Papers Remaining Useful Lifetime Estimation
“Remaining Useful Lifetime Estimation” 태그가 달린 논문 7편 · 필터 해제
PHMD: An easy data access tool for prognosis and health management datasets
This work introduces a comprehensive open-source Python library designed for seamless access and handling of Prognostics and Health Management (PHM) datasets. The library currently supports 59 datasets from diverse domai…
Anomaly DetectionFault DetectionFault DiagnosisManagement+3Conformal Prediction Intervals for Remaining Useful Lifetime Estimation
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 inc…
Conformal PredictionManagementPredictionPrediction Intervals+2Variational encoding approach for interpretable assessment of remaining useful life estimation
A new method for evaluating aircraft engine monitoring data is proposed. Commonly, prognostics and health management systems use knowledge of the degradation processes of certain engine components together with professio…
ManagementRemaining Useful Lifetime EstimationVariational InferenceKnowledge Informed Machine Learning using a Weibull-based Loss Function
Machine learning can be enhanced through the integration of external knowledge. This method, called knowledge informed machine learning, is also applicable within the field of Prognostics and Health Management (PHM). In …
BIG-bench Machine LearningManagementRemaining Useful Lifetime EstimationTime Series RegressionA stacked deep convolutional neural network to predict the remaining useful life of a turbofan engine
This paper presents the data-driven techniques and methodologies used to predict the remaining useful life (RUL) of a fleet of aircraft engines that can suffer failures of diverse nature. The solution presented is based …
Bayesian OptimizationModel SelectionRemaining Useful Lifetime EstimationA stacked DCNN to predict the RUL of a turbofan engine
This paper presents the data-driven techniques and methodologies used to predict the remaining useful life (RUL) of a fleet of aircraft engines that can suffer failures of diverse nature. The solution presented is based …
Bayesian OptimizationModel SelectionRemaining Useful Lifetime EstimationImproving Semi-Supervised Learning for Remaining Useful Lifetime Estimation Through Self-Supervision
RUL estimation suffers from a server data imbalance where data from machines near their end of life is rare. Additionally, the data produced by a machine can only be labeled after the machine failed. Semi-Supervised Lear…
Remaining Useful Lifetime Estimation