Open Access

ARTICLE

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

Guotai Huang, Xiyu Gao, Peng Liu, Liming Zhou^*

School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130025, China

* Corresponding Author: Liming Zhou. Email:

Computers, Materials & Continua 2026, 87(2), 9 https://doi.org/10.32604/cmc.2026.074941

Received 22 October 2025; Accepted 25 December 2025; Issue published 12 March 2026

Abstract

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.

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Keywords

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