摘要
为进一步改善输油管道泄漏的检测方法,概述了目前一些常用的输油管道泄漏检测方法,如直接检测法、负压波检测法和基于神经网络的检测方法等。分析了这些检测方法在应用时的优缺点。然而,随着对输油管道泄漏检测要求的提高,这些检测方法不能满足人们的要求,仍需要进一步改善。同时,将深度学习引入了输油管道的泄漏检测中。深度学习是在神经网络基础上的进一步发展,它在许多方面上的应用弥补了该应用基于神经网络方法存在的不足。其中,深度学习已经在图像和语音识别应用中取得了成功。这些情况为以后将深度学习应用于输油管道的泄漏检测提供了部分理论支持。
In order to improve the detection methods of oil pipeline leakage, this article briefly introduces some commonly used methods of oil pipeline leakage detection, such as direct detection methods, negative pressure wave detection method and neural network based detection method and so on. These methods have their advantages and disadvantages in application. However, with the improvement of oil pipeline leak detection requirements, these methods cannot meet people＇s requirements, still need to be further improved. Subsequently, we introduce the deep learning. Deep learning is the development on the basis of neural network and its application in many aspects makes up the deficiency of the application. Among them, deep learning has been successful in image and speech recognition applications. These facts provide some theoretical support for the subsequent application of deep learning in oil pipeline leak detection.
作者
安杏杏
董宏丽
张勇
邵晓光
代丽艳
AN Xingxing DONG Hongli ZHANG Yong SHAO Xiaoguang DAI Liyan(Institute of Complex Systems and Advanced Control Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China)
出处
《吉林大学学报（信息科学版）》
CAS
2017年第4期424-429,共6页
Journal of Jilin University（Information Science Edition）
基金
国家自然科学基金资助项目(61422301
61374127)
东北石油大学青年拔尖人才基金资助项目(rc201601)
东北石油大学研究生创新科研基金资助项目(YJSCX2016-026NEPU)
德国亚历山大.冯.洪堡基金资助项目
关键词
输油管道
泄漏检测
深度学习
oil pipeline
leak detection
deep learning
