Leakage detection and localization method for pipelines in complicated conditions
It is difficult to detect leakage for oil pipelines in complicated conditions. For solving the difficulty a method based on SVM (Support Vector Machine) is proposed and the diagnosis model was established. Model train can be completed in few samples to distinguish different conditions of pipelines. The experimental result demonstrates it was effective in the classification with a few samples and the correct rate increased more greatly compared with traditional BP method. Moreover, in hot pipelines pressure velocity is affected by oil and pipeline axial temperature drop. Location usually has obvious error. For solving this problem axial temperature drop was analyzed and pressure velocity was revised. By means of Newton-Cotes integration method location formula was improved. The field experiments show that the improved located formula made location accuracy increased from 2.5% to 1.0%.