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Prediction of Remaining Useful Life of Aero-engines Based on CNN-LSTM-Attention

International Journal of Computational Intelligence Systems
Accurately predicting the remaining useful life (RUL) of aircraft engines is crucial for maintaining financial stability and aviation safety. To further enhance the prediction accuracy of aircraft engine RUL, a deep learning-based RUL prediction method ...
Sizhe Deng, Jian Zhou
doaj   +1 more source

Bayesian Networks for Remaining Useful Life Prediction

PHM Society European Conference
Remaining useful life (RUL) prediction is a critical task in the field of condition-based maintenance. It is important to perform RUL prediction in a statistical sound way. However, it is not straightforward to properly combine multiple information sources about an asset, such as available statistics, measurements, derived features, and prior knowledge
Erik Hostens, Kerem Eryilmaz, Merijn Vangilbergen, Ted Ooijevaar   +3 more
openaire   +1 more source

Remaining useful life prediction of aeroengine based on Parallel-GraphSAGE-GRU

Jixie qiangdu
ObjectiveDeep learning-based remaining useful life (RUL) prediction of aeroengine provides a foundation for guaranteeing the reliability of the aeroengine.
GUO Wenwen, WANG Guohu, YUAN Yongliang, WANG Peiying, GUO Yinxiao   +4 more
doaj  

SiMBA-Augmented Physics-Informed Neural Networks for Industrial Remaining Useful Life Prediction

Machines
Remaining useful life (RUL) prediction of industrial equipment is critical for achieving safe operations and optimizing predictive maintenance. To tackle the limitations of poor interpretability, inaccurate predictions, and high computational cost in ...
Min Li, Jianfeng Qin, Haifeng Fan, Ting Ke   +3 more
doaj   +1 more source

Robust Prediction of Remaining Useful Life Using Physics-Guided Multi-Fidelity Approaches

IEEE Access
Data-driven approaches are known to have difficulty in predicting system’s response in the extrapolation region where no data are available.
Yoojeong Noh, Nam Ho Kim
doaj   +1 more source

Remaining useful life prediction of lithium-ion batteries based on FEEMD-LSTM-TAM-OKELM

AIP Advances
The Remaining Useful Life (RUL) of lithium-ion batteries is an essential indicator in battery management systems. Accurately predicting the RUL of lithium-ion batteries is beneficial for designing a reliable battery system, ensuring the safety and ...
Jingmei Yu, Yaoyang Cai, Yingxin Huang, Xinle Yang   +3 more
doaj   +1 more source

A Multi-Model Fusion Framework for Aeroengine Remaining Useful Life Prediction

Eng
As the core component of aircraft systems, aeroengines require accurate Remaining Useful Life (RUL) prediction to ensure flight safety, which serves as a key part of Prognostics and Health Management (PHM).
Bing Tan, Yang Zhang, Xia Wei, Lei Wang, Yanming Chang, Li Zhang, Yingzhe Fan, Caio Graco Rodrigues Leandro Roza   +7 more
doaj   +1 more source

Effective Latent Representation for Prediction of Remaining Useful Life

Computer Systems Science and Engineering, 2021
Qihang Wang, Gang Wu
openaire   +1 more source

Remaining useful life prediction of lithium-ion batteries: A decomposition-based multi-model prediction approach

AIP Advances
Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is critical to ensuring the reliability and safety of battery-powered systems.
Shuyi Xue, Lei Li
doaj   +1 more source

Mechanism-Informed Health Representation and Causal Stage-Aware Remaining Useful Life Prediction for Rolling Bearings. [PDF]

Sensors (Basel)
Tian X, Yin H, Chen S, Dong Z, Xu H.
europepmc   +1 more source