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.
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
.
DOI: 10.7527/S1000-6893.2021.25952
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