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[1]艾月乔　徐亮　陶江华　陈瑞波.基于人工蜂群算法优化的SVM管道风险评估[J].油气储运,2019,38(03):1-6.
　AI Yueqiao,XU Liang,TAO Jianghua,et al.An optimized SVM pipeline risk assessment method based on artificial bee colony algorithm [J].Oil & Gas Storage and Transportation,2019,38(03):1-6.
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基于人工蜂群算法优化的SVM管道风险评估

《油气储运》[ISSN:1000-8241/CN:13-1093/TE] 卷: 38 期数: 2019年03期 页码: 1-6 栏目: 出版日期: 2019-03-25
Title:
An optimized SVM pipeline risk assessment method based on artificial bee colony algorithm
 
作者:
艾月乔　徐亮　陶江华　陈瑞波
Author(s):
AI Yueqiao;  XU Liang;  TAO Jianghua;  CHEN Ruibo
关键词:
油气管道; 运行风险; 管道失效特征; 人工蜂群算法; SVM
Keywords:
oil and gas pipeline;  running risk;  pipeline failure feature;  artificial bee colony algorithm;  SVM
分类号:
TE832
文献标志码:
A
摘要:

管道风险评估是管道风险管理的重要组成部分，其目的是通过对风险的调查和分析，识别可能导致管道事故的重要因素，使得管道风险管理更加科学化。为了对管道日常运行状态风险进行准确评估，提出了一种利用人工蜂群算法优化的支持向量机（Support Vector Machine，SVM）管道安全风险评估方法：建立管道风险评估模型，从工艺运行角度收集了成品油管道、正反输原油管道、掺混输送原油管道的工艺运行特征，并形成了样本特征集合。对4种类型管道特征集合进行试验，结果表明：在小样本情况下，采用基于人工蜂群算法优化的SVM管道风险评估方法正确率较高，并具有良好的普遍适用性，能够根据管道实际运行状态给出正确的风险评估结果。

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更新日期/Last Update: 2019-03-29