Authors:
Oriane Le Pocher
1
;
Eric Duviella
1
and
Karine Chuquet
2
Affiliations:
1
Univ. Lille Nord de France and EMDouai, France
;
2
VNF - Service de la navigation du Nord Pas-de-Calais, France
Keyword(s):
Supervision, Fault detection, Classification algorithm, Large scale system, Hydraulic system.
Related
Ontology
Subjects/Areas/Topics:
Business Analytics
;
Change Detection
;
Data Engineering
;
Environmental Monitoring and Control
;
Informatics in Control, Automation and Robotics
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Surveillance
Abstract:
This paper focuses on the sensor fault detection of a hydraulic channel used for navigation. This system has the particularities to have large scale dimension, without slope, with several inputs and ouputs, and thus difficult to be modelled according to classical modelling methods. For recent years, it was equipped with level sensors in order to have better knowwledge of its behavior, to detect its state online and thus improve its management. However, level sensors are subjected to measurement or transmission errors, setting errors, and quick or slow drifts. In order to detect these sensor errors, a classification approach is proposed. It appears adapted to the fault detection of large scale hydraulic systems without model. The classification approach is used on data measured from 2006 to 2009. The first results and analysis show that the classification method is effective for addressing the problem of sensor fault detection.