Authors:
Oussama Mustapha
1
;
Mohamad Khalil
1
;
Ghaleb Hoblos
2
;
Houcine Chafouk
2
and
Dimitri Lefebvre
3
Affiliations:
1
Lebanese University, Faculty of Engineering; Islamic University of Lebanon, Lebanon
;
2
ESIGELEC, IRSEEM, France
;
3
GREAH – University Le Havre, France
Keyword(s):
Signal processing, Filters Bank, Dynamic Cumulative Sum, Fault detection, Chemical processes.
Related
Ontology
Subjects/Areas/Topics:
Business Analytics
;
Change Detection
;
Data Engineering
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
Early fault detection, which reduces the possibility of catastrophic damage, is possible by detecting the change of characteristic features of the signals. The aim of this article is to detect faults in complex industrial systems, like the Tennessee Eastman Challenge Process, through on-line monitoring. The faults that are concerned correspond to a change in frequency components of the signal. The proposed approach combines the filters bank technique, for extracting frequency and energy characteristic features, and the Dynamic Cumulative Sum method (DCS), which is a recursive calculation of the logarithm of the likelihood ratio between two local segments. The method is applied to detect the perturbations that disturb the Tennessee Eastman Challenge Process and may lead the process to shut down.