A Prediction Model for Remaining Useful Life of Turbofan Engines by Fusing Broad Learning System and Temporal Convolutional Network

Abstract

In this paper, a prediction model based on a broad learning system (BLS) and temporal convolutional network (TCN) is proposed to measure the remaining useful life (RUL) of turbofan engines. Firstly, a variational autoencoder (VAE) is used to extract important low-dimensional features from the engine sensor data. Then, the degradation information is extracted from the time and feature dimensions of fragment data using TCN. Further, the BLS combined with residual connection is used to enhance the nonlinear representation of the model. The proposed method is validated on the commercial modular aero propulsion system simulation (C-MAPSS) dataset and compared with some state-of-the-art methods. The experimental results show that the proposed method is effective in RUL prediction.