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• W2030407330 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." @default.
• W2030407330 created "2016-06-24" @default.
• W2030407330 creator A5067123750 @default.
• W2030407330 date "2014-05-01" @default.
• W2030407330 modified "2023-09-30" @default.
• W2030407330 title "Model selection and fault detection approach based on Bayes decision theory: Application to changes detection problem in a distillation column" @default.
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• W2030407330 doi "https://doi.org/10.1016/j.psep.2013.02.004" @default.
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