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

Paulo Roberto de Oliveira da Costa, Alp Akcay, Yingqian Zhang, and Uzay Kaymak
Publication Target: 
IJPHM
Publication Issue: 
Special Issue on Deep Learning and Emerging Analytics
Submission Type: 
Full Paper
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ijphm_19_034.pdf977.69 KBJanuary 17, 2020 - 8:03am

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.

Publication Year: 
2019
Publication Volume: 
10
Publication Control Number: 
034
Page Count: 
12
Submission Keywords: 
Prognostics; Deep Learning;Recurrent Neural Networks; Attention
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
  
 
 
 

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