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Classification of Acoustic Emission Signals for Fatigue Crack Opening and Closure by Artificial Neural Network Based on Principal Component Analysis  

Kim, Ki-Bok (Center for Safety Measurement, Korea Research Institute of Standards and Science)
Yoon, Dong-Jin (Center for Safety Measurement, Korea Research Institute of Standards and Science)
Jeong, Jung-Chae (Center for Safety Measurement, Korea Research Institute of Standards and Science)
Lee, Seung-Seok (Center for Safety Measurement, Korea Research Institute of Standards and Science)
Publication Information
Journal of the Korean Society for Nondestructive Testing / v.22, no.5, 2002 , pp. 532-538 More about this Journal
Abstract
This study was performed to classify the fatigue crack opening and closure for three kinds of aluminum alloy using principal component analysis (PCA). Fatigue cycle loading test was conducted to acquire AE signals which come from different source mechanisms 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 accounted for 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 the shape of AE signals. An artificial neural network (ANN) an analysis was successfully used to classify AE signals into six classes. The ANN classifier based on PCA appeared to be a promising tool to classify AE signals for fatigue crack opening and closure.
Keywords
acoustic emission; crack opening and closure; principal component analysis; artificial neural network; aluminum alloy;
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