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
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