A transfer-learning approach for corrosion prediction in pipeline infrastructures

نویسندگان

چکیده

Abstract Pipeline infrastructures, carrying either gas or oil, are often affected by internal corrosion, which is a dangerous phenomenon that may cause threats to both the environment (due potential leakages) and human beings accidents explosions in presence of leakages). For this reason, predictive mechanisms needed detect address corrosion phenomenon. Recently, we have seen first attempt at leveraging Machine Learning (ML) techniques field thanks their high ability modeling highly complex phenomena. In order rely on these techniques, need set data, representing factors influencing given pipeline, together with related supervised information, measuring level along considered infrastructure profile. Unfortunately, it not always possible access information for pipeline since costly time-consuming operation. paper, will problem devising ML-based model under assumption unavailable interest, while available some other pipelines can be leveraged through Transfer (TL) build itself. We cover all methodological steps from data creation usage TL. The whole methodology experimentally validated real-world pipelines.

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ژورنال

عنوان ژورنال: Applied Intelligence

سال: 2021

ISSN: ['0924-669X', '1573-7497']

DOI: https://doi.org/10.1007/s10489-021-02771-y