DocumentCode :
3087681
Title :
Sensor fault detection and diagnosis in drinking water distribution networks
Author :
Bouzid, Sara ; Ramdani, Mohammed
Author_Institution :
Electron. Dept., Badji-Mokhtar Univ., Annaba, Algeria
fYear :
2013
fDate :
12-15 May 2013
Firstpage :
378
Lastpage :
383
Abstract :
In this work, the local PCA approach is used as a statistical process control tool for drinking water distribution(DWD) systems to detect and isolate sensor faults. The multivariate statistical process monitoring task is carried out by learning a finite mixture model to describe the local statistical behavior in each cluster, followed by the determination of the local statistical confidence limits. The objective of a water distribution system is to convey treated water to consumers through a pressurized network pipe. The aim is diagnosing sensor faults in DWD. Experimental results using a model of an actual water distribution network illustrate the effectiveness of the proposed approach.
Keywords :
fault diagnosis; pipelines; principal component analysis; process monitoring; sensors; statistical process control; water supply; water treatment; DWD; cluster; drinking water distribution network; finite mixture model; local PCA approach; local statistical confidence limit; multivariate statistical process monitoring task; pressurized network pipe; sensor fault detection; sensor fault diagnosis; sensor fault isolation; statistical process control tool; water treatment; Fault detection; Fault diagnosis; Mathematical model; Monitoring; Principal component analysis; Vectors; Water resources;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Signal Processing and their Applications (WoSSPA), 2013 8th International Workshop on
Conference_Location :
Algiers
Type :
conf
DOI :
10.1109/WoSSPA.2013.6602395
Filename :
6602395
Link To Document :
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