Research on sinusoidal load identification method under structural natural frequency excitation based on LSTM-CNN

Addressing the challenge of low identification accuracy in traditional load identification methods based on the truncated singular value decomposition（TSVD）method，especially when the external load frequency approaches or reaches the natural frequency of the structure，the LSTM-CNN load identification model is proposed in this paper. This model combines the feature extraction capabilities of the convolutional neural network（CNN）with the long-term memory function of the long short-term memory network（LSTM）. The load identification method based on the LSTM-CNN model is then applied to research load time domain waveform identification on the GAR TEUR aircraft model. For model training and load identification，the response data and excitation data from the structure are corrected. The identification results are compared with the TSVD method，LSTM method，and DCNN method. Results show that the load identification method based on the LSTM-CNN model proves effective for sinusoidal load identification problems，especially under the natural frequency excitation of the structure. The method exhibits high identification accuracy and robust noise resistance capabilities.