New Feature for Evaluating Depth of Partial Decarburized Layer Based on Cepstrum Filter Banks

Abstract Decarburization refers to a phenomenon that the content of carbon in metal is reduced, and this could be advantageous or detrimental depending on the usage scenario. In this paper, a new feature, called Barkhausen frequency scale cepstrum coefficients (BFCCs), which was based on the cepstrum was proposed to evaluate the depth of the decarburized layer. The Genetic algorithm was used to optimize the parameters of the BFCCs feature. In the experiment part, the regression precision was compared under BFCCs and MBN envelope features using multiple linear regression (MLR) and BP neural network (BP). The regression experiment result was measured by mean absolute error (MAE) and shows the good representative ability of BFCCs for the depth of the decarburized layer.