Planners may focus on the health of the equipment using the condition-based maintenance technique and make recommendations based on the data collected. However, the effectiveness of the fault detection and diagnosis technique must be established first. The safety and dependability of oil and gas pipeline systems may be considerably improved by fault detection of pressurization devices. A deep learning-based fault detection and diagnosis approach is introduced in this chapter. The diagnostic accuracy for flaws of this technology, which combines time–frequency domain analysis and visual pattern recognition algorithms, can be above 95%.


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    Title :

    Fault Detection and Diagnose Method for Pressurization Devices


    Contributors:
    Su, Huai (editor) / Liao, Qi (editor) / Zhang, Haoran (editor) / Zio, Enrico (editor) / Fan, Lin (author) / Peng, Shiliang (author)

    Published in:

    Publication date :

    2023-12-13


    Size :

    17 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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