[HTML][HTML] The development of machine learning-based remaining useful life prediction for lithium-ion batteries
X Li, D Yu, VS Byg, SD Ioan - Journal of Energy Chemistry, 2023 - Elsevier
Lithium-ion batteries are the most widely used energy storage devices, for which the
accurate prediction of the remaining useful life (RUL) is crucial to their reliable operation and …
accurate prediction of the remaining useful life (RUL) is crucial to their reliable operation and …
[HTML][HTML] A critical review of improved deep convolutional neural network for multi-timescale state prediction of lithium-ion batteries
Lithium-ion batteries are widely used as effective energy storage and have become the main
component of power supply systems. Accurate battery state prediction is key to ensuring …
component of power supply systems. Accurate battery state prediction is key to ensuring …
A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition
Most supervised learning-based approaches follow the assumptions that offline data and
online data must obey a similar distribution, which is difficult to satisfy in realistic remaining …
online data must obey a similar distribution, which is difficult to satisfy in realistic remaining …
An overview of data-driven battery health estimation technology for battery management system
Battery degradation, caused by multiple coupled degradation mechanisms, severely affects
the safety and sustainability of a battery management system (BMS). The battery state of …
the safety and sustainability of a battery management system (BMS). The battery state of …
Artificial intelligence-based data-driven prognostics in industry: A survey
MA El-Brawany, DA Ibrahim, HK Elminir… - Computers & Industrial …, 2023 - Elsevier
In the age of Industry 5.0, prognostics and health management (PHM) is very important for
proactive and scheduled maintenance in industrial processes. The target of prognosis is the …
proactive and scheduled maintenance in industrial processes. The target of prognosis is the …
A dynamic spectrum loss generative adversarial network for intelligent fault diagnosis with imbalanced data
Intelligent fault diagnosis with imbalanced data is a problem that often raises concerns. The
diagnosis is more effective when the imbalanced dataset is supplemented with data …
diagnosis is more effective when the imbalanced dataset is supplemented with data …
Joint training of a predictor network and a generative adversarial network for time series forecasting: A case study of bearing prognostics
The lack of bearing run-to-failure data has been one of the challenges in developing and
practically implementing robust bearing prognostics models. This paper proposes a new …
practically implementing robust bearing prognostics models. This paper proposes a new …
Dual residual attention network for remaining useful life prediction of bearings
Rolling bearing is a critical component of rotating machines and it is indispensable to
accurately predict the remaining useful life (RUL) of bearings to realize predictive …
accurately predict the remaining useful life (RUL) of bearings to realize predictive …
A novel dual attention mechanism combined with knowledge for remaining useful life prediction based on gated recurrent units
Y Li, Y Chen, H Shao, H Zhang - Reliability Engineering & System Safety, 2023 - Elsevier
Abstract Improving Remaining Useful Life (RUL) prediction accuracy in Prognostic and
Health Management (PHM) is the primary pursuit of researchers. Deep learning provides …
Health Management (PHM) is the primary pursuit of researchers. Deep learning provides …
Transfer life prediction of gears by cross-domain health indicator construction and multi-hierarchical long-term memory augmented network
The long-term remaining useful life (RUL) prediction of gears is crucial for the safe operation
and maintenance of rotating machinery. However, most existing RUL prediction methods …
and maintenance of rotating machinery. However, most existing RUL prediction methods …