摘要
基于管道泄漏产生的负压波波动本征参数较多,具有多种参数在不同工况下差异不明显的特点。对负压波信号进行一阶差分提取8种典型参数作为负压波信号的特征参数,采用主成分分析法对8种特征参数进行降维处理,使用得到的典型负压波信号降维特征参数训练得到需要的自组织映射神经网络。采用该网络对所有负压波工况样本的识别结果表明:该方法能够有效提取不同工况负压波数据的主要特征,进行管道泄漏识别,模型计算速度快、精度高。
Negative pressure waves generated by pipeline leakage may contain multiple intrinsic parameters, which present insignificant differences under different working conditions. Accordingly, first-order differences of negative pressure waves are deployed to extract 8 typical parameters as characteristic parameters of negative pressure wave signals. Principal component analysis is used to reduce dimensions of the 8 characteristic parameters. By using the resulting dimensionreduction features of the typical negative pressure wave signals, training can be made to generate required neural network with self-organized mapping. The network is used to identify samples of negative pressure waves under different working conditions. Relevant results show that the new system can effectively extract major features of negative pressure waves under different working conditions to recognize pipeline leakage. The model is characterized by fast computation and high accuracy.
出处
《油气储运》
CAS
北大核心
2015年第7期737-740,共4页
Oil & Gas Storage and Transportation
基金
国家自然科学基金资助项目"复杂腐蚀缺陷深海管道失效机理和安全评价"
51309236
关键词
管道泄漏
负压波
特征提取
主成分分析
自组织映射神经网络
pipeline leakage
negative pressure wave
feature extraction
principal component analysis
self-organized mapping neural network
