Explainable artificial intelligence for the remaining useful life prognosis of the turbofan engines

Chang Woo Hong, Changmin Lee, Kwangsuk Lee, Min Seung Ko, Kyeon Hur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020
EditorsTeen-Hang Meen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages144-147
Number of pages4
ISBN (Electronic)9781728193335
DOIs
Publication statusPublished - 2020 Aug 21
Event3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020 - Kaohsiung, Taiwan, Province of China
Duration: 2020 Aug 212020 Aug 23

Publication series

NameProceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020

Conference

Conference3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020
CountryTaiwan, Province of China
CityKaohsiung
Period20/8/2120/8/23

Bibliographical note

Funding 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).

Publisher Copyright:
© 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

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