Investigation on the use of raw time series and artificial neural networks for flow pattern identification in pipelines

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dc.contributor.advisor Thompson, Chris en_UK Goudinakis, George en_UK 2005-11-23T14:33:31Z 2005-11-23T14:33:31Z 2004-03 en_UK
dc.description.abstract A new methodology was developed for flow regime identification in pipes. The method utilizes the pattern recognition abilities of Artificial Neural Networks and the unprocessed time series of a system-monitoring-signal. The methodology was tested with synthetic data from a conceptual system, liquid level indicating Capacitance signals from a Horizontal flow system and with a pressure difference signal from a S-shape riser. The results showed that the signals that were generated for the conceptual system had all their patterns identified correctly with no errors what so ever. The patterns for the Horizontal flow system were also classified very well with a few errors recorded due to original misclassifications of the data. The misclassifications were mainly due to subjectivity and due to signals that belonged to transition regions, hence a single label for them was not adequate. Finally the results for the S-shape riser showed also good agreement with the visual observations and the few errors that were identified were again due to original misclassifications but also to the lack of long enough time series for some flow cases and the availability of less flow cases for some flow regimes than others. In general the methodology proved to be successful and there were a number of advantages identified for this neural network methodology in comparison to other ones and especially the feature extraction methods. These advantages were: Faster identification of changes to the condition of the system, inexpensive suitable for a variety of pipeline geometries and more powerful on the flow regime identification, even for transitional cases. en_UK
dc.format.extent 1883 bytes
dc.format.extent 7569841 bytes
dc.format.mimetype text/plain
dc.format.mimetype application/pdf
dc.language.iso en_UK en_UK
dc.publisher Cranfield University en_UK
dc.subject.other Artificial neural networks en_UK
dc.subject.other Horizontal flow system en_UK
dc.title Investigation on the use of raw time series and artificial neural networks for flow pattern identification in pipelines en_UK
dc.type Thesis or dissertation en_UK
dc.type.qualificationlevel Doctoral
dc.type.qualificationname PhD
dc.publisher.department School of Engineering; Applied Mathematics and Computing Group

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