Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty [PDF]
Sensors, 2023 Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling,
Wenbo Wu +4 more
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CONFORMAL PREDICTION INTERVALS FOR REMAINING USEFUL LIFETIME ESTIMATION
International Journal of Prognostics and Health Management, 2023 The main objective of Prognostics and Health Management is to estimate the Remaining Useful Lifetime (RUL), namely, the time that a system or a piece of equipment is still in working order before starting to function incorrectly.
Alireza Javanmardi , Eyke Hullermeier
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Using temporal convolution network for remaining useful lifetime prediction [PDF]
Engineering Reports, 2021 Because Convolutional Neural Network (CNN) can extract spatial feature, while Long ShortâTerm Memory (LSTM) can learn temporal features, many methods combining CNN with LSTM are proposed for remaining useful lifetime prediction. In practice, it is better
Jungan Chen, Danjiang Chen, Gaoping Liu
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Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network [PDF]
Scientific Reports, 2023 Estimation of Remaining Useful Lifetime (RUL) of discrete power electronics is important to enable predictive maintenance and ensure system safety. Conventional data-driven approaches using neural networks have been applied to address this challenge ...
Zhonghai Lu +3 more
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Remaining Useful Lifetime Prediction Based on Extended Kalman Particle Filter for Power SiC MOSFETs [PDF]
Micromachines, 2023 Nowadays, the performance of silicon-based devices is almost approaching the physical limit of their materials, which have difficulty meeting the needs of modern high-power applications.
Wei Wu +4 more
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Shared Temporal Attention Transformer for Remaining Useful Lifetime Estimation
IEEE Access, 2022 This paper proposes a novel deep learning architecture for estimating the remaining useful lifetime (RUL) of industrial components, which solely relies on the recently developed transformer architectures.
Gavneet Singh Chadha +3 more
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Remaining useful lifetime prediction via deep domain adaptation [PDF]
Reliability Engineering and System Safety, 2020 In Prognostics and Health Management (PHM) sufficient prior observed degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most previous data-driven prediction methods assume that training (source) and testing (target) condition monitoring data have similar distributions.
Uzay Kaymak +2 more
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Controlling the Remaining Useful Lifetime using Self- Optimization
Chemical Engineering Transactions, 2013 Self-optimizing mechatronic systems offer possibilities well beyond those of traditional mechatronic systems. Among these is the adaptation of the system behavior to the current situation.
T. Meyer +3 more
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Remaining useful lifetime prediction for predictive maintenance in manufacturing [PDF]
Computers and Industrial Engineering, 2023 Traditional maintenance approaches often result in either premature replacement of machine parts or downtime in production lines due to malfunctions. Consequently, these lead to significant amount waste in material, time and, ultimately, money. In this study, a machine learning-based predictive maintenance approach is proposed to predict the Remaining ...
Serkan Ayvaz
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A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods [PDF]
Frontiers in Mechanical Engineering, 2021 Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue.
Shunli Wang +4 more
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