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dc.contributor.authorSowgath, Md Tanvir*
dc.contributor.authorAhmed, S.*
dc.date.accessioned2016-12-15T15:14:47Z
dc.date.available2016-12-15T15:14:47Z
dc.date.issued2014-12-22
dc.identifier.citationSowgath MT and Ahmed S (2014) Fault Detection of Brahmanbaria Gas Plant using Neural Network. In: Proceedings of the 8th International Conference on Electrical and Computer Engineering (ICECE). 20-22 Dec 2014, Pan Pacific Sonargaon, Dhaka, Bangladesh.en_US
dc.identifier.urihttp://hdl.handle.net/10454/10981
dc.descriptionNoen_US
dc.description.abstractIn 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.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttp://dx.doi.org/10.1109/ICECE.2014.7026933en_US
dc.subjectArtificial neural networks; Fault detection; Poles; Towers; Monitoring; Valves; Neuronsen_US
dc.titleFault Detection of Brahmanbaria Gas Plant using Neural Networken_US
dc.status.refereedYesen_US
dc.typeConference paperen_US
dc.type.versionNo full-text in the repositoryen_US


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