Estimation of Remaining Useful Life of Rolling Element Bearings Using Wavelet Packet Decomposition and Artificial Neural Network

2018 ◽  
Vol 43 (S1) ◽  
pp. 233-245 ◽  
Author(s):  
Abbas Rohani Bastami ◽  
Aref Aasi ◽  
Hesam Addin Arghand
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Packet ◽  
Remaining Useful Life ◽  
Wavelet Packet Decomposition ◽  
Rolling Element Bearings ◽  
Rolling Element ◽  
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.

Remaining useful life prediction for lithium-ion batteries using particle filter and artificial neural network

2019 ◽  
Vol 120 (2) ◽  
pp. 312-328 ◽  
Author(s):  
Wei Qin ◽  
Huichun Lv ◽  
Chengliang Liu ◽  
Datta Nirmalya ◽  
Peyman Jahanshahi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Filter ◽  
Prediction Accuracy ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Content Type ◽  
Public Data ◽  
Useful Life ◽  
Artificial Neural

Purpose With the promotion of lithium-ion battery, it is more and more important to ensure the safety usage of the battery. The purpose of this paper is to analyze the battery operation data and estimate the remaining life of the battery, and provide effective information to the user to avoid the risk of battery accidents. Design/methodology/approach The particle filter (PF) algorithm is taken as the core, and the double-exponential model is used as the state equation and the artificial neural network is used as the observation equation. After the importance resampling process, the battery degradation curve is obtained after getting the posterior parameter, and then the system could estimate remaining useful life (RUL). Findings Experiments were carried out by using the public data set. The results show that the Bayesian-based posterior estimation model has a good predictive effect and fits the degradation curve of the battery well, and the prediction accuracy will increase gradually as the cycle increases. Originality/value This paper combines the advantages of the data-driven method and PF algorithm. The proposed method has good prediction accuracy and has an uncertain expression on the RUL of the battery. Besides, the method proposed is relatively easy to implement in the battery management system, which has high practical value and can effectively avoid battery using risk for driver safety.

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