With increasing automation of NDE-measurements and the implied necessity to quickly scan through large amounts of data for the localisation of flaw candidates, there is an increasing demand for computer-based know-how. At NDT Systems & Services AG there is the demand to quickly analyse NDE-data that has been acquired by intelligent pigs used for in-line inspection of pipelines. Usually the problem is solved by finding defect candidates first using simple methods with minor discrimination power. In a second step a classification will distinguish between relevant flaws and irrelevant spurious signals. This is achieved by means of artificial intelligence. In the past the classification task has often been carried out by neural networks. This contribution will demonstrate how the application of Support Vector Machines (SVM) has considerably improved the handling and later work with such a learning machine. A comparison with neural networks is given. The improvements with continued training are demonstrated. The topic of retraining, i.e. the extension of the knowledge is discussed with special respect to the selection of training samples and their archiving. The successful application of the presented system will allow to speed up the analysis process for the generation of final and preliminary reports. The decision making will become reproducible and will be less dependent on human errors.


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    Title :

    The application of artificial intelligence for the analysis of data acquired by in-line inspection of pipelines


    Additional title:

    Der Einsatz von künstlicher Intelligenz zur Datenanalyse bei der in-line Prüfung von Pipelines


    Contributors:
    Reber, K. (author) / Willems, H. (author) / Barbian, A.O. (author) / Zoellner, M. (author) / Ziegemeyer, M. (author)


    Publication date :

    2004


    Size :

    8 Seiten, 5 Bilder, 1 Tabelle, 3 Quellen


    Remarks:

    (Nicht paginiert)


    Type of media :

    Conference paper


    Type of material :

    Storage medium


    Language :

    English




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