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    Fault Detection of Brahmanbaria Gas Plant using Neural Network

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    Publication date
    2014-12-22
    Author
    Sowgath, Md Tanvir
    Ahmed, S.
    Keyword
    Artificial neural networks; Fault detection; Poles; Towers; Monitoring; Valves; Neurons
    Peer-Reviewed
    Yes
    
    Metadata
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    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.
    URI
    http://hdl.handle.net/10454/10981
    Version
    No full-text in the repository
    Citation
    Sowgath 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.
    Link to publisher’s version
    http://dx.doi.org/10.1109/ICECE.2014.7026933
    Type
    Conference paper
    Collections
    Engineering and Informatics Publications

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