This paper proposes a deep-stacked neural network to prognose the remaining useful life of the turbofan engines and analyze results using explainable artificial intelligence. The proposed model prognoses the remaining useful life of the turbofan engines accurately by properly stacking a one-dimensional convolutional neural network (1D-CNN), long short-term memory (LSTM), and bidirectional LSTM algorithms. This model also uses dropout technique and residual network to enhance the prediction accuracy. The Explainable artificial intelligence analyzes the prognostic results of RUL. Using SHAP (SHapely Addictive exPlanation), this model can analyze features that have significantly influenced RUL prediction. The SHAP force plot and decision plot can help decision-makers of the turbofan engine properly manage the maintenance by showing the influence of sensors.
|Title of host publication||Proceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|Publication status||Published - 2020 Aug 21|
|Event||3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020 - Kaohsiung, Taiwan, Province of China|
Duration: 2020 Aug 21 → 2020 Aug 23
|Name||Proceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020|
|Conference||3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020|
|Country||Taiwan, Province of China|
|Period||20/8/21 → 20/8/23|
Bibliographical noteFunding Information:
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
- Management of Technology and Innovation
- Artificial Intelligence
- Computer Networks and Communications
- Hardware and Architecture
- Electrical and Electronic Engineering
- Electronic, Optical and Magnetic Materials