Legendre Memory Model Enabled Attention Network for Turbofan Engines Remaining Useful Life Prediction

Accurate Remaining Useful Life (RUL) prediction of turbofan engines not only reduces labor and maintenance costs but also mitigates the risk of aircraft accidents. To solve the noise interference problem, we present a Legendre memory model enabled Attention Network (LAN). The LAN utilizes Legendre memory model and dual multi-head self-attention. Legendre memory model contains two sub-layers: Legendre Projection Unit (LPU) and Frequency Selection Layer (FSL). The sensor data is projected into the state space using the LPU to retain the historical data information. Next, frequency components are selected using the FSL to reduce the impact of noise. Meanwhile, the dual multi-head self-attention is applied to capture useful feature information from both the time step and sensor dimensions for RUL prediction. Lastly, the extracted features are inputted into the residual convolutional network for RUL prediction. Experiments are performed on the widely adopted C-MAPSS dataset, and the results substantiate the efficacy of the proposed approach.