Results 271 to 280 of about 8,942,242 (366)

Remaining Useful Life Prediction of Lithium-Ion Battery With Adaptive Noise Estimation and Capacity Regeneration Detection

IEEE/ASME transactions on mechatronics, 2023
As an indispensable energy device, 18650 lithium-ion battery has widespread applications in electric vehicles. Remaining useful life (RUL) prediction of lithium-ion battery is critical for the normal operation of electric vehicles.
Jiusi Zhang, Yuchen Jiang, Xiang Li, Hao Luo, Shen Yin, O. Kaynak   +5 more
semanticscholar   +1 more source

A Parallel Hybrid Neural Network With Integration of Spatial and Temporal Features for Remaining Useful Life Prediction in Prognostics

IEEE Transactions on Instrumentation and Measurement, 2023
Prediction of remaining useful life (RUL) is an indispensable part of prognostics health management (PHM) in complex systems. Considering the parallel integration of the spatial and temporal features implicated in measurement data, this article proposes ...
Jiusi Zhang, Jilun Tian, Minglei Li, Jose I. Leon, L. Franquelo, Hao Luo, Shen Yin   +6 more
semanticscholar   +1 more source

Remaining Useful Life Prediction for Lithium-Ion Batteries With a Hybrid Model Based on TCN-GRU-DNN and Dual Attention Mechanism

IEEE Transactions on Transportation Electrification, 2023
The instability of lithium-ion batteries may result in system operation failure and cause safety accidents, thus predicting the remaining useful life (RUL) accurately is helpful for reducing the risk of battery failure and extending its useful life.
Lei Li, Yuanjiang Li, Runze Mao, Li Li, Wenbo Hua, Jinglin Zhang   +5 more
semanticscholar   +1 more source

Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach

IEEE transactions on industrial electronics (1982. Print), 2021
For prognostics and health management of mechanical systems, a core task is to predict the machine remaining useful life (RUL). Currently, deep structures with automatic feature learning, such as long short-term memory (LSTM), have achieved great ...
Zhenghua Chen, Min Wu, Rui Zhao, Feri Guretno, Ruqiang Yan, Xiaoli Li   +5 more
semanticscholar   +1 more source

Dynamic Model-Assisted Bearing Remaining Useful Life Prediction Using the Cross-Domain Transformer Network

IEEE/ASME transactions on mechatronics, 2023
Remaining useful life (RUL) prediction of rolling bearings is of paramount importance to various industrial applications. Recently, intelligent data-driven RUL prediction methods have achieved fruitful results.
Yongchao Zhang, Ke Feng, J. Ji, Kun Yu, Zhaohui Ren, Zheng Liu   +5 more
semanticscholar   +1 more source

Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction

IEEE Transactions on Industrial Informatics, 2021
Accurate prediction of remaining useful life (RUL) has been a critical and challenging problem in the field of prognostics and health management (PHM), which aims to make decisions on which component needs to be replaced when.
Meng Ma, Z. Mao
semanticscholar   +1 more source

A Synthetic Feature Processing Method for Remaining Useful Life Prediction of Rolling Bearings

IEEE Transactions on Reliability, 2023
In the context of industrial big data, the data-driven remaining useful life prediction for rolling bearings has been greatly developed. Aimed at the shortcomings of feature selection, feature fusion, and health state segment, this article proposes a ...
J. Mi, Lulu Liu, Yonghao Zhuang, L. Bai, Yanfeng Li   +4 more
semanticscholar   +1 more source