Remaining Useful Life Prediction for Supercapacitors Using an Optimized End-to-End Deep Learning Approach 


Vol. 47,  No. 3, pp. 482-491, Mar.  2022
10.7840/kics.2022.47.3.482


PDF
  Abstract

Remaining useful life (RUL) prediction for supercapacitors is particularly important to ensure the safety of the applied system and reduce the cost of operation. The existing RUL prediction method utilized health indicators (HIs) that are extracted by a conventional method. This method has the risk of dropping useful information in the supercapacitor data which leads to low accuracy because of poor quality features. To resolve this issue, this paper proposes an optimized end-to-end deep learning model for RUL prediction. Specifically, a genetic algorithm (GA) for automatic feature selection and long short-term memory (LSTM) network (GA-LSTM) for RUL prediction. GA is utilized for automatic feature extraction which ensures all important information in the supercapacitor data is considered during HI extraction. The combination of the best-selected features is used as the input to the LSTM model for final RUL prediction. Our proposed model achieved a root mean square error (RMSE) of 0.03 unlike the recurrent neural network, LSTM, and deep convolutional neural network with RMSE of 23.87, 0.51, and 0.38, respectively. When compared with other models, the overall results show that our model exhibits excellent performance for the RUL prediction of supercapacitors.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

C. U. Udeogu, A. C. Caliwag, W. Lim, "Remaining Useful Life Prediction for Supercapacitors Using an Optimized End-to-End Deep Learning Approach," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 3, pp. 482-491, 2022. DOI: 10.7840/kics.2022.47.3.482.

[ACM Style]

Chigozie Uzochukwu Udeogu, Angela C. Caliwag, and Wansu Lim. 2022. Remaining Useful Life Prediction for Supercapacitors Using an Optimized End-to-End Deep Learning Approach. The Journal of Korean Institute of Communications and Information Sciences, 47, 3, (2022), 482-491. DOI: 10.7840/kics.2022.47.3.482.

[KICS Style]

Chigozie Uzochukwu Udeogu, Angela C. Caliwag, Wansu Lim, "Remaining Useful Life Prediction for Supercapacitors Using an Optimized End-to-End Deep Learning Approach," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 3, pp. 482-491, 3. 2022. (https://doi.org/10.7840/kics.2022.47.3.482)

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880