Abstract: Remaining useful life (RUL) prediction is crucial for prognostics and health management of large equipment. However, nonlinear characteristics such as high dimensionality, large scale, strong coupling, and time-varying parameters in monitoring data of some devices can lead to low accuracy in RUL prediction. To solve this problem, this paper introduces a neural network model that combines a transformer decoder with a multiscale bi-directional long and short-term memory network. This model improves prediction accuracy of the model by integrating global information through a multi-head attention mechanism. Using aviation engines as the research focus, comparative experiments were conducted employing various models on NASA’s C-MPASS dataset. The results show that the proposed multi-scale bi-directional long and short-term memory network fused with Transformer model (MSBiLSTM-Transformer) outperforms other benchmark models, demonstrating superior performance in both accuracy and root mean square error metrics.

Key words: remaining useful life (RUL), prognostics and health management, bi-directional long short-term memory, Transformer