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

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 are obtained with the distributed optical fiber vibration sensor on the basis of Mach-Zehnder optical fiber interferometer theory. The wavelet packet threshold denoising is used to preprocess the signal. 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 detecting whether abnormal events happened along the pipelines.