Utilizing uncertainty information in remaining useful life estimation via Bayesian neural networks and Hamiltonian Monte Carlo

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung


  • Maximilian Benker - , Technische Universität München (Erstautor:in)
  • Lukas Furtner - , Technische Universität München (Zweitautor:in)
  • Thomas Semm - , Technische Universität München (Gemeinsame:r Letztautor:in)
  • Michael F. Zaeh - , Technische Universität München (Letztautor:in)


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.


Seiten (von - bis)799 - 807
FachzeitschriftJournal of manufacturing systems : an official journal of the Society of Manufacturing Engineers (SME)
PublikationsstatusVeröffentlicht - 7 Dez. 2020
Extern publiziertJa

Externe IDs

Scopus 85097471434


Ziele für nachhaltige Entwicklung


  • Prognostics and health management, Bayesian neural networks, Remaining useful life, Uncertainty quantification, C-MAPSS