Method for Plate Crack Damage Detection Based on Long Short-Term Memory Neural Network

doi:10.16183/j.cnki.jsjtu.2020.095

Abstract

Abstract:

Aimed at the problem of intelligent classification of crack damage in different positions of the plate, a method for plate crack damage detection based on long short-term memory (LSTM) neural network is proposed. The Abaqus secondary development is used to build the plate crack damage model and calculate the acceleration response of the plate under Gaussian white noise excitation. The data set is generated by data augmentation, and the influence of noise on damage detection is considered. An intelligent crack detection model based on LSTM is established, which directly takes the acceleration response of the plate as the input and does not require additional damage feature extraction. With the goal of minimizing prediction error, the hyperparameter of the model is selected and the model configuration is optimized. The comparison of the multi-layer perceptron model and the multi-layer perceptron model based on wavelet packet transform shows that the LSTM model proposed in this paper has a higher damage location accuracy and a better applicability in plate crack detection.

Key words: damage detection, long short-term memory (LSTM) neural network, plate crack, noise

CLC Number:

U663.6
TP183

ZHANG Songlin, MA Dongliang, WANG Deyu. Method for Plate Crack Damage Detection Based on Long Short-Term Memory Neural Network[J]. Journal of Shanghai Jiao Tong University, 2021, 55(5): 527-535.

Figures/Tables 16

Fig.1

Fig.2

Tab.1

Tab.2

Fig.3

Fig.4

Fig.5

Tab.3

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

References 13

[1]	张敬芬, 赵德有. 工程结构裂纹损伤振动诊断的发展现状和展望[J]. 振动与冲击, 2002, 21(4):22-26.
	ZHANG Jingfen, ZHAO Deyou. Summary review of vibration-based crack diagnosis technique for engingeering structures[J]. Journal of Vibration and Shock, 2002, 21(4):22-26.
[2]	贾连徽, 任慧龙, 孙树政, 等. 船体结构应力监测点的选取方法研究[J]. 船舶力学, 2013, 17(4):389-397.
	JIA Lianhui, REN Huilong, SUN Shuzheng, et al. Research on ship structural stress monitoring points selection method[J]. Journal of Ship Mechanics, 2013, 17(4):389-397.
[3]	YANG J, HWANG C N, YANG B L. Crack identification in beams and plates by discrete wavelet transform method[J]. Journal of Ship Mechanics, 2008, 12(3):464-472.
[4]	MEHRJOO M, KHAJI N, MOHARRAMI H, et al. Damage detection of truss bridge joints using Artificial Neural Networks[J]. Expert Systems with Applications, 2008, 35(3):1122-1131. doi: 10.1016/j.eswa.2007.08.008 URL
[5]	GREFF K, SRIVASTAVA R K, KOUTNÍK J, et al. LSTM: A search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(10):2222-2232. doi: 10.1109/TNNLS.2016.2582924 URL
[6]	张建付, 宋雨, 李刚, 等. 基于长短时记忆神经网络的风电机组滚动轴承故障诊断方法[J]. 计算机测量与控制, 2017, 25(1):16-19.
	ZHANG Jianfu, SONG Yu, LI Gang, et al. A method of fault diagnosis for rolling bearing of wind turbines based on long short-term memory neural network[J]. Computer Measurement & Control, 2017, 25(1):16-19.
[7]	ZOU P, HOU B C, LEI J, et al. Bearing fault diagnosis method based on EEMD and LSTM[J]. International Journal of Computers Communications & Control, 2020, 15(1):1-14.
[8]	陈伟, 陈锦雄, 江永全, 等. 基于RS-LSTM的滚动轴承故障识别[J]. 中国科技论文, 2018, 13(10):1134-1141.
	CHEN Wei, CHEN Jinxiong, JIANG Yongquan, et al. Fault identification of rolling bearing based on RS-LSTM[J]. China Sciencepaper, 2018, 13(10):1134-1141.
[9]	YU L, QU J L, GAO F, et al. A novel hierarchical algorithm for bearing fault diagnosis based on stacked LSTM[J]. Shock and Vibration, 2019, 2019:1-10.
[10]	LUO P, HU Y. Research on rolling bearing fault Identification method based on LSTM neural network[C]//2018 International Conference on Mechanical Engineering, Materials Science and Civil Engineering, Xiamen, China: nstitute of Physics Publishing, 2019, 542:012048.
[11]	SRIVASTAVA N, HINTON G E, KRIZHEVSKY A, et al. Dropout: A simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1):1929-1958.
[12]	LIN Y Z, NIE Z H, MA H W. Structural damage detection with automatic feature-extraction through deep learning[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(12):1025-1046. doi: 10.1111/mice.2017.32.issue-12 URL
[13]	李世玲, 李治, 李合生. 基于小波包能量特征的滚动轴承故障监测方法[J]. 系统仿真学报, 2003, 15(1):76-80.
	LI Shiling, LI Zhi, LI Hesheng. The method of roller bearing fault monitoring based on wavelet packet engergy feature[J]. Journal of System Simulation, 2003, 15(1):76-80.

