Abstract:

This study aimed to establish a quick method for non-destructive testing of cracked eggs. We firstly developed
a detection system for cracked eggs based on sweep frequency vibration of the magnetostrictive vibrator. The system was
based on the acoustic characteristics, and by Welch power spectrum analysis of vibration audio signal of eggs and extraction
of useful information in the feature vector through the principal component analysis (PCA), the detection model for egg
cracks was constructed based on generalized regression neural network (GRNN). A total of 290 eggs, including 200 eggs in
the training set and 90 eggs in the test set, were detected in this study. The results showed that the recognition rates of intact
eggs and cracked eggs reached 96.7% and 98.3%, respectively, in the test set. The research indicated the feasibility of using
the magnetostrictive vibrator sweep and Welch power spectrum analysis and extracting useful information in the feature
vector through PCA method coupled with GRNN neural network model for the detection of cracked eggs.

Key words: eggshell crack detection, magnetostriction, Welch power spectrum, principal component analysis (PCA), generalized regression neural network (GRNN)