Results 51 to 60 of about 1,643,808 (218)
Remaining useful life prediction of lithium-ion batteries using CEEMDAN and WOA-SVR model
Frontiers in Energy Research, 2022 The remaining useful life (RUL) prediction of Lithium-ion batteries (LIBs) is a crucial element of battery health management. The accurate prediction of RUL enables the maintenance and replacement of batteries with potential safety hazards, which ensures
Xianmeng Meng +5 more
doaj +1 more source
Tracking Battery Microstructural Degradation Through Directional Thermal Transport Signatures
Advanced Functional Materials, EarlyView.In lithium‐ion batteries, heat conduction varies between the through‐plane and in‐plane directions because of the cell's layered structure. Therefore, cell degradation influences thermal conductivity in each direction uniquely due to detailed microstructural and compositional changes.
Mohammad Shoghi Tekmedash +6 more
wiley +1 more source
Comparison of RUL prediction between GPM, SVM, and GPR of No.5 and No.6 batteries.
, 2016 Comparison of RUL prediction between GPM, SVM, and GPR of No.5 and No.6 batteries.
Kuei-Hsiang Chao (3121788) +4 more
core +1 more source
Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Sustainable Materials Design With Multi‐Modal Artificial Intelligence
Advanced Science, EarlyView.Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu +8 more
wiley +1 more source
IEEE Access, 2022 Existing joint maintenance decision research typically ignores remaining useful lifetime (RUL) predictions for the accelerated degradation of equipment.
Dingyuan Xue, Zezhou Wang, Yunxiang Chen
doaj +1 more source
jiaxiang-cheng/PyTorch-LSTM-for-RUL-Prediction: LSTM for RUL Prediction
, 2021 PyTorch implementation of remaining useful life prediction with long-short term memories (LSTM), performing on NASA C-MAPSS data sets. Partially inspired by Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017, June).
Jia-Xiang Cheng
core +1 more source
Unsupervised and Supervised Machine Learning Methods for Cutting Tool Path Clustering and RUL Estimation in Manufacturing [PDF]
, 2023 Machine learning has been widely used in manufacturing, leading to significant advances in diverse problems, including the prediction of wear and remaining useful life (RUL) of machine tools.
Avid Roman-Gonzalez +7 more
core +1 more source
Energy Science &Engineering, EarlyView.A rolling local inference framework integrates Kalman filtering, RTS smoothing, variational parameter estimation, and first‐passage‐time analysis for probabilistic remaining useful life prediction of photovoltaic modules. The framework provides stage‐dependent RUL estimates with prediction intervals while explicitly characterizing degradation ...
Wei Chen, Ao Wang, Tingting Pei, Yang Wu
wiley +1 more source
Prediction of RUL of Lubricating Oil Based on Information Entropy and SVM
, 2023 This paper studies the remaining useful life (RUL) of lubricating oil based on condition monitoring (CM). Firstly, the element composition and content of the lubricating oil in use were quantitatively analyzed by atomic emission spectrometry (AES ...
Zhongxin Liu +3 more
core +1 more source