Monitoring pipeline operating conditions is a vital component in pipeline safety and risk management. Although the SCADA system stores a large amount of operational data, the data lacks associated condition labels, making it difficult to mine. Furthermore, the operating circumstances of the multi-product pipeline vary often, and identification and monitoring by on-site employees are prone to error, so the pipeline’s operating conditions cannot be reliably identified. To address the aforementioned challenges, this chapter presents semi-supervised learning for operating condition identification. The findings show that semi-supervised learning has more stability and improved performance regardless of how the neural network is built. The suggested technique may be utilized as a decision-making tool for monitoring and identifying multi-product pipeline operating conditions.


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    Title :

    Operation Condition Monitoring for Pipeline


    Contributors:
    Su, Huai (editor) / Liao, Qi (editor) / Zhang, Haoran (editor) / Zio, Enrico (editor) / Zhang, Li (author)

    Published in:

    Publication date :

    2023-12-13


    Size :

    13 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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