SENSOR FAULT DETECTION IN A REAL HYDRAULIC SYSTEM USING A CLASSIFICATION APPROACH Oriane Le Pocher, Eric Duviella Univ. Lille Nord de France, F-59000 Lille, France EMDouai, IA, F-59500 Douai, France Karine Chuquet VNF - Service de la navigation du Nord Pas-de-Calais, 37 rue du Plat, 59034 Lille Cedex, France Keywords: Supervision, Fault detection, Classification algorithm, Large scale system, Hydraulic system. 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. 1 INTRODUCTION Hydrographical networks are large scale systems characterized by nonlinear dynamics and varying time delays. They are used for several human activities, especially navigation and transport. In the northern Europe, navigation channels assure the transport of goods with the objective, within a few years, of ac- comodating large broad gauge boats. The control of the water level in navigation channels becomes cru- cial. In order to achieve this objective, sensor net- works have been implemented. These sensors allow the measurement of water levels or water discharges, and the implementation of level control algorithms for a local water management. At a larger scale, the level and discharge measurements are essential to provide an efficient water management of the navigation chan- nel networks, by mainly characterizing their state on- line. However, sensor networks are impacted by mea- sure errors, transmission faults, or drifts of operation. So, in order to improve the management of navigation channels, sensor fault detection techniques have to be employed. Fault Detection and Isolation (FDI) techniques are largely proposed in the litterature and employed by following a systematic approach. The first step consists in characterizing the operating modes of the system to be supervised. Several model-based ap- proaches were proposed (Frank et al., 2000), based on parameters identification technique (Weihua et al., 2003), parity equations method (Gertler, 1998), diag- nosis observers (Akhenak et al., 2004), or Kalman fil- ters (Xie et al., 1994). Even if these FDI techniques have proven to be as powerful and effective, they re- quire an accurate model of the system by minimizing the uncertainties and the process noise. Very recently, fault detection methods based on residual generation, extended Kalman filter and finite memory observer are proposed in (Bedjaoui and Weyer, 2010), in order to detect and localize leak in an irrigation network. This detection method is based on physical hydraulic system model, in particular on the Saint-Venant par- tial differential equations (Chow et al., 1998). How- ever, due to their physical characteristics, i.e. large dimensions, no slope, etc., navigation channels can not always be modelled using physical laws without requiring numeric models. In this way, traditionnal FDI techniques cannot reach the fault detection aims. When the physical modelling of the system is not realizable, pattern recognition techniques consti- 382 Le Pocher O., Duviella E. and Chuquet K.. SENSOR FAULT DETECTION IN A REAL HYDRAULIC SYSTEM USING A CLASSIFICATION APPROACH. DOI: 10.5220/0003571603820387 In Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics (ICINCO-2011), pages 382-387 ISBN: 978-989-8425-74-4 Copyright c  2011 SCITEPRESS (Science and Technology Publications, Lda.)