Results 21 to 30 of about 1,541,566 (221)
IEEE Access, 2020 Focusing on the fact that the existing research on optimal maintenance decision for remaining useful lifetime (RUL) prediction and imperfect maintenance has low accuracy of RUL prediction and rationality of decision results, an optimal maintenance ...
Yunxiang Chen, Zezhou Wang, Zhongyi Cai
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Sensors, 2022 The remaining useful life (RUL) prediction is important for improving the safety, supportability, maintainability, and reliability of modern industrial equipment.
Haitao Wang +3 more
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Energies, 2023 Lithium-ion batteries are a green and environmental energy storage component, which have become the first choice for energy storage due to their high energy density and good cycling performance.
Liyuan Shao +5 more
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IET Smart Grid, 2021 Accurate prediction of the remaining useful life (RUL) in Lithium‐ion batteries (LiBs) is a key aspect of managing its health, in order to promote reliable and secure systems, and to reduce the need for unscheduled maintenance and costs.
Mo'ath El‐Dalahmeh +3 more
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A New Model for Remaining Useful Life Prediction Based on NICE and TCN-BiLSTM under Missing Data
Machines, 2022 The Remaining Useful Life (RUL) prediction of engineering equipment is bound to face the situation of missing data. The existing methods of RUL prediction for such cases mainly take “data generation—RUL prediction” as the basic idea but are often limited
Jianfei Zheng +4 more
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Segmental Degradation RUL Prediction of IGBT Based on Combinatorial Prediction Algorithms
IEEE Access, 2022 Aiming at the segmentation nonlinear degradation characteristics of IGBT, the traditional single remaining useful lifetime (RUL) method has low accuracy. This paper proposes a method combining gray prediction and particle filter algorithm. The gray prediction model is used for slow degradation trends prediction in the early stage.
Linghui Meng 0002 +2 more
openaire +2 more sources
jiaxiang-cheng/PyTorch-CNN-for-RUL-Prediction: CNN for RUL Prediction
, 2021 Inspired by Babu, G. S., Zhao, P., & Li, X. L. (2016, April). Deep convolutional neural network-based regression approach for estimation of remaining useful life. In International conference on database systems for advanced applications (pp.
Jia-Xiang Cheng
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IEEE Access, 2020 The prediction of remaining useful life (RUL) of mechanical equipment provides a timely understanding of the equipment degradation and is critical for predictive maintenance of the equipment.
Jialin Li, David He
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Bearings RUL prediction based on contrastive self-supervised learning
, 2023 International audienceThis paper proposes a new contrastive self-supervised learning paradigm for bearing remaining useful life (RUL) prediction based on CNN-LSTM models.
Deng, Weikun +4 more
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Ensemble Neural Networks for Remaining Useful Life (RUL) Prediction [PDF]
, 2023 A core part of maintenance planning is a monitoring system that provides a good prognosis on health and degradation, often expressed as remaining useful life (RUL).
Andresen, Juan Carlos +5 more
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