This paper proposes a deep model structure to improve the prediction accuracy of remaining useful life (RUL) of the turbofan engine by using the correlation analysis that reduces the model complexity. The proposed model maximizes the performance by appropriately stacking one dimensional convolutional neural network (1D-CNN), long short-term memory (LSTM), and bidirectional LSTM algorithm. It also includes residual network and dropout technique to improve the learning ability of the proposed model. The result of RUL forecasting using the proposed model is compared with that of various conventional methods, which demonstrates better efficiency yet high performance thanks to excluding the low correlated data.
|Title of host publication||Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020|
|Editors||Wookey Lee, Luonan Chen, Yang-Sae Moon, Julien Bourgeois, Mehdi Bennis, Yu-Feng Li, Young-Guk Ha, Hyuk-Yoon Kwon, Alfredo Cuzzocrea|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|Publication status||Published - 2020 Feb|
|Event||2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020 - Busan, Korea, Republic of|
Duration: 2020 Feb 19 → 2020 Feb 22
|Name||Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020|
|Conference||2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020|
|Country||Korea, Republic of|
|Period||20/2/19 → 20/2/22|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (grant number.2018R1D1A1A09083054).
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Information Systems and Management
- Control and Optimization