Small-Sample Coal-Rock Recognition Model Based on MFSC and Siamese Neural Network

Given the advantages of deep learning in feature extraction and learning ability, it has been used in coal-rock recognition. Deep learning techniques rely on a large number of independent identically distributed samples. However, the complexity and variability of coal-rock deposit states make the dataset exhibit small sample characteristics, resulting in poor performance of deep learning model. To address this problem, this paper proposes a framework named MFSC-Siamese, which combines the advantages of log Mel-Frequency Spectral Coefficients (MFSC) and Siamese neural network. First, the MFSC is used to extract vibration signal features to preserve the information of the original signal as much as possible, which makes the extraction of vibration features more accurate. Second, a recognition model based on Siamese neural network is proposed to reduce the number of participants by sharing network branches, which achieves coal-rock recognition by learning the distance between sample features, closing the distance between similar samples and distancing the distance between dissimilar samples. To evaluate the effectiveness of the proposed method, a real vibration signal dataset was used for comparative experiments. The experimental results show that the proposed method has better generalization performance and efficiency, with accuracy up to 98.41%, which is of great significance for the construction of intelligent mines.