Remaining useful life(RUL)prediction is crucial for prognostics and health management of large equipment.However,nonlinear characteristics such as high dimen-sionality,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 multi-scale 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.
remaining useful life(RUL)prognostics and health managementbi-directional long short-term memoryTransformer
NETL
NSTL
万方数据
