Ultrasonic Sensor Data Processing using Support Vector Machines

Ultrasonic sensors are ideal for non-destructive testing due to its many advantages over conventional sensors. Oil and gas pipelines are an area which uses ultrasonic sensors for monitoring and detecting the presence corrosion and defects. The proposed techniques ultimately aims at providing a continuous monitoring system using an array of ultrasonic sensors strategically positioned on the surface of the pipeline to predict the occurrence of defects rather than just monitoring. 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. Key-WordUltrasonic Sensor, Pipeline, Support Vector Machines, Discrete wavelet transform