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

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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

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