摘要：Submarine pipelines play an important role in the oil and gas transportation. However, pipeline corrosion can cause the structural degradation of pipelines, which may lead to failure accident and environmental pollution. Pipeline degradation prediction based on monitoring data is of great significance for preventing corrosion failure. In this paper, an ensemble learning (EL) based approach integrated with multi-objective optimization is developed for pipeline corrosion degradation prediction. Firstly, the engineering theory and domain knowledge are integrated into feature engineering to improve the interpretability. Several new feature variables are constructed based on the corrosion mechanism and empirical models. Secondly, an improved multi-objective seagull optimization algorithm (IMOSOA) is proposed to optimize the hyper-parameters of EL model. Subsequently, a novel data-driven model, so-called theory-guided IMOSOA-EL is proposed for the corrosion rate prediction. And six benchmark functions are used to verify the optimization performance of IMOSOA. Then, different feature subsets are developed based on correlation analysis. A comprehensive evaluation indicator is proposed for the optimal feature subset selection. The results demonstrate that the proposed model presents superiority in prediction accuracy compared with other models. This study is significant for reliability assessment and maintenance decision-making of submarine pipelines.

关键字：Submarine pipeline degradation; Corrosion rate prediction; Theory-guided model; Improved multi-objective seagull optimization; algorithm; Ensemble learning

ISSN号：0951-8320

卷、期、页：卷243

发表日期：2024-03-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：一区

收录情况：SCI（科学引文索引印刷版）,SCIE（科学引文索引网络版）

发表期刊名称：RELIABILITY ENGINEERING & SYSTEM SAFETY

通讯作者：苗兴园

第一作者：赵弘

论文类型：期刊论文

论文概要：苗兴园,赵弘，Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model，RELIABILITY ENGINEERING & SYSTEM SAFETY，2024，卷243

论文题目：Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model