Works about REMAINING useful life
Results: 717
Identification of operational deflection shapes of a wind turbine gearbox using fiber-optic strain sensors on a serial production end-of-line test bench.
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- Wind Energy Science, 2025, v. 10, n. 1, p. 207, doi. 10.5194/wes-10-207-2025
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A MODELLING APPROACH FOR ASSET DEGRADATION: ADVANCING DIGITAL TWIN IN MAINTENANCE.
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- International Journal of Simulation Modelling (IJSIMM), 2025, v. 24, n. 1, p. 76, doi. 10.2507/IJSIMM24-1-715
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Framework for Addressing Imbalanced Data in Aviation with Federated Learning.
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- Information (2078-2489), 2025, v. 16, n. 2, p. 147, doi. 10.3390/info16020147
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- Article
A Comparative Analysis of Lithium-Ion Batteries Using a Proposed Electrothermal Model Based on Numerical Simulation.
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- World Electric Vehicle Journal, 2025, v. 16, n. 2, p. 60, doi. 10.3390/wevj16020060
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A Systematic Mapping Study on State Estimation Techniques for Lithium-Ion Batteries in Electric Vehicles.
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- World Electric Vehicle Journal, 2025, v. 16, n. 2, p. 57, doi. 10.3390/wevj16020057
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RUL Prediction for Lithium Battery Systems in Fuel Cell Ships Based on Adaptive Modal Enhancement Networks.
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- Journal of Marine Science & Engineering, 2025, v. 13, n. 2, p. 296, doi. 10.3390/jmse13020296
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An Improved Nonlinear Health Index CRRMS for the Remaining Useful Life Prediction of Rolling Bearings.
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- Actuators, 2025, v. 14, n. 2, p. 88, doi. 10.3390/act14020088
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Power Transformer Prognostics and Health Management Using Machine Learning: A Review and Future Directions.
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- Machines, 2025, v. 13, n. 2, p. 125, doi. 10.3390/machines13020125
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RUL Prediction of Rolling Bearings Based on Fruit Fly Optimization Algorithm Optimized CNN-LSTM Neural Network.
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- Lubricants (2075-4442), 2025, v. 13, n. 2, p. 81, doi. 10.3390/lubricants13020081
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- Article
Improved sand cat swarm optimization algorithm assisted GraphSAGE-GRU for remaining useful life of engine.
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- Scientific Reports, 2025, v. 15, n. 1, p. 1, doi. 10.1038/s41598-025-91418-w
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- Article
Intelligent Performance Degradation Prediction of Light-Duty Gas Turbine Engine Based on Limited Data.
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- Symmetry (20738994), 2025, v. 17, n. 2, p. 277, doi. 10.3390/sym17020277
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Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive Maintenance.
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- Sensors (14248220), 2025, v. 25, n. 4, p. 1218, doi. 10.3390/s25041218
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Multi-Harmonic Nonlinear Ultrasonic Fusion with Deep Learning for Subtle Parameter Identification of Micro-Crack Groups.
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- Sensors (14248220), 2025, v. 25, n. 4, p. 1152, doi. 10.3390/s25041152
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Mechanical element's remaining useful life prediction using a hybrid approach of CNN and LSTM.
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- Multimedia Tools & Applications, 2024, v. 83, n. 31, p. 75927, doi. 10.1007/s11042-024-18546-9
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Remaining useful life prediction based on spatiotemporal autoencoder.
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- Multimedia Tools & Applications, 2024, v. 83, n. 28, p. 71407, doi. 10.1007/s11042-024-18251-7
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A remaining useful life prediction method of SiC MOSFET considering failure threshold uncertainty.
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- IET Power Electronics (Wiley-Blackwell), 2024, v. 17, n. 12, p. 1594, doi. 10.1049/pel2.12611
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Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies.
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- Batteries, 2025, v. 11, n. 1, p. 31, doi. 10.3390/batteries11010031
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Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review.
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- Batteries, 2025, v. 11, n. 1, p. 17, doi. 10.3390/batteries11010017
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Repurposing Second-Life EV Batteries to Advance Sustainable Development: A Comprehensive Review.
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- Batteries, 2024, v. 10, n. 12, p. 452, doi. 10.3390/batteries10120452
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- Article
An AI-Driven Particle Filter Technology for Battery System State Estimation and RUL Prediction.
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- Batteries, 2024, v. 10, n. 12, p. 437, doi. 10.3390/batteries10120437
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Lithium-Ion Battery Life Prediction Using Deep Transfer Learning.
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- Batteries, 2024, v. 10, n. 12, p. 434, doi. 10.3390/batteries10120434
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Prediction of Lithium-Ion Battery Health Using GRU-BPP.
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- Batteries, 2024, v. 10, n. 11, p. 399, doi. 10.3390/batteries10110399
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Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data.
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- Batteries, 2024, v. 10, n. 11, p. 389, doi. 10.3390/batteries10110389
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Predicting the Future Capacity and Remaining Useful Life of Lithium-Ion Batteries Based on Deep Transfer Learning.
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- Batteries, 2024, v. 10, n. 9, p. 303, doi. 10.3390/batteries10090303
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Deep Learning Regression with Sequences of Different Length: An Application for State of Health Trajectory Prediction and Remaining Useful Life Estimation in Lithium-Ion Batteries.
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- Batteries, 2024, v. 10, n. 8, p. 292, doi. 10.3390/batteries10080292
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Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries.
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- Batteries, 2024, v. 10, n. 6, p. 204, doi. 10.3390/batteries10060204
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Empowering Electric Vehicles Batteries: A Comprehensive Look at the Application and Challenges of Second-Life Batteries.
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- Batteries, 2024, v. 10, n. 5, p. 161, doi. 10.3390/batteries10050161
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Battery Passports for Second-Life Batteries: An Experimental Assessment of Suitability for Mobile Applications.
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- Batteries, 2024, v. 10, n. 5, p. 153, doi. 10.3390/batteries10050153
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Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on the 1D CNN-BLSTM Neural Network.
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- Batteries, 2024, v. 10, n. 5, p. 152, doi. 10.3390/batteries10050152
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Aging in First and Second Life of G/LFP 18650 Cells: Diagnosis and Evolution of the State of Health of the Cell and the Negative Electrode under Cycling.
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- Batteries, 2024, v. 10, n. 4, p. 137, doi. 10.3390/batteries10040137
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Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries.
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- Batteries, 2024, v. 10, n. 3, p. 106, doi. 10.3390/batteries10030106
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Charge Scheduling of Electric Vehicle Fleets: Maximizing Battery Remaining Useful Life Using Machine Learning Models †.
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- Batteries, 2024, v. 10, n. 2, p. 60, doi. 10.3390/batteries10020060
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Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods.
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- Batteries, 2024, v. 10, n. 1, p. 10, doi. 10.3390/batteries10010010
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Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Iterative Transfer Learning and Mogrifier LSTM.
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- Batteries, 2023, v. 9, n. 9, p. 448, doi. 10.3390/batteries9090448
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Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Cubic Polynomial Degradation Model and Envelope Extraction.
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- Batteries, 2023, v. 9, n. 9, p. 441, doi. 10.3390/batteries9090441
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A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization.
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- Batteries, 2023, v. 9, n. 8, p. 414, doi. 10.3390/batteries9080414
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An Optimized Random Forest Regression Model for Li-Ion Battery Prognostics and Health Management.
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- Batteries, 2023, v. 9, n. 6, p. 332, doi. 10.3390/batteries9060332
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- Article
A Novel Fine-Tuning Model Based on Transfer Learning for Future Capacity Prediction of Lithium-Ion Batteries.
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- Batteries, 2023, v. 9, n. 6, p. 325, doi. 10.3390/batteries9060325
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- Article
A Lithium-Ion Battery Capacity and RUL Prediction Fusion Method Based on Decomposition Strategy and GRU.
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- Batteries, 2023, v. 9, n. 6, p. 323, doi. 10.3390/batteries9060323
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Transfer Learning Based on Transferability Measures for State of Health Prediction of Lithium-Ion Batteries.
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- Batteries, 2023, v. 9, n. 5, p. 280, doi. 10.3390/batteries9050280
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- Article
Battery Performance, Ageing, Reliability and Safety.
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- Batteries, 2023, v. 9, n. 5, p. 277, doi. 10.3390/batteries9050277
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A Multi-Stage Adaptive Method for Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Swarm Intelligence Optimization.
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- Batteries, 2023, v. 9, n. 4, p. 224, doi. 10.3390/batteries9040224
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- Article
Accurate Prediction Approach of SOH for Lithium-Ion Batteries Based on LSTM Method.
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- Batteries, 2023, v. 9, n. 3, p. 177, doi. 10.3390/batteries9030177
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A Review of Modern Machine Learning Techniques in the Prediction of Remaining Useful Life of Lithium-Ion Batteries.
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- Batteries, 2023, v. 9, n. 1, p. 13, doi. 10.3390/batteries9010013
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Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction.
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- Batteries, 2022, v. 8, n. 10, p. N.PAG, doi. 10.3390/batteries8100192
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SOC, SOH and RUL Estimation for Supercapacitor Management System: Methods, Implementation Factors, Limitations and Future Research Improvements.
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- Batteries, 2022, v. 8, n. 10, p. N.PAG, doi. 10.3390/batteries8100189
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Early Prediction of the Health Conditions for Battery Cathodes Assisted by the Fusion of Feature Signal Analysis and Deep-Learning Techniques.
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- Batteries, 2022, v. 8, n. 10, p. N.PAG, doi. 10.3390/batteries8100151
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Electric Vehicle Batteries: Status and Perspectives of Data-Driven Diagnosis and Prognosis.
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- Batteries, 2022, v. 8, n. 10, p. N.PAG, doi. 10.3390/batteries8100142
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- Article
风电跟踪调度计划时降低寿命损耗的 电池储能分组控制策略.
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- Electric Power Automation Equipment / Dianli Zidonghua Shebei, 2023, v. 43, n. 3, p. 54, doi. 10.16081/j.epae.202209016
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考虑综合能源聚合商与配电公司利益均衡的 配电网故障恢复方法.
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- Electric Power Automation Equipment / Dianli Zidonghua Shebei, 2023, v. 43, n. 3, p. 38, doi. 10.16081/j.epae.202209002
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