Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline

Acoustic emission bursts are signal waveforms that include a number of consecutive imbrication transients with variable strengths and contain crucial information on the leakage phenomenon in a pipeline system. Detection and isolation of a burst against the background signal increases the ability of a pipe's fault diagnosis system. This paper proposes a methodology using the Enhanced Constant Fault Alarm Rate (ECFAR) to detect bursts and exploit the burst phenomenon in acoustic emission. The extracted information from the burst waveform is used to distinguish several levels of leakage in a laboratory leak-off experimental testbed. The multi-class support vector machine in the one-against-all method is established as the classifier. The results are compared with those of the wavelet threshold-based method, another algorithm utilized for impulse and burst detection, which indicates that the ECFAR method gives an ameliorative classification result with an accuracy of 93% for different levels of leakage.