Results 11 to 20 of about 1,541,566 (221)

A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon

Energies, 2019
Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of
Xiaoqiong Pang, Rui Huang, Jie Wen, Yuanhao Shi, Jianfang Jia, Jianchao Zeng   +5 more
doaj   +2 more sources

RUL prediction based on a new similarity-instance based approach [PDF]

2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), 2014
Prognostics is a major activity of Condition-Based Maintenance (CBM) in many industrial domains where safety, reliability and cost reduction are of high importance. The main objective of prognostics is to provide an estimation of the Remaining Useful Life (RUL) of a degrading component/ system, i.e.
Racha Khelif, Simon Malinowski, Brigitte Chebel-Morello, Noureddine Zerhouni   +3 more
openaire   +3 more sources

Different methods for RUL prediction considering sensor degradation

Reliability Engineering & System Safety, 2023
International ...
Hassan Hachem, Hai Canh Vu, Mitra Fouladirad   +2 more
openaire   +3 more sources

Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines

Algorithms
Remaining useful life (RUL) prediction is widely applied in prognostic and health management (PHM) of turbofan engines. Although some of the existing deep learning-based models for RUL prediction of turbofan engines have achieved satisfactory results ...
Zhaofeng Liu, Xiaoqing Zheng, Anke Xue, Ming Ge, Aipeng Jiang   +4 more
doaj   +2 more sources

Gear Health Monitoring and RUL Prediction Based on MSB Analysis [PDF]

, 2022
Gearbox is a key component in mechanical transmission and faults on gears will lead to breakdowns and unscheduled downtime. Health condition monitoring and remaining useful life (RUL) prediction can provide sufficient leading time for gearbox timely ...
Xu, Minmin, Gu, Fengshou, Chen, Xiaohui, Sun, Xiuquan, Han, Yaoyao, Ding, Xiaoxi   +5 more
core   +1 more source

Remaining Useful Life Prediction of Bearings Based on Convolution Attention Mechanism and Temporal Convolution Network

IEEE Access, 2023
The prediction of the remaining useful life (RUL) of bearings is of great significance for reducing cost and increasing efficiency of mechanical equipment and ensuring healthy operation.
Haitao Wang, Jie Yang, Ruihua Wang, Lichen Shi   +3 more
doaj   +1 more source

Performance-Complexity Trade-Offs in Battery Lifetime Prediction with Task-Aware Transformers. [PDF]

Adv Sci (Weinh)
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Zhao J, Cai M, Wang Z, Wang Y, Li Y, Dong B, Wang H, Burke AF, Onori S, Harris SJ.   +9 more
europepmc   +2 more sources

A Novel Remaining Useful Life Prediction Method Based on CEEMDAN-IFTC-PSR and Ensemble CNN/BiLSTM Model for Cutting Tool

IEEE Access, 2022
To accurately predict the remaining useful life (RUL) of cutting tool, a novel RUL prediction method is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose original cutting tool ...
Lanjun Wan, Keyu Chen, Yuanyuan Li, Yuezhong Wu, Zhibing Wang, Changyun Li   +5 more
doaj   +1 more source

A RUL prediction method of equipments based on MSDCNN-LSTM

, 2021
In order to solve the problems of high data dimension and insufficient consideration of time series correlation information, a multi-scale deep convolutional neural network and long-short-term memory (MSDCNN-LSTM) hybrid model is proposed for remaining ...

core   +2 more sources

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