东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2009, Vol. 30 ›› Issue (3): 321-324.DOI: -

• 论著 • 上一篇    下一篇

基于压力时间序列的输油管道在线泄漏故障诊断算法

刘金海;张化光;冯健;   

  1. 东北大学信息科学与工程学院;
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2009-03-15 发布日期:2013-06-22
  • 通讯作者: Liu, J.-H.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60534010,60572070,60521003,60774048,60774093,60728307);;

On-line leak-detection method for pressure time series of oil pipeline

Liu, Jin-Hai (1); Zhang, Hua-Guang (1); Feng, Jian (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2009-03-15 Published:2013-06-22
  • Contact: Liu, J.-H.
  • About author:-
  • Supported by:
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摘要： 针对输油管道中的泄漏问题提出一种基于混沌特性的输油管道压力时间序列在线故障诊断算法.该算法通过重构时间序列的相空间,求得输油管道压力序列的嵌入维为5维,嵌入延迟为4.以5维重构向量作为神经网络模型的输入,先离线训练网络,得到初始参数,然后在线训练神经网络模型,实现网络模型权值在线调整,从而实现实时对故障信号的检测.通过对实测数据的仿真表明本算法可以检测压力故障,证明了本算法在实际中的有效性.

关键词: 输油管道, 压力时间序列, 泄漏故障诊断, 相空间重构, 神经网络

Abstract: An on-line leak-detection method based on pressure time series of oil pipeline is proposed for leakage. Reconstructing the phase-space of pressure time series of oil pipeline, the numbers of embedding dimensions and embedding computation delays are given as 5 and 4, respectively. Taking the 5-D reconstructing vectors as the input to the neural network model, the proposed method is used to train the network off-line so as to obtain the initial parameters and, then, train the model on-line to implement the on-line modification of the weighted values in the network model, thus realizing the real-time detection of leak. The datasets measured in simulation revealed that the method proposed is available to detect pressure faults efficiently.

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