Classification of acoustic emission signal for fatigue crack opening and closure by principal component analysis

This study was performed to present the analyzing method for fatigue crack opening and closure for three kinds of aluminum alloy by principal component analysis (PCA). Fatigue cycle loading test was conducted on a MTS closed loop hydraulic loading machine in order to acquire AE signals which come from different source mechanism such as crack opening and closure, rubbing, fretting etc.. To extract the significant feature from AE signal, correlation analysis was performed. Over 94% of the variance of AE parameters could be accounted for in the first two principal components. The results of the PCA on AE parameters showed that the first principal component was associated with the size of AE signals and the second principal component was associated with shape of AE signals. An artificial neural network (ANN) analysis was successfully used to identify AE signals to six classes. The ANN classifier based on PCA might be a promising tool to analyze AE signals for fatigue crack opening and closure.