项目	参数
密度/(t·mm^-3)	7.85×10^-9
弹性模量/MPa	2.1×10⁵
泊松比	0.3
α/s^-1	7.2
β/s	2×10^-7

模态阶数	频率/Hz
模态阶数	简支	固支
1	38.33	81.35
2	49.79	90.54
3	68.94	107.50
4	95.84	133.21
5	130.60	167.99
6	142.98	211.80
7	154.35	222.11
8	173.34	231.92

参数	数值
时期	40
批处理大小	64
输入特征维度	11
隐藏层维度	12
LSTM层数	3
舍弃概率	0.5

[1]	FAN Hong, WEI Xinwu, JIA Qingshan, LUO Jiayi. Collaborative Optimization Scheduling Method for Electric-Gas-Thermal Multi-Energy System Under Energy-Transportation Integration [J]. Journal of Shanghai Jiao Tong University, 2026, 60(2): 211-223.
[2]	LIU Qi, HE Yifei, GU Ming, CHEN Zihao, LI Yunhao, WANG Tao. Density Cluster-Based Clutter Removal Technology for Millimeter-Wave Radar Target Point Cloud [J]. Air & Space Defense, 2026, 9(1): 63-72.
[3]	MA Changxi, HUANG Xiaoting, MENG Wei. Predicting Parking Spaces Using CEEMDAN and GRU [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 962-975.
[4]	NIU Zirong, WU Feng, HAN Zhaolong, ZHUO Yang, CHE Ailan, ZHU Hongbo. An Improved Generalized Flexibility Sensitivity Method for Structural Damage Detection [J]. Journal of Shanghai Jiao Tong University, 2025, 59(4): 541-549.
[5]	LI Jiayang, ZHAO Jiuyi, QIAN Yong, LI Guoyu, XU Zhiren, PAN Chao, SHENG Gehao. Location of Partial Discharge in GIS Based on Electromagnetic Wave Time Reversal [J]. Journal of Shanghai Jiao Tong University, 2025, 59(12): 1763-1772.
[6]	CAO Wenping, WANG Yao, ZHANG Yue, LUO Kui, HU Cungang, RUI Tao. Three-Vector Model-Free Predictive Current Control Method for Grid-Connected Inverters with Sampling Noise Compensation [J]. Journal of Shanghai Jiao Tong University, 2025, 59(10): 1523-1532.
[7]	LI Fen, SUN Ling, WANG Yawei, QU Aifang, MEI Nian, ZHAO Jinbin. Short-Term Interval Forecasting of Photovoltaic Power Based on CEEMDAN-GSA-LSTM and SVR [J]. Journal of Shanghai Jiao Tong University, 2024, 58(6): 806-818.
[8]	WANG Zengwei (王增伟), DING Lei^∗ (丁雷). Structural Damage Detection and Localization Using Response Difference Transmissibility [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(6): 1126-1138.
[9]	ZHANG Guodong, LIU Kai, PU Haitao, YAO Fuqiang, ZHANG Shuaishuai. Identification of Inrush Current and Fault Current Based on Long Short-Term Memory Neural Network [J]. Journal of Shanghai Jiao Tong University, 2024, 58(5): 730-738.
[10]	ZHANG Xiaotian¹(张啸天), HE Defeng^1* (何德峰), LIAO Fei² (廖飞). Iterative Model Predictive Control for Automatic Carrier Landing of Carrier-Based Aircrafts Under Complex Surroundings and Constraints [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(4): 712-724.
[11]	JIANG Ruitao(姜瑞涛), YANG Xing(杨星), DENG Youming(邓又铭)，LENG Yongbin(冷用斌). Diagnostic Method for Beam Position Monitor Based on Clustering by Fast Search and Find of Density Peaks [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 457-462.
[12]	YU Sheng (于生), LI Xiangshun^∗ (李向舜). Identification of Steady State and Transient State [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(2): 261-270.
[13]	HAN Yun, ZHAO Xinfang, MA Yongben. Analysis of Airborne Sonobuoy Self-noise [J]. Ocean Engineering Equipment and Technology, 2024, 11(1): 77-81.
[14]	SHANG Fancheng, LI Chuanqing, ZHAN Ke, ZHU Renchuan. Application of Improved LSTM Neural Network in Time-Series Prediction of Extreme Short-Term Wave [J]. Journal of Shanghai Jiao Tong University, 2023, 57(6): 659-665.
[15]	LI Jie, SI Weijian, LIU Jianing. DOA Estimation of Nested Array Under Non-Uniform Noise [J]. Air & Space Defense, 2023, 6(4): 86-92.