Results 11 to 20 of about 1,643,808 (218)

Adaptively Lightweight Spatiotemporal Information-Extraction-Operator-Based DL Method for Aero-Engine RUL Prediction [PDF]

Sensors, 2023
Accurate prediction of machine RUL plays a crucial role in reducing human casualties and economic losses, which is of significance. The ability to handle spatiotemporal information contributes to improving the prediction performance of machine RUL ...
Junren Shi, Jun Gao, Sheng Xiang
doaj   +3 more sources

A noval RUL prediction method for rolling bearing: TcLstmNet-CBAM [PDF]

Scientific Reports
Abstract Rolling bearings are pivotal components within rotating mechanical systems, and accurately predicting their remaining service life holds significant practical importance. This paper addresses issues prevalent in common deep learning methods for predicting remaining useful life (RUL), notably inadequate feature extraction and low ...
Qiang Liu, Zhengwei Dai, Hongxi Lai, Minghao Chen, Huiyuan Huang, Jiahui Fu, Mingxin Hou, Xiaoming Xu, Guangbin Wang, Jin Yan   +9 more
core   +5 more sources

A Lightweight Transformer Edge Intelligence Model for RUL Prediction Classification [PDF]

Sensors
Remaining Useful Life (RUL) prediction is a crucial task in predictive maintenance. Currently, gated recurrent networks, hybrid models, and attention-enhanced models used for predictive maintenance face the challenge of balancing prediction accuracy and model lightweighting when extracting complex degradation features.
Wang, Lilu, Li, Yongqi, Liu, Haiyuan, Liu, Taihui   +3 more
core   +5 more sources

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

A novel gear RUL prediction method by diffusion model generation health index and attention guided multi-hierarchy LSTM [PDF]

Scientific Reports
Gears, as indispensable components of machinery, demand accurate prediction of their Remaining Useful Life (RUL). To enhance the utilization of ordered information within time series data and elevate RUL prediction precision, this study introduces the ...
Xinping Chen
doaj   +2 more sources

FDBRP: A Data–Model Co-Optimization Framework Towards Higher-Accuracy Bearing RUL Prediction [PDF]

Sensors (Basel)
This paper proposes Feature fusion and Dilated causal convolution model for Bearing Remaining useful life Prediction (FDBRP), an integrated framework for accurate Remaining Useful Life (RUL) prediction of rolling bearings that combines three key ...
Xiaoyu Gao, Dongmei Qin, Qiang Liu, Qing Ye, Muyu Lin, Shiyue Na   +5 more
core   +2 more sources

Attention-Based Quantile Regression for RUL Uncertainty Prediction [PDF]

Sensors
Remaining Useful Life prediction is a core challenge in the field of Prognostics and Health Management. Traditional point prediction methods only provide a single estimate and cannot quantify prediction uncertainty, limiting their application in critical decision-making. This paper proposes a probabilistic prediction model integrating an LSTM–attention
Huang, Lin, Hu, Xianjun, Gong, Li, Liu, Yajie, Yang, Songlin   +4 more
openaire   +3 more sources

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers [PDF]

Advanced Science, Volume 13, Issue 56, 9 October 2026.
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.
Jingyuan Zhao, Misheng Cai, Zhenghong Wang, Yan Wang, Yuqi Li, Bo Dong, Hewu Wang, Andrew F. Burke, Simona Onori, Stephen J. Harris   +9 more
wiley   +2 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

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