Pipeline Defects Detection and Classification Based on Distributed Fiber Sensors and Neural Networks

An integrated approach is presented to detect pipeline defects using distributed acoustic sensors and machine learning. Over 80% accuracy for defected pipe identification was achieved with defect depth classifications with error less than 1-mm.