Search Results for author:
Found 4 papers, 1 papers with code
A Benchmark on Uncertainty Quantification for Deep Learning Prognostics
The assessment includes RUL prediction accuracy, the quality of predictive uncertainty, and the possibility to break down the total predictive uncertainty into its aleatoric and epistemic parts.
Uncertainty-aware Remaining Useful Life predictor
Remaining Useful Life (RUL) estimation is the problem of inferring how long a certain industrial asset can be expected to operate within its defined specifications.
Real-Time Model Calibration with Deep Reinforcement Learning
The dynamic, real-time, and accurate inference of model parameters from empirical data is of great importance in many scientific and engineering disciplines that use computational models (such as a digital twin) for the analysis and prediction of complex physical processes.
Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders
With this work, we propose training a variational autoencoder (VAE) with labeled and unlabeled samples while inducing implicit supervision on the latent representation of the healthy conditions.
