Non-destructive pipeline monitoring using intelligent fiber-optic acoustic sensor system

Detection of defects and damages due to aging and transient events are important contributors to pipeline accidents and monitoring them together is challenging. In this work, we demonstrate an intelligent fiber-optic acoustic sensor system for pipeline monitoring that enables real-time recognition, and classification of defects and transient threats together by analyzing the combined acoustic NDE data from the ultrasonic guidedwaves and acoustic emission methods. A 6"carbon-steel pipeline (16-ft long, SCH40) having multiple structural defects (weld and corrosion) is used with multiplexed optical fiber sensors as acoustic receivers attached to the pipe for ultrasonic GW monitoring to identifying structural defects and transient event (intrusion and impact) detection by the spontaneous acoustic emission method. Finally, we discussed our strategy to apply the convolutional neural network (CNN) model to the acoustic NDE data obtained by two methods to realize an accurate and automated pipeline health monitoring solution.