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

Maximilian Benker, Lukas Furtner, Thomas Semm, Michael F. Zaeh

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)799-807
Number of pages9
JournalJournal of Manufacturing Systems
Volume61
DOIs
StatePublished - Oct 2021

Keywords

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

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