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.
| Translated title of the contribution | Feature extraction and identification of stationary random dynamic load using deep neural network |
|---|---|
| Original language | Chinese (Traditional) |
| Article number | 225952 |
| Journal | Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica |
| Volume | 43 |
| Issue number | 9 |
| DOIs | |
| State | Published - 25 Sep 2022 |