TY - JOUR
T1 - Utilizing uncertainty information in remaining useful life estimation via Bayesian neural networks and Hamiltonian Monte Carlo
AU - Benker, Maximilian
AU - Furtner, Lukas
AU - Semm, Thomas
AU - Zaeh, Michael F.
N1 - Publisher Copyright:
© 2020 The Author(s)
PY - 2021/10
Y1 - 2021/10
N2 - The estimation of remaining useful life (RUL) of machinery is a major task in prognostics and health management (PHM). Recently, prognostic performance has been enhanced significantly due to the application of deep learning (DL) models. However, only few authors assess the uncertainty of the applied DL models and therefore can state how certain the model is about the predicted RUL values. This is especially critical in applications, in which unplanned failures lead to high costs or even to human harm. Therefore, the determination of the uncertainty associated with the RUL estimate is important for the applicability of DL models in practice. In this article, Bayesian DL models, that naturally quantify uncertainty, were applied to the task of RUL estimation of simulated turbo fan engines. Inference is carried out via Hamiltonian Monte Carlo (HMC) and variational inference (VI). The experiments show, that the performance of Bayesian DL models is similar and in many cases even beneficial compared to classical DL models. Furthermore, an approach for utilizing the uncertainty information generated by Bayesian DL models is presented. The approach was applied and showed how to further enhance the predictive performance.
AB - The estimation of remaining useful life (RUL) of machinery is a major task in prognostics and health management (PHM). Recently, prognostic performance has been enhanced significantly due to the application of deep learning (DL) models. However, only few authors assess the uncertainty of the applied DL models and therefore can state how certain the model is about the predicted RUL values. This is especially critical in applications, in which unplanned failures lead to high costs or even to human harm. Therefore, the determination of the uncertainty associated with the RUL estimate is important for the applicability of DL models in practice. In this article, Bayesian DL models, that naturally quantify uncertainty, were applied to the task of RUL estimation of simulated turbo fan engines. Inference is carried out via Hamiltonian Monte Carlo (HMC) and variational inference (VI). The experiments show, that the performance of Bayesian DL models is similar and in many cases even beneficial compared to classical DL models. Furthermore, an approach for utilizing the uncertainty information generated by Bayesian DL models is presented. The approach was applied and showed how to further enhance the predictive performance.
KW - Bayesian neural networks
KW - C-MAPSS
KW - Prognostics and health management
KW - Remaining useful life
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85097471434&partnerID=8YFLogxK
U2 - 10.1016/j.jmsy.2020.11.005
DO - 10.1016/j.jmsy.2020.11.005
M3 - Article
AN - SCOPUS:85097471434
SN - 0278-6125
VL - 61
SP - 799
EP - 807
JO - Journal of Manufacturing Systems
JF - Journal of Manufacturing Systems
ER -