平稳随机载荷的信号特征提取与深度神经网络识别

doi:10.7527/S1000-6893.2021.25952

Abstract

Abstract: A feature signal identification method for stationary random dynamic load is proposed based on the dynamic principle of structures. using Wavelet transform is used to extract the time-frequency characteristics of signals, and Long-Short Term Memory (LSTM) is employed to model and map sequence problems. The feasibility of the method is proved byidentification of stationary random dynamic loads acting on a three-degree-of-freedom vibration system. The dynamic load identification experiment is carried out on a stiffened panel structure model under two-point stationary random loads. The results show that the root mean square error of dynamic load identified by the proposed method is less than 5%, and the method has good identification ability.

Key words: stationary random dynamic load, wavelet transform, vibration signal feature extraction, deep neural network, dynamic load identification

CLC Number:

YANG Te, YANG Zhichun, LIANG Shuya, KANG Zaifei, JIA You. Feature extraction and identification of stationary random dynamic load using deep neural network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(9): 225952-225952.

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Feature extraction and identification of stationary random dynamic load using deep neural network

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