This chapter addresses the problem of the identification of both linear and nonlinear dynamic systems for fault diagnosis. In the case of nonlinear dynamic systems, the identification will be performed by exploiting parametric nonlinear models, such as affine, piecewise-affine and fuzzy models. Using the concepts of model-based fault detection, the design of a residual generator based on a fuzzy model of a nonlinear dynamic process is addressed. The chapter also addresses the decomposition of a nonlinear identification problem into a set of locally linear models by means of product space fuzzy clustering. The identification algorithm exploited to estimate the parameters and orders of the local affine submodels is based on the well-established Frisch Scheme method for linear systems. A set of optimal parameters with respect to the model output can also be estimated from the identification data set by using the ordinary least-squares methods.

Data‐Driven Methods for Fault Diagnosis

Simani, Silvio
Primo
Writing – Original Draft Preparation
2021

Abstract

This chapter addresses the problem of the identification of both linear and nonlinear dynamic systems for fault diagnosis. In the case of nonlinear dynamic systems, the identification will be performed by exploiting parametric nonlinear models, such as affine, piecewise-affine and fuzzy models. Using the concepts of model-based fault detection, the design of a residual generator based on a fuzzy model of a nonlinear dynamic process is addressed. The chapter also addresses the decomposition of a nonlinear identification problem into a set of locally linear models by means of product space fuzzy clustering. The identification algorithm exploited to estimate the parameters and orders of the local affine submodels is based on the well-established Frisch Scheme method for linear systems. A set of optimal parameters with respect to the model output can also be estimated from the identification data set by using the ordinary least-squares methods.
2021
978-1-78945-058-3
9781119882329
Fault diagnosis, fault tolearant control, wind turbine systems
File in questo prodotto:
File Dimensione Formato  
Ch_6_Data_Driven_Methods_for_FDI.pdf

solo gestori archivio

Descrizione: Pre-print
Tipologia: Pre-print
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 805.01 kB
Formato Adobe PDF
805.01 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
2471088.pdf

solo gestori archivio

Descrizione: versione editoriale
Tipologia: Full text (versione editoriale)
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 3.86 MB
Formato Adobe PDF
3.86 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in SFERA 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/11392/2471088
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact