Novel Transformer-Based Fusion Models for Aero-Engine Remaining Useful Life Estimation

Abstract

Remaining Useful Life (RUL) estimation is a crucial technology in prognostic and health management (PHM) for modern aero-engines, as it ensures the reliability and safety of aircraft. With advances in sensor technology, data-driven approaches for RUL estimation have gained significant interest in recent years, especially deep learning-based methods. To further contribute to the field and improve the accuracy of RUL estimation, this paper, proposes novel Transformer-based fusion models for aero-engine RUL estimation. The vanilla Transformer is adapted for RUL estimation by modifying its structure based on the characteristics of aero-engine sensor data. The modified Transformer is then fused with Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to extract degradation features from multiple aspects. Specifically, the LSTM and CNN layers are incorporated into the decoder and encoder of the Transformer. The effectiveness and superiority of the proposed models are demonstrated through experiments on the C-MAPSS benchmark dataset. The experimental results show that the proposed LSTM-Transformer fusion model outperforms the existing state-of-the-art approaches, with up to 66.53% and 84.86% improvement in RMSE and score metrics, respectively.

Keywords

• Aero-engine
• prognostic and health management
• remaining useful life estimation
• transformer
• long short-term memory neural network
• convolution neural network