管道(软件)
管道运输
计算机科学
启发式
人工神经网络
工程类
状态监测
风险分析(工程)
系统工程
可靠性工程
人工智能
医学
电气工程
环境工程
程序设计语言
作者
Yufeng Yang,Qiang Zhang,Xixiang Zhang,Shuyi Xie,Gang Wu,Lifeng Li
标识
DOI:10.1061/jpsea2.pseng-1490
摘要
As an important part of the energy transportation system, oil and gas pipelines are developing toward intelligence and digitalization. Vigorously developing and constructing pipeline networks based on intelligent methods such as big data and neural networks will help improve the efficiency of operation and management. This work provides an overview of recent developments of intelligent methods, including machine learning approaches, heuristic algorithms, and mathematical programming, used in the pipeline industry that can provide beneficial support for various applications enhancing operation and maintenance. Several aspects such as operating condition recognition, safety monitoring, pipeline remaining life prediction, and fault detection are reviewed and discussed. The study shows the need to focus on improving management level and ensuring pipeline safety.
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