Remaining Useful Life Prediction of Aircraft Engine Based on Bi-LSTM Network Integrated With Attention Mechanism
Guixian Qu, Tian Qiu, Shuiting Ding, Long Ma, Qiyu Yuan, Qinglin Ma, Yang Si · 2024
Abstract Predicting the remaining useful life (RUL) of an aircraft engine is crucial for ensuring the reliability and safety of an aircraft. This study has developed a novel data-driven hybrid network combining a Bidirectional Long Short-Term Memory (BiLSTM) with an attention mechanism to predict the RUL in aircraft engines. The model introduces an innovative approach by incorporating the feature-capture attention mechanism before the BiLSTM layer, which enhances the model’s focus on relevant sensor data segments to enable more effective feature extraction from multi-sensor data. This integration significantly enhances prediction accuracy compared to traditional shallow and deep learning models by leveraging the BiLSTM’s capability to analyze time-series data in both forward and backward directions. The proposed model demonstrates superior accuracy through comparative experiments conducted on the NASA C-MAPSS dataset, underscoring its potential to advance malfunction prediction and health management in aircraft engines.