Acoustic emission classification by cross-correlation


Acoustic emission signals specific to the incremental advance of fatigue cracks can be detected, cycle after cycle, during crack propagation tests. They were termed acoustic multiplets in reference to repeating earthquakes. Detecting such multiplets would give information about the fracturing process and provide early warnings when monitoring industrial parts in service. We developed a clustering method to extract multiplets signals from all signals and noise of fatigue tests. This method is based on the computation of the cross-correlation function between waveforms. On the distribution of the cross-correlation matrix, signals belonging to multiplets are clearly identified and permit to automatically clustered. This paper develops the steps of the method, presents results obtained on a dataset of a fatigue test and shows its noise robustness.
