DocumentCode :
251322
Title :
Fault detection of Brahmanbaria Gas Plant using Neural Network
Author :
Sowgath, M.T. ; Ahmed, Shehab
Author_Institution :
Chem. Eng. Dept., BUET, Dhaka, Bangladesh
fYear :
2014
fDate :
20-22 Dec. 2014
Firstpage :
733
Lastpage :
736
Abstract :
In recent years, several accidents in pioneer gas processing industries led industries to put emphasis on real-time fault detection. Neural Network (NN) based fault (abnormal situation) detection technique played an important role in monitoring industrial safety. In this work, an attempt has been made to study the fault detection of Brahmanbaria gas processing plant using multi layered feed forward NN based system. NN based fault detection system is trained, validated and tested using data generated using the dynamic model. Preliminary results show that NN based method is able to detect the faults of Brahmanbaria Gas processing plant for fewer no of faults.
Keywords :
fault diagnosis; fuel processing industries; gas industry; industrial accidents; industrial plants; multilayer perceptrons; production engineering computing; safety; Brahmanbaria gas processing plant; NN based fault detection system; dynamic model; gas processing industries; industrial safety monitoring; multilayered feed forward NN based system; neural network; real-time fault detection; Artificial neural networks; Fault detection; Monitoring; Neurons; Poles and towers; Valves; Fault detection; GasProcessing Plant; Industrial Safety Management; Neural Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Computer Engineering (ICECE), 2014 International Conference on
Conference_Location :
Dhaka
Print_ISBN :
978-1-4799-4167-4
Type :
conf
DOI :
10.1109/ICECE.2014.7026933
Filename :
7026933
Link To Document :
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