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

Research output: Contribution to journal › Research article › Contributed › peer-review

Contributors

  • Maximilian Benker - , Technical University of Munich (First author)
  • Lukas Furtner - , Technical University of Munich (Second author)
  • Thomas Semm - , Technical University of Munich (Joint last author)
  • Michael F. Zaeh - , Technical University of Munich (Last author)

Details

Original languageEnglish
Pages (from-to)799 - 807
JournalJournal of manufacturing systems : an official journal of the Society of Manufacturing Engineers (SME)
Volume2021
Issue number61
Publication statusPublished - 7 Dec 2020
Peer-reviewedYes
Externally publishedYes

External IDs

Scopus 85097471434

Keywords

Keywords

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