LSTM-GRU and Multi-Head Attention Based Multivariate Time Series Prediction Model for Electro-Hydraulic Servo Material Fatigue Testing Machine

To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions, this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory (LSTM) encoder, a Gated Recurrent Unit (GRU) decoder, and a multi-head attention mechanism. This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions, thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics. Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set, with MAE and RMSE of approximately 0.018 and 0.052, respectively, and a coefficient of determination reaching 0.98. This significantly outperforms traditional identification methods and single RNN models. Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead. Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities. This model can be applied to residual-driven early warning in health monitoring, and risk assessment with scheme optimization in test design. It enables near-real-time deployment feasibility, providing a practical data-driven technical pathway for reliability assurance in advanced equipment.