Using neural networks and statistical tests for detecting changes in the process dynamics Online publication date: Tue, 08-Jul-2008
by Yahya Chetouani
International Journal of Modelling, Identification and Control (IJMIC), Vol. 3, No. 2, 2008
Abstract: In this paper, a real-time system for detecting changes in dynamic systems is designed. This study involves the determination and identification of black-box models, the design of a model-based residual generator and finally the evaluation of the residuals via two statistical information tests; the Page-Hinkley test and the Bayes classifier. It 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 NARX (Non-linear Auto-Regressive (AR) with exogenous input) model has been chosen in this study. It is based on the neural network approach. This paper shows also the choice and the performance of the neural network in the training and test phases. An analysis of the inputs number, hidden neurons and their influence on the behaviour of the neural predictor is carried out. Three statistical criteria, Aikeke's Information Criterion (AIC), Minimum Description Length (MDL) and Bayesian Information Criterion (BIC), 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 in order to show the technique's potential. The purpose is to detect the occurrence of change, and pinpoint the moment it occurred.
Online publication date: Tue, 08-Jul-2008
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com