Remaining Useful Life Prediction of Turbofan Engine Using Hybrid Model Based on Autoencoder and Bidirectional Long Short-Term Memory

Abstract

Turbofan engine is a critical aircraft component with complex structure and high-reliability requirements. Effectively predicting the remaining useful life (RUL) of turbofan engines has essential significance for developing maintenance strategies and reducing maintenance costs. Considering the characteristics of large sample size and high dimension of monitoring data, a hybrid health condition prediction model integrating the advantages of autoencoder and bidirectional long short-term memory (BLSTM) is proposed to improve the prediction accuracy of RUL. Autoencoder is used as a feature extractor to compress condition monitoring data. BLSTM is designed to capture the bidirectional long-range dependencies of features. A hybrid deep learning prediction model of RUL is constructed. This model has been tested on a benchmark dataset. The results demonstrate that this autoencoder-BLSTM hybrid model has a better prediction accuracy than the existing methods, such as multi-layer perceptron (MLP), support vector regression (SVR), convolutional neural network (CNN) and long short-term memory (LSTM). The proposed model can provide strong support for the health management and maintenance strategy development of turbofan engines.