Affiliation:
1. School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
2. Key Laboratory of Intelligent Mining and Robotics, Ministry of Emergency Management, Beijing 100083, China
3. Inner Mongolia Research Institute, University of Mining and Technology (Beijing), Ordos 017004, China
Abstract
Coal–gangue sorting is a vital component of intelligent mine construction. As intelligent manufacturing continued to advance, data-driven coal–gangue recognition emerged as a prominent research topic. However, conventional data-driven methods for coal–gangue recognition heavily rely on expert-extracted features. The process of feature extraction is labor-intensive and significantly impacts the final outcome. Deep learning (DL) offers an effective approach to automatically extract features from raw data. Among the various DL techniques, convolutional neural networks (CNNs) have proven to be particularly effective. In this paper, we propose an intelligent method for recognizing coal–rock by fusing multiple preprocessing techniques applied to near-infrared spectra and employing dual attention. Initially, a signal-to-RGB image conversion method is applied to fuse three types of preprocessing data, namely first-order differential, second-order differential, and standard normal transform, into an RGB image representation. Subsequently, we propose a neural network model (CBAM-VIT) that integrates the convolutional block attention mechanism (CBAM) and Vision Transformer (VIT). When evaluated on the coal–rock dataset, this model achieves an accuracy of 98.5%, surpassing the performance of VIT (95.3%), VGG-16 (89%), and AlexNet (82%). The comparative results clearly demonstrate that the proposed coal–gangue recognition method yields significant improvements in classification outcomes.
Funder
Theory and Method of Excavation-Support-Anchor Parallel Control for Intelligent Excavation Complex System
National key research and development program
Green, intelligent, and safe mining of coal resources
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference31 articles.
1. Recent technological advances and problems in coal mine intelligence;Wang;Coal Sci. Technol.,2022
2. A review of coal rock identification technology for intelligent mining in coal mines;Zhang;Coal Sci. Technol.,2022
3. A coal rock identification method based on multi-strain data fusion of rocker pins in coal mining machines;Tian;J. Coal,2020
4. Liu, K., Zhang, X., and Chen, Y.Q. (2018). Extraction of coal and gangue geometric features with multifractal detrending fluctuation analysis. Appl. Sci., 8.
5. Multispectral imaging: A new solution for identification of coal and gangue;Hu;IEEE Access,2019