基于支持向量机的油气管道安全监测信号识别方法

An SVM-Based Recognition Method for Safety Monitoring Signals of Oil and Gas Pipeline

针对基于经验风险最小化原则的传统学习方法的不足,提出了一种基于支持向量机的油气管道安全识别方法.通过基于Mach-Zehnder光纤干涉仪原理的分布式光纤振动信号传感器获取管道沿线振动信号,利用基于小波包分解的"能量-模式"方法提取振动信号的特征向量,并利用支持向量机根据振动信号的特征对其进行识别,判断管道沿线是否有异常事件发生.利用现场实验数据对该方法进行验证分析.结果表明,该方法识别正确率较高,实时性好.

An SVM-based recognition method for the safety of oil and gas pipeline was proposed due to limitation of the traditional learning methods based on empirical risk minimization. The vibration signals along the pipelines were obtained with the distributed optical fiber vibration sensor on the basis of Mach-Zehnder optical fiber interferometer theory. Then the eigenvectors of vibration signals were extracted through the energy-pattern method based on wavelet packet decomposition. At last the vibration signals were recognized by support vector machine （SVM） through the eigenvectors with a view to de- tecting whether abnormal events happened along the pipelines. The data obtained at the experimental site were used to evalu- ate the method, and the results show that the method is of high accuracy and excellent real-time performance.