Abstract
To improve the quality of the pre-baked anode products, it is necessary to check the internal cracks of the pre-baked anode. The developed automatic knocking device is used to determine whether there are cracks in the pre-baked anode by using the obtained sound signal of the pre-baked anode knocking and machine learning. In this paper, first, the Fast Fourier Transform (FFT) is used to transform the sound signal to obtain 10 features in the frequency domain. Then, the main features of the pre-baked anode are obtained by principal component analysis (PCA). Next, the binary classification of support vector machine (SVM) is used to determine whether the pre-baked anode contains cracks, and finally, satisfactory results are obtained.
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