Author:
Jiang HaiYan,Zong DaShuai,Song QingJun,Gao KuiDong,Shao HuiZhi,Liu ZhiJiang,Tian Jing
Abstract
AbstractTraditional coal-gangue recognition methods usually do not consider the impact of equipment noise, which severely limits its adaptability and recognition accuracy. This paper mainly studies the more accurate recognition of coal-gangue in the noise site environment with the operation of shearer, conveyor, transfer machine and other device in the process of top coal caving. Mel Frequency Cepstrum Coefficients (MFCC) smoothing method was introduced to express the intrinsic feature of sound pressure more clearly in the coal-gangue recognition site. Then, a multi-branch convolution neural network (MBCNN) model with three branches was developed, and the smoothed MFCC feature was incorporated into this model to realize the recognition of falling coal and gangue in noisy environment. The sound pressure signal datasets under the operation of different device were constructed through a great deal of laboratory and site data acquisition. Comparative experiments were carried out on noiseless dataset, single noise dataset and simulated site dataset, and the results show that our method can provide higher correct recognition accuracy and better robustness. The proposed coal-gangue recognition approach based on MBCNN and MFCC smoothing can not only recognize the state of falling coal or gangue, but also recognize the operational state of site device.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shan-dong Provincial China
Shandong Province Key Laboratory of Mine Mechanical Engineering open fund
SDUST Research Fund
Innovation capability improvement project of scientific and technological small and medium-sized enterprises of Shandong Province China
Major special project of scientific and technological innovation of Tai'an City Shandong Province China
Publisher
Springer Science and Business Media LLC
Cited by
2 articles.
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