Performance Analysis of Coal Gangue Recognition Based on Hierarchical Filtering and Coupled Wrapper Feature Selection Method

Coal gangue recognition of top coal caving is one of the important links in the process of intelligent coal mine construction. However, the recognition accuracy of this technology in practical application is still challenging, because the recognition model is not perfect in the following aspects: (1) the filtering scale is not suitable for signal noise reduction, and (2) the selected features have no obvious difference in vibration signals of coal and gangue, and (3) the overall relevance of the model input data to the target classification is insufficient. The purpose of this paper is to establish a coal gangue recognition model with effective filtering, feature extraction and classification capabilities, which can adaptively carry out purposive feature extraction while retaining relevant information to improve the recognition accuracy. Firstly, hierarchical filtering (HF) method was proposed. Secondly, an effective information correlation fusion based coal gangue recognition model (EICF-coal gangue recognition model) was established by coupling wrapper feature selection method and recognition algorithm. Then, 2223 groups of vibration impact tests were carried out on the coal gangue mixture with gangue content of 0 to 50%, and two kinds of coal gangue recognition sample sets of "caving category" and "shutdown category" were established. Finally, coal gangue recognition experiments were carried out on 9 hierarchical filter sample sets by coupling wrapper and 5 recognition algorithms. Under the combined effect of the HF method, wrapper feature selection method and Stacking, coal gangue recognition accuracy reaches 99%. This paper demonstrates the effectiveness of the EICF-coal gangue recognition model.