Ball, John E.
Date of Degree
Graduate Thesis - Open Access
Electrical and Computer Engineering
Master of Science (M.S.)
James Worth Bagley College of Engineering
Department of Electrical and Computer Engineering
Remaining useful life (RUL) prediction is a problem that researchers in the prognostics and health management (PHM) community have been studying for decades. Both physics-based and data-driven methods have been investigated, and in recent years, deep learning has gained significant attention. When sufficiently large and diverse datasets are available, deep neural networks can achieve state-of-the-art performance in RUL prediction for a variety of systems. However, for end users to trust the results of these models, especially as they are integrated into safety-critical systems, RUL prediction uncertainty must be captured. This work explores an approach for estimating both epistemic and heteroscedastic aleatoric uncertainties that emerge in RUL prediction deep neural networks and demonstrates that quantifying the overall impact of these uncertainties on predictions reveal valuable insight into model performance. Additionally, a study is carried out to observe the effects of RUL truth data augmentation on perceived uncertainties in the model.
Army Research Laboratory
Cornelius, Samuel J., "Uncertainty-aware deep learning for prediction of remaining useful life of mechanical systems" (2021). Theses and Dissertations. 5354.