A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems

Remaining useful life (RUL) estimation plays a crucial role in evaluating health states and improving maintenance plans of mechanical systems. Recently, artificial intelligence-based data-driven methods that use monitoring data as input have made significant progress in machine prognostics. However, current methods commonly ignore the correlations and internal differences of monitoring data, consequently leading to limited estimation performance. Therefore, this paper proposes a novel data-driven RUL estimation method named Dual-Stream Self-Attention Neural Network (DS-SANN). First, the multi-head self-attention mechanism is employed to learn correlations between different monitoring data and weigh the features dynamically to obtain global degraded information. Then, a dual-stream structure network is established to extract features from the original and auxiliary data simultaneously to make a comprehensive reflection of health states. The original and auxiliary data represent absolute values and internal differences of monitoring data, respectively. Finally, the multilayer perceptron is adopted to fuse the obtained features and estimate RUL. In addition, the effectiveness of DS-SANN is validated by the public degradation dataset of turbine engines. Compared with several existing prognostics methods, DS-SANN shows better estimation performance when averaging across all sub-datasets. Specifically, estimation effects evaluated by RMSE and Score improve 21.77% and 32.67%, respectively.

Automated summary

This paper proposes a novel data-driven method called Dual-Stream Self-Attention Neural Network (DS-SANN) for RUL estimation. By employing the multi-head self-attention mechanism and dual-stream structure network, DS-SANN can better capture the correlations and internal differences of monitoring data, leading to improved estimation performance for RUL.