Results 41 to 50 of about 1,541,566 (221)

Degradation Modeling and RUL Prediction of Hot Rolling Work Rolls Based on Improved Wiener Process. [PDF]

Materials (Basel)
Hot rolling work rolls are essential components in the hot rolling process. However, they are subjected to high temperatures, alternating stress, and wear under prolonged and complex working conditions. Due to these factors, the surface of the work rolls
Yan X, Zhou S, Zhang H, Yi C.
europepmc   +2 more sources

Rolling Bearing Health Indicator Extraction and RUL Prediction Based on Multi-Scale Convolutional Autoencoder

, 2022
Rolling bearings are some of the most crucial components in rotating machinery systems. Rolling bearing failure may cause substantial economic losses and even endanger operator lives.
Qiang Zhang, Zijian Ye, Yuwei Zhao, Tianlin Niu, Siyu Shao   +4 more
core   +1 more source

A Two-Stage Transfer Regression Convolutional Neural Network for Bearing Remaining Useful Life Prediction

Machines, 2022
Recently, deep learning techniques have been successfully used for bearing remaining useful life (RUL) prediction. However, the degradation pattern of bearings can be much different from each other, which leads to the trained model usually not being able
Xianling Li, Kai Zhang, Weijun Li, Yi Feng, Ruonan Liu   +4 more
doaj   +1 more source

Prediction sketch of the RUL of batteries.

, 2016
Prediction sketch of the RUL of batteries.
Kuei-Hsiang Chao (3121788), Lingling Li (427154), Yang Xie (46489), Pengchong Wang (3121785), Yatong Zhou (3121791)   +4 more
core   +1 more source

A Lithium-Ion Battery Capacity and RUL Prediction Fusion Method Based on Decomposition Strategy and GRU

, 2023
To safeguard the security and dependability of battery management systems (BMS), it is essential to provide reliable forecasts of battery capacity and remaining useful life (RUL).
Chaolong Zhang, Huihan Liu, Laijin Luo, Yanmei Li   +3 more
core   +1 more source

Three-stage feature selection approach for deep learning-based RUL prediction methods [PDF]

, 2023
The remaining useful life (RUL) prediction plays an increasingly important role in predictive maintenance. With the development of big data and the Internet-of-Things (IoT), deep learning (DL) techniques have been widely adopted for RUL prediction ...
Wang, Youdao, Zhao, Yifan, Youdao Wang, Yifan Zhao   +3 more
core   +1 more source

Rethinking RUL Prediction

Annual Conference of the PHM Society
Prognostics and Health Management (PHM) plays a key role in predicting the Remaining Useful Life (RUL) of systems, which is essential for enabling decision-making for Predictive Maintenance (PdM) and operations. While most research has traditionally focused on improving the accuracy of RUL predictions, this paper argues that four essential ...
Salinas-Camus, Mariana, Goebel, Kai, Eleftheroglou, Nick   +2 more
openaire   +2 more sources

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, Xianmeng Meng, Cuicui Cai, Yueqin Wang, Qijian Wang, Linglong Tan   +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, Rituparna Mohanty, Jack Leber, David Nageb, Mirella Richard, Sharan Singh, Amin Reihani   +6 more
wiley   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

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, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source