Abstract
This paper proposed a new fault-detection method based on the combination of Rough Set (RS) and Artificial Neural Network (ANN), called hybrid fault-detection method based on RS and ANN (HFDMRSNN), which uses RS to reduce parameters of a pipeline system and then uses ANN (three-layer neural network) to form a detection model. This method could detect fault of pipeline not only in stationary status but also in non-stationary status. The efficiency of the HFDMRSNN in detecting fault in real pipeline system is evaluated by an experiment in a long product oil pipeline in Shandong China. From the results, it is observed that the proposed HFDMRSNN is able to identify the status of complex pipeline effectively.
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© 2007 Springer Berlin Heidelberg
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Jinhai, L., Huaguang, Z., Jian, F., Heng, Y. (2007). A New Fault Detection and Diagnosis Method for Oil Pipeline Based on Rough Set and Neural Network. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_70
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DOI: https://doi.org/10.1007/978-3-540-72395-0_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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