https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
BAYESIAN INFORMATION FUSION FOR GAS TURBINES DIAGNOSTICS AND PROGNOSTICS
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0001-6101-2863
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-8466-356X
2023 (English)In: Proc. ASME Turbo Expo, American Society of Mechanical Engineers (ASME) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Prognosis, or the forecasting of remaining operational life of a component, is a fundamental step for predictive maintenance of turbomachines. While diagnostics gives important information on the current conditions of the engine, it is through prognostics that a suitable maintenance interval can be determined, which is critical to minimize costs. However, mature prognostic models are still lacking in industry, which still heavily relies on human experience or generic statistical quantifications. Predicting future conditions is very challenging due to many factors that introduce significant uncertainty, including unknown future machine operations, interaction between multiple faults, and inherent errors in diagnostic and prognostic models. Given the importance to quantify this uncertainty and its impact on operational decisions, this work presents an information fusion approach for gas turbine prognostics. Condition monitoring performed by a Bayesian network is fused with a particle filter for prognosis of gas turbine degradation, and the effect of diagnostic models uncertainty on the prognosis are estimated through probabilistic analysis. Gradual and rapid degradation are simulated on a gas turbine performance model and the impact of sensor noise and initial conditions for the particle filter estimation are assessed. This work demonstrates that the combination of Bayesian networks and particle filters can give good results for short-term prognosis.

Place, publisher, year, edition, pages
American Society of Mechanical Engineers (ASME) , 2023.
Keywords [en]
Bayesian inference, Information fusion, Particle filter, Prognostics, Condition monitoring, Gas turbines, Inference engines, Monte Carlo methods, Uncertainty analysis, Bayesia n networks, Bayesian information, Condition, Diagnostic model, Diagnostics and prognostics, Prognostic, Prognostic modeling, Uncertainty, Bayesian networks
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-64854DOI: 10.1115/GT2023-103171ISI: 001215570900021Scopus ID: 2-s2.0-85177194757ISBN: 9780791886977 (print)OAI: oai:DiVA.org:mdh-64854DiVA, id: diva2:1815495
Conference
ASME Turbomachinery Technical Conference and Exposition (Turbo Expo) on Collaborate, Innovate and Empower - Propulsion and Power for a Sustainable Future, Boston, June 26-30, 2023.
Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2024-07-31Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Zaccaria, ValentinaFentaye, Amare DesalegnKyprianidis, Konstantinos

Search in DiVA

By author/editor
Zaccaria, ValentinaFentaye, Amare DesalegnKyprianidis, Konstantinos
By organisation
Future Energy Center
Energy Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 58 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf