Active incremental Support Vector Machine for oil and gas pipeline defects prediction system using long range ultrasonic transducers.

This work proposes a long range ultrasonic transducers technique in conjunction with an active incremental Support Vector Machine (SVM) classification approach that is used for real-time pipeline defects prediction and condition monitoring. Oil and gas pipeline defects are detected using various techniques. One of the most prevalent techniques is the use of "smart pigs" to travel along the pipeline and detect defects using various types of sensors such as magnetic sensors and eddy-current sensors.