Distributed model-based sensor fault diagnosis of marine fuel engines

N. Kougiatsos*, R.R. Negenborn, V. Reppa

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

1 Citation (Scopus)
32 Downloads (Pure)

Abstract

This paper proposes a distributed model-based methodology for the detection and isolation of sensor faults in marine fuel engines. The proposed method considers a Mean Value First Principle model and a wide selection of heterogeneous sensors for monitoring the engine components. The detection of faults is realised based on residuals generated using nonlinear Differential Algebraic estimators combined with adaptive thresholds. The isolation of faults is, then, realised in two levels; local sensor fault detection and isolation agents are designed to monitor specific sensor sets and aim to detect faults in these sets; and a global decision logic is designed to isolate multiple sensor faults that may be propagated between the local monitoring agents. Finally, simulation results are used to illustrate the application of this method and its efficiency.
Original languageEnglish
Pages (from-to)347-353
JournalIFAC-PapersOnLine
Volume55
Issue number6
DOIs
Publication statusPublished - 2022
Event11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2022 - Aliathon Resort, Pafos, Cyprus
Duration: 8 Jun 202210 Jun 2022
Conference number: 11
https://safeprocess2021.eu/

Funding

This research is supported by project READINESS with project number TWM.BL.019.002 of the research programme ”Topsector Water & Maritime: the Blue Route” which is partly financed by the Dutch Research Council (NWO).

Keywords

  • Distributed Fault Diagnosis
  • sensor faults
  • Differential-algebraic equations (DAE's)
  • nonlinear systems
  • Marine system modelling
  • interconnected systems

Fingerprint

Dive into the research topics of 'Distributed model-based sensor fault diagnosis of marine fuel engines'. Together they form a unique fingerprint.

Cite this