Structural response estimation based on deep learning can suffer from reduced estimation performance owing to discrepancies between the training and test data as the noise level in the test data increases. This study proposes a short‐time Fourier transform‐based long short‐term memory (STFT‐LSTM) model to improve estimation performance in the presence of noise and ensure estimation robustness. This model enables robust estimations in the presence of noise by positioning an STFT layer before feeding the data into the LSTM layer. The output transformed into the time‐frequency domain by the STFT layer is learned by the LSTM model. The robustness of the proposed model was validated using a numerical model with three degrees of freedom at various signal‐to‐noise ratio levels, and its robustness against impulse and periodic noise was verified. Experimental validation assessed the estimation robustness under impact load and verified the robustness against environmental noise in the acquired acceleration response.

中文翻译：

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使用短时傅里叶变换和长短期记忆的噪声鲁棒结构响应估计方法


随着测试数据中噪声水平的增加，由于训练数据和测试数据之间的差异，基于深度学习的结构响应估计可能会降低估计性能。本研究提出了一种基于短时傅里叶变换的长短期记忆 （STFT-LSTM） 模型，以提高存在噪声时的估计性能并确保估计稳健性。该模型通过在将数据馈送到 LSTM 层之前定位 STFT 层，在存在噪声的情况下实现稳健估计。STFT 层转换为时频域的输出由 LSTM 模型学习。使用在不同信噪比水平下具有三个自由度的数值模型验证了所提出的模型的鲁棒性，并验证了其对脉冲和周期性噪声的鲁棒性。实验验证评估了冲击载荷下的估计鲁棒性，并验证了获得性加速响应中对环境噪声的鲁棒性。