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. (27th October 2008)

• Record Type: Journal Article
• Authors: Chetouani, Yahya
• Is Part Of: International journal of computer applications technology. Volume 32:Number 3(2008)
• Page Start: 187
• Page End: 193
• Publication Date: 2008-10-27
• Subjects: fault detection -- NARMAX model -- neural networks -- Page-Hinkley test -- process safety -- reactor exchanger -- nonlinear modelling -- reliability -- process dynamics; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

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.