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Real-Time Leak Detection for a Gas Pipeline Using a k-NN Classifier and Hybrid AE Features
2021
Sensors
This paper introduces a technique using a k-nearest neighbor (k-NN) classifier and hybrid features extracted from acoustic emission (AE) signals for detecting leakages in a gas pipeline. The whole algorithm is embedded in a microcontroller unit (MCU) to detect leaks in real-time. The embedded system receives signals continuously from a sensor mounted on the surface of a gas pipeline to diagnose any leak. To construct the system, AE signals are first recorded from a gas pipeline testbed under
doi:10.3390/s21020367
pmid:33430370
fatcat:5qd3wugdvzakpdpic2arqye7uu