A novel method for aero-engine time-series forecasting based on multi-resolution transformer

Abstract

Time-series forecasting is widely studied in the data-driven component-level modeling, fault diagnosis, and performance prediction of aero-engines. The operational data of aero-engines have the characteristics of complexity and nonlinearity. This paper proposes a multi-resolution transformer (MRT) model for the non-steady state process of aero-engines. This transformer-based model can learn temporal patterns of different scales by performing multi-resolution down-sampling and patch-based tokeniczation on the input time-series. On this basis, a two-stage multi-resolution transformer (TSMRT) framework is proposed for the entire processes time-series modeling of aero-engines. The TSMRT framework employs Augmented Dickey-Fuller (ADF) test to classify aero-engine operational data into steady state processes and non-steady state processes, and models the two processes separately using time–frequency feature neural network (TFNN) model and MRT model. The time-series forecasting performance of the MRT model is validated on three publicly available benchmark datasets and an ablation study is conducted on turbofan engine test bench datasets. The results indicate that the TSMRT framework demonstrates superior forecasting performance across steady state, non-steady state, and entire processes. This framework stands out as a novel method for component-level modeling of aero-engines.