Sensor fault diagnosis of inland navigation system using physical model and pattern recognition approach

Abstract

Trabajo presentado al 19th IFAC World Congress celebrado del 24 al 29 de agosto de 2014 en Cape Town (Sudafrica).

In recent years, inland navigation networks benefit from the innovation of the instrumentation and SCADA systems. These data acquisition and control systems lead to a reactive asset-management of inland navigation networks. However, sensors and actuators are subject to faults due to the strong effects of the environment, aging, etc. In this paper, a sensor Fault Detection and Isolation (FDI) approach is proposed using an Integrator-Delay-Zero (IDZ) model, interval observers and the dynamic classification algorithm AUDyC. The combined use of these approaches allows the improvement of the sensor fault diagnosis. The proposed approach is introduced through the case study of the Cuinchy-Fontinettes reach in the north of France.

This work is a contribution to the GEPET’Eau project which is granted by the French ministery MEDDE - GICC, the French institution ORNERC and the DGITM

Peer Reviewed

Original document

The different versions of the original document can be found in:

• http://hdl.handle.net/10261/127389

• http://hdl.handle.net/2117/25018

• http://pdfs.semanticscholar.org/9c67/79193ad2e577f9ecd97c6e43a19835b3b644.pdf

• https://api.elsevier.com/content/article/PII:S1474667016424390?httpAccept=text/xml,

https://api.elsevier.com/content/article/PII:S1474667016424390?httpAccept=text/plain,

http://dx.doi.org/10.3182/20140824-6-za-1003.01548 under the license https://www.elsevier.com/tdm/userlicense/1.0/

• https://www.sciencedirect.com/science/article/pii/S1474667016424390,

http://www.iri.upc.edu/files/scidoc/1609-Sensor-fault-diagnosis-of-inland-navigation-system-using-physical-model-and-pattern-recognition-approach.pdf,

https://upcommons.upc.edu/handle/2117/25018,

https://digital.csic.es/handle/10261/127389,

http://digital.csic.es/bitstream/10261/127389/1/Pattern%20Recognition%20Approach.pdf,

https://digital.csic.es/bitstream/10261/127389/1/Pattern%20Recognition%20Approach.pdf,

https://academic.microsoft.com/#/detail/2047325171

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