Title: A non-linear auto-regressive moving average with exogenous input non-linear modelling and fault detection using the cumulative sum (Page-Hinkley) test: application to a reactor
Authors: Yahya Chetouani
Addresses: Departement Genie Chimique, Universite de Rouen, Rue Lavoisier, Mont Saint Aignan Cedex 76821, France
Abstract: In this paper, a real-time system for detecting changes in dynamic systems is designed. The Cumulative Sum or the Page-Hinkley test is intended to reveal any drift from the normal behaviour of the process. The process behaviour under its normal operating conditions is established by a reliable model. In order to obtain this reliable model for the process dynamics, the black-box identification by means of a Non-linear Auto-Regressive Moving Average with eXogenous input model has been chosen in this study. It is based on the neural network approach. This paper will show the choice and the performance of this neural network in the training and the test phases. A study is related to the input number, and of hidden neurons used and their influence on the behaviour of the neural predictor. Three statistical criterions are used for the validation of the experimental data. After describing the system architecture and the proposed methodology of the fault detection, we present a realistic application like a reactor in order to show the technique|s potential. The purpose is to detect the change presence, and pinpoint the moment it occurred.
Keywords: fault detection; NARMAX model; neural networks; Page-Hinkley test; process safety; reactor exchanger; nonlinear modelling; reliability; process dynamics.
International Journal of Computer Applications in Technology, 2008 Vol.32 No.3, pp.187 - 193
Published online: 26 Oct 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article