Title :
Process fault detection and signal reconstruction using statistical and advanced techniques within the water industry
Author :
Fletcher, I. ; Adgar, A. ; Boehme, T.J. ; Cox, C.S.
Author_Institution :
Sch. of Eng. & Adv. Technol., Univ. of Sunderland, Sunderland, UK
fDate :
Aug. 31 1999-Sept. 3 1999
Abstract :
The efficient and robust operation of any industrial system is critically dependant upon the quality of the measurements made. This paper investigates techniques that are not only capable of identifying faulty sensors, but also of estimating values to permit continued plant operation. These techniques being of particular value when the luxury of sensor redundancy can not be afforded. A comparison between Statistical and Artificial Neural Network methodologies is presented and results of the most successful technique applied to data collected from a Water Treatment Plant are shown.
Keywords :
environmental science computing; fault tolerant computing; neural nets; sensors; signal reconstruction; statistical analysis; water treatment; ANN; advanced techniques; artificial neural network methodologies; faulty sensors; plant operation; process fault detection; sensor redundancy; signal reconstruction; statistical techniques; water industry; water treatment plant; Artificial neural networks; Data models; Fault detection; Principal component analysis; Process control; Sensors; Standards; Artificial Neural Networks; Fault detection; Principal Component Analysis; Water Treatment;
Conference_Titel :
Control Conference (ECC), 1999 European
Conference_Location :
Karlsruhe
Print_ISBN :
978-3-9524173-5-5
