YU Qiongfang1, 2, 3, SUN Chengcheng1, YANG Yi1, YANG Pengfei1, WANG Keyi1
Journal of Vibration and Shock. 2026, 45(3): 20-31.
Accurate prediction of hydraulic bracket pressure signals has important theoretical significance and application value for improving the safety production level of coal mines.However, existing methods still have certain limitations in dealing with complex time-frequency characteristics of pressure signals.Here, a stress signal prediction framework called time-frequency transformer network (TF-TransNet) was proposed to integrate time-frequency domain analysis and deep learning.Firstly, Gaussian moving average filtering was used to denoise the original pressure signal, and Granger causality analysis was used to screen the most predictive feature variables.In feature representation stage, channel attention mechanism was introduced to dynamically adjust feature weights, and a feature fusion module was designed to enhance interaction modeling ability among variables.The core of the model was an innovative time-frequency fusion encoding and decoding structure, it could combine fast Fourier transform (FFT), long short-term memory (LSTM) network and probability sparse attention mechanism to realize deep mining of multi-dimensional time-frequency features of pressure signals.FFT could provide global information in frequency domain to reveal periodic patterns hidden in signal.LSTM could effectively capture long-term time sequential dependencies.Probabilistic sparse attention mechanism could guide the model to focus on key time-frequency feature information.Taking actual hydraulic bracket pressure data of Fucun Coal Mine in Zaozhuang, Shandong as the basis, TF-TransNet model was compared with traditional LSTM and Transformer models, respectively.Contributions of various modules to model performance were verified with ablation experiments.The experimental results showed that in a 12-step prediction task, compared to LSTM and Transformer, TF-TransNet can reduce root mean square error by about 28.10% and 20.48%, respectively, reduce mean absolute error by about 33.62% and 24.00%, respectively and improve R2 index by 0.482 6 and 0.301 0, respectively.
