This paper proposes a novel distributed fault detection and isolation approach for the monitoring of non linear large-scale systems. The proposed architecture considers stochastic characterization of the measurement noises and modeling uncertainties, computing at each step stochastic timevarying thresholds with guaranteed false alarms probability levels. The convergence properties of the distributed estimation are demonstrated. A novel fault isolation method is proposed basing on a Generalized Observer Scheme, providing guaranteed error probabilities of the fault exclusion task. A consensus approach is used for the estimation of variables shared among more than one subsystem; a method is proposed to define the time-varying consensus weights in order to minimize at each step the variance of the uncertainty of the fault detection and isolation thresholds. Detectability and isolability conditions are provided.

Distributed Model-Based Fault Diagnosis with Stochastic Uncertainties

PARISINI, Thomas
2015-01-01

Abstract

This paper proposes a novel distributed fault detection and isolation approach for the monitoring of non linear large-scale systems. The proposed architecture considers stochastic characterization of the measurement noises and modeling uncertainties, computing at each step stochastic timevarying thresholds with guaranteed false alarms probability levels. The convergence properties of the distributed estimation are demonstrated. A novel fault isolation method is proposed basing on a Generalized Observer Scheme, providing guaranteed error probabilities of the fault exclusion task. A consensus approach is used for the estimation of variables shared among more than one subsystem; a method is proposed to define the time-varying consensus weights in order to minimize at each step the variance of the uncertainty of the fault detection and isolation thresholds. Detectability and isolability conditions are provided.
2015
9781479978854
9781479978861
http://ieeexplore.ieee.org/document/7402918/
File in questo prodotto:
File Dimensione Formato  
Boem_Parisini_CDC2015.pdf

Accesso chiuso

Descrizione: pdf editoriale
Tipologia: Documento in Versione Editoriale
Licenza: Digital Rights Management non definito
Dimensione 231 kB
Formato Adobe PDF
231 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2851526
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 4
social impact