A comparison between extreme learning machine and artificial neural network for remaining useful life prediction

2016 ◽  
Author(s):  
Zhe Yang ◽  
Piero Baraldi ◽  
Enrico Zio
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Extreme Learning Machine ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life ◽  
Artificial Neural ◽  
Learning Machine

Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

2015 ◽  
Vol 56-57 ◽  
pp. 150-172 ◽  
Author(s):  
Jaouher Ben Ali ◽  
Brigitte Chebel-Morello ◽  
Lotfi Saidi ◽  
Simon Malinowski ◽  
Farhat Fnaiech
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Weibull Distribution ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life ◽  
Artificial Neural

scholarly journals Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7521
Author(s):  
Shaheer Ansari ◽  
Afida Ayob ◽  
Molla Shahadat Hossain Lipu ◽  
Aini Hussain ◽  
Mohamad Hanif Md Saad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Ion Batteries ◽  
Electric Vehicle ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Systematic Sampling ◽  
Channel Input ◽  
Useful Life ◽  
Artificial Neural

Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets.

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