PIPELINE DEFECT PREDICTION USING SUPPORT VECTOR MACHINES

Oil and gas pipeline condition monitoring is a potentially challenging process due to varying temperature conditions, harshness of the flowing commodity and unpredictable terrains. Pipeline breakdown can potentially cost millions of dollars worth of loss, not to mention the serious environmental damage caused by the leaking commodity. The proposed techniques, although implemented on a lab scale experimental rig, ultimately aim at providing a continuous monitoring system using an array of different sensors strategically positioned on the surface of the pipeline. The sensors used are piezoelectric ultrasonic sensors. The raw sensor signal will be first processed using the discrete wavelet transform (DWT) as a feature extractor and then classified using the powerful learning machine called the support vector machine (SVM). Preliminary tests show that the sensors can detect the presence of wall thinning in a steel pipe by classifying the attenuation and frequency changes of the propagating lamb waves. The SVM algorithm was able to classify the signals as abnormal in the presence of wall thinning.