Remaining Useful Lifetime Estimation
6 papers with code • 2 benchmarks • 4 datasets
Estimating the number of machine operation cycles until breakdown from the time series of previous cycles.
Most implemented papers
Improving Semi-Supervised Learning for Remaining Useful Lifetime Estimation Through Self-Supervision
Previous work on SSL evaluated their approaches under unrealistic conditions where the data near failure was still available.
A 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.
A 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.
Knowledge Informed Machine Learning using a Weibull-based Loss Function
Machine learning can be enhanced through the integration of external knowledge.
Variational encoding approach for interpretable assessment of remaining useful life estimation
However, most of them lack an explanatory component to understand model learning and/or the nature of the data.
Conformal 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 incorrectly.