TY  - JOUR
Y1  - 2021///
SN  - 20799292
PB  - MDPI
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116790136&doi=10.3390%2felectronics10202453&partnerID=40&md5=d9c02e2a8437af0a46aee3e9c59729ba
JF  - Electronics (Switzerland)
A1  - Muneer, A.
A1  - Taib, S.M.
A1  - Naseer, S.
A1  - Ali, R.F.
A1  - Aziz, I.A.
VL  - 10
AV  - none
N2  - Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method's applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
N1  - cited By 26
IS  - 20
TI  - Data-driven deep learning-based attention mechanism for remaining useful life prediction: Case study application to turbofan engine analysis
ID  - scholars14399
ER  -