Experimental study of metal corrosion and fatigue acoustic emission waveforms

Acoustic emission technology is a key strategy for improving the operational safety of petrochemical equipment in monitoring its corrosion and fatigue status. However, when implementing online monitoring, the acoustic emission waveform signal contains corrosion, fatigue, noise, and a huge amount of signal data, which make it extremely complicated to accurately distinguish between corrosion and fatigue-induced acoustic emission waveform signals. To address this problem, this study carried out corrosion and fatigue acoustic emission experiments respectively, used db4 wavelet decomposition in Matlab, and then proposed a new filtering processing method. This method successfully extracts the effective acoustic emission waveform signals in the corrosion and fatigue processes while greatly reducing the amount of data. Finally, it is concluded that the frequency of corrosion acoustic emission waveform is in the range of 70-80 kHz, and the main frequencies of fatigue waveform are in the three frequency bands of 50-90 kHz, 180-200 kHz, and 250-270 kHz. This provides a theoretical basis for analyzing acoustic emission monitoring fatigue and corrosion waveform data in engineering.