Attention and Long Short-Term Memory Network for Remaining Useful Lifetime Predictions of Turbofan Engine Degradation

• Published in: International journal of prognostics and health management Vol. 10; no. 4
• Main Authors: Costa, Paulo Roberto de Oliveira da, Akcay, Alp, Zhang, Yingqian, Kaymak, Uzay
• Format: Journal Article
• Language: English
• Published: The Prognostics and Health Management Society 04.06.2023
• Subjects: attention; deep learning; prognostics; recurrent neural networks
• DOI: 10.36001/ijphm.2019.v10i4.2623
• ISSN: 2153-2648

Machine Prognostics and Health Management (PHM) is often concerned with the prediction of the Remaining Useful Lifetime (RUL) of assets. Accurate real-time RUL predictions enable equipment health assessment and maintenance planning. In this work, we propose a Long Short-Term Memory (LSTM) network combined with global Attention mechanisms to learn RUL relationships directly from time-series sensor data. We use the NASA Commercial Modular Aero- Propulsion System Simulation (C-MAPPS) datasets to assess the performance of our proposed method. We compare our approach with current state-of-the-art methods on the same datasets and show that our results yield competitive results. Moreover, our method does not require previous degradation knowledge, and attention weights can be used to visualise temporal relationships between inputs and predicted outputs.