A new approach to fault detection and isolation that combines Principal Component Analysis (PCA), Clustering and Pattern Recognition is presented. Single, multiple faults which may cause errors in the sensor readings and/or in the actuators as well as process faults are considered. Determination of the number of principal components is based on the statistical test ANOVA following the approach proposed by the authors in previous works. To overcome to the growth of complexity in the analysis of process faults that typically involve many variables, an automatic procedure for the isolation of the principal known faults has been developed. The proposed methodology which is based on Clustering and Pattern Recognition Analysis represents the new contribution of the present paper. The method is tested on experimental data from an IGCC (Integrated Gasification & Combined Cycle) section of an oil refinery plant to monitor a compression’s process. Results show the goodness and effectiveness of the proposed approach on process faults detection and isolation

An application of Hybrid automata for the MIMO model identification of a gasification plant / Zanoli, Silvia Maria; Barboni, L; Leo, Tommaso. - 2007:(2007), pp. 1427-1432. (Intervento presentato al convegno Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on tenutosi a Montreal nel 7-10 October 2007) [10.1109/ICSMC.2007.4413764].

An application of Hybrid automata for the MIMO model identification of a gasification plant

ZANOLI, Silvia Maria;LEO, TOMMASO
2007-01-01

Abstract

A new approach to fault detection and isolation that combines Principal Component Analysis (PCA), Clustering and Pattern Recognition is presented. Single, multiple faults which may cause errors in the sensor readings and/or in the actuators as well as process faults are considered. Determination of the number of principal components is based on the statistical test ANOVA following the approach proposed by the authors in previous works. To overcome to the growth of complexity in the analysis of process faults that typically involve many variables, an automatic procedure for the isolation of the principal known faults has been developed. The proposed methodology which is based on Clustering and Pattern Recognition Analysis represents the new contribution of the present paper. The method is tested on experimental data from an IGCC (Integrated Gasification & Combined Cycle) section of an oil refinery plant to monitor a compression’s process. Results show the goodness and effectiveness of the proposed approach on process faults detection and isolation
2007
9781424409914
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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/11566/42816
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 0
social impact