Online fault detection strategy applied to a binary component distillation. (1st January 2015)

• Record Type: Journal Article
• Authors: Chetouani, Yahya
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 7:Number 2(2015)
• Page Start: 136
• Page End: 145
• Publication Date: 2015-01-01
• Subjects: reliability -- anomaly detection -- normal behaviour -- modelling -- artificial neural networks -- ANNs -- online fault detection -- distillation column -- production safety -- production continuity -- residual generator design -- process behaviour; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• DOI: 10.1504/IJESMS.2015.068647
• Languages: English
• ISSNs: 1755-9758

Abstract

This paper deals with an early fault detection method in order to improve the safety and continuity of production. It consists of the design of a residual generator, which is based on a multi–layer perceptron artificial neural network to reveal any drift. Three statistical criteria are used for the validation of the experimental data. This study shows another technique for reduction of neural models into account the physical knowledge of the process. A distillation column is presented in order to illustrate the reliability of the prediction and model reduction. Satisfactory agreement between identified and experimental data is found and results show that the neural model successfully predicts the process behaviour. Then, the proposed FD method, which is based on the standardised residuals, is developed and tested on a real incident. The study shows that it detects the change presence, and pinpoints the moment it occurred.