摘要
针对多工况下管道泄漏检测数据处理量大、误报率较高的问题,提出了结合主成分分析和RBF神经网络的泄漏检测方法。在数据预处理基础上,计算管道压力序列的时域特征来降低数据处理量;对时域特征进行主成分分析降维,提取新的更能反映压力变化特性的综合特征;将综合特征作为RBF神经网络的输入、工况模式作为输出建立识别模型,进行管道泄漏检测。现场实验结果表明:该方法不仅减少了泄漏检测的数据处理量,提高了检测速度,而且能有效区分工况调节与管道泄漏,保证泄漏检测的识别率达100%。
This paper presents a leak detection method combining principal component analysis and RBF neural network in consideration of heavy data processing load and high false alarm rate for leak detection. On the basis of data preprocessing, the time domain features of pipeline pressure sequence is computed to reduce the data processing load, and the principal component analysis of the time-domain characteristics is conducted for dimensionality reduction to extract new integrated features that can better reflect the pressure change characteristics. Then, the recognition model is established for pipeline leak detection, with integrated features as the input and working conditions as the output of RBF neural network. The field experimental results show that this method not only reduces the data processing load and improve the detection speed, but also effectively distinguish working condition adjustment and pipeline leak, thereby ensuring the detection accuracy.
出处
《油气储运》
CAS
北大核心
2015年第7期759-763,共5页
Oil & Gas Storage and Transportation
关键词
管道
泄漏
工况识别
主成分分析
RBF
神经网络
pipeline
leakage
condition recognition
principal component analysis
RBF
neural network
