Abstract
To intelligentize the top-coal caving’s process, many data-driven coal-gangue recognition techniques have been proposed recently. However, practical applications of these techniques are hindered by coal mine underground’s high background noise and complex environment. Considering that workers distinguish coal and gangue by hearing the impact sounds on the hydraulic support, we proposed a novel feature extraction method based on an auditory nerve (AN) response model simulating the human auditory system. Firstly, vibration signals were measured by an acceleration sensor mounted on the back of the hydraulic support’s tail beