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Model selection and fault detection approach based on Bayes decision theory: Application to changes detection problem in a distillation column.
- Source :
-
Process Safety & Environmental Protection: Transactions of the Institution of Chemical Engineers Part B . May2014, Vol. 92 Issue 3, p215-223. 9p. - Publication Year :
- 2014
-
Abstract
- The fault detection of industrial processes is very important for increasing the safety, reliability and availability of the different components involved in the production scheme. In this paper, a fault detection (FD) method is developed for nonlinear systems. The main contribution consists in the design of this FD scheme through a combination of the Bayes theorem and a neural adaptive black-box identification for such systems. The performance of the proposed fault detection system has been tested on a real plant as a distillation column. The simplicity of the developed neural model of normal condition operation, under all regimes (i.e. steady-state and unsteady state), used in this case is realised by means of a NARX (Nonlinear Auto-Regressive with exogenous input) model and by an experimental design. To show the effectiveness of proposed fault detection method, it was tested on a realistic fault of a distillation plant of laboratory scale. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09575820
- Volume :
- 92
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Process Safety & Environmental Protection: Transactions of the Institution of Chemical Engineers Part B
- Publication Type :
- Academic Journal
- Accession number :
- 96659457
- Full Text :
- https://doi.org/10.1016/j.psep.2013.02.004