inception module. Finally, a dual attention mechanism is introduced to explore damage features with higher relevance, leading to a successful identification of structural damage location and severity. The effectiveness of the proposed method is validated by two numerical simulation cases of IASC-ASCE SHM Benchmark Phase I model and the Qatar University Grandstand Simulator dataset. The results demonstrate that the proposed method not only reduces model parameters and accelerates model convergence, but also has high accuracy and robust noise resistance in the context of multi-category damage identification on frame structures.

Key words

dual attention mechanism / local maximum synchrosqueezing transform / convolutional neural network / damage identification / frame structure

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

LIU Jingliang1, L Yulin1, ZHENG Wenting2, LIAO Feiyu1, CHEN Zongyan3. Damage identification of frame structure based on CNN model with dual-attention mechanism and improved Inception module#br#[J]. Journal of Vibration and Shock, 2024, 43(23): 321-328

References

[1] 马亚飞，李诚，何羽，等. 基于小波散射卷积神经网络的结构损伤识别[J]. 振动与冲击，2023，42(14)：138-146.
MA Ya-fei, LI Cheng, HE Yu, et al. Structural damage identification based on the wavelet scattering convolution neural network [J]. Journal of Vibration and Shock, 2023, 42(14): 138-146.
[2] 李东升，陈琪舟，魏达，等. 不确定刚度和边界约束条件下的轴力识别[J]. 振动与冲击，2022，41(20)：208-215.
LI Dong-sheng, CHEN Qi-zhou, WEI Da, et al. Axial force identification for uncertain stiffness and boundary constraints [J]. Journal of Vibration and Shock, 2022, 41(20): 208-215.
[3] 袁野. 温度和车辆作用下梁式桥梁结构固有频率分析方法[J]. 吉林大学学报(工学版)，2023，53(6)：1702-1710.
YUAN Ye. Natural frequency analysis of beam bridge structure under temperature and vehicle action [J]. Journal of Jilin University (Engineering and Technology Edition), 2023, 53(6): 1702-1710.
[4] 宋福春，杨子豪，付聿旻，等. 基于LSTM神经网络和残余力向量法的结构损伤识别[J]. 沈阳建筑大学学报(自然科学版)，2023，39(5)：872-879.
SONG Fu-chun, YANG Zi-hao, FU Yu-min, et al. Structural damage recognition based on LSTM neural network and residual force vector method [J] Journal of Shenyang Jianzhu University (Natural Science) 2023, 39(5): 872-879.
[5] Zhang Y, 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(9): 822-839.
[6] 袁建虎，韩涛，唐建，等. 基于小波时频图和CNN的滚动轴承智能故障诊断方法[J]. 机械设计与研究，2017，33(2)：93-97.
YUAN Jian-hu, HAN Tao, TANG Jian, et al. An approach to intelligent fault diagnosis of rolling bearing using wavelet time-frequency representations and CNN [J]. Machine Design and Research, 2017, 33(2): 93-97.
[7] 何浩祥，王玮，黄磊. 基于卷积神经网络和递归图的桥梁损伤智能识别[J]. 应用基础与工程科学学报，2020，28(4)：966-980.
HE Hao-xiang, WANG Wei, HUANG Lei. Intelligent damage detection for bridge based on convolution neural network and recurrence plot [J]. Journal of Basic Science and Engineering, 2020, 28(4): 966-980.
[8] 姚齐水，别帅帅，余江鸿，等. 一种结合改进Inception V2模块和CBAM的轴承故障诊断方法[J]. 振动工程学报，2022，35(4)：949-957.
YAO Qi-shui, BIE Shuai-shuai, YU Jiang-hong, et al. A bearing fault diagnosis method combining improved inception V2 module and CBAM [J]. Journal of Vibration Engineering, 2022, 35(4): 949-957.
[9] 王茜，田慕琴，宋建成，等. 基于经验小波变换的振动信号特征量提取[J]. 振动与冲击，2021，40(16)：261-266.
WANG Xi, TIAN Mu-qin, SONG Jian-cheng, et al. Feature extraction of vibration signals based on empirical wavelet transform[J]. Journal of Vibration and Shock, 2021, 40(16): 261-266.
[10] Daubechies I, Lu J F, Wu H T. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool[J]. Applied and Computation Harmonic Analysis, 2011, 30(2): 243-261.
[11] Yu G, Wang Z, Zhao P, et al. Local maximum synchrosqueezing transform: An energy-concentrated time-frequency analysis tool[J]. Mechanical Systems and Signal Processing, 2019, 117: 537-552.
[12] 赵小强，罗维兰. 改进卷积Lenet-5神经网络的轴承故障诊断方法[J]. 电子测量与仪器学报，2022，36(6)：113-125.
Zhao Xiao-qiang, Luo Wei-lan. Improved convolutional Lenet-5 neural network for bearing fault diagnosis[J]. Journal of Electronic Measurement and Instrumentation, 2022, 36(6): 113-125.
[13] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 2012, 25(2): 1097-1105.
[14] Szecedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions [C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston: CVPR, 2015.
[15] Fu J, Liu J, Tian H J, et al. Dual attention network for scene segmentation[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, 2019.
[16] Guo Y, Chen Q, Chen J, et al. Auto-embedding generative adversarial networks for high resolution image synthesis[J]. IEEE Transactions on Multimedia, 2019, 21(11): 2726-2737.
[17] Johnson E A, Lam H F, Katafygiotis L S, et al. Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data[J]. Journal of Engineering Mechanics, 2004, 130(1): 3-15.
[18] 王航航，张健，袁平平，等. 基于改进同步提取GST的结构瞬时频率识别[J]. 振动、测试与诊断，2022，42(3)：543-548.
WANG Hang-hang, ZHANG Jian, YUAN Ping-ping, et al. Structural instantaneous frequency identification based on improved synchroextracting generalized S transform [J]. Journal of Vibration, Measurement and Diagnosis, 2022, 42(3): 543-548.
[19] Kiranyaz S, Avci O, Abdeljaber O, et al. 1D convolutional neural networks and applications: a survey[J]. Mechanical Systems and Signal Processing, 2021, 151: 107398.