LSTM-based multi-layer self-attention method for remaining useful life estimation of mechanical systems

Abstract

Accurate remaining useful life (RUL) estimation is significant in reducing maintenance costs and avoiding catastrophic failures of mechanical systems like an aeroengine. To effectively estimate the RUL of mechanical systems, the long short-term memory (LSTM)-based multi-layer self-attention (MLSA) (LSTM-MLSA) method is proposed by designing MLSA mechanism and LSTM, to improve the modeling precision and computing efficiency. In the MLSA mechanism, the multi-layer is respected to extract the effective features of system degradation data in different subspace, and self-attention is employed to establish the accurate correlation of time steps in raw time series data by parallel computation. The LSTM is used to process the extracted features and capture the degradation process of the mechanical system. The RUL estimation of an aeroengines with life degradation data is implemented, to validate the proposed LSTM-MLSA method by comparing with other RUL estimation methods. The results illustrate that the LSTM-MLSA method has high computational efficiency theoretically, high accuracy, and strong robustness in the RUL estimation of an aeroengines. The efforts of this paper provide a highly-efficient method for the RUL estimation of complex mechanical systems, which is promising to enhance the operation and maintenance of the mechanical system by reducing costs and improving RUL estimation precision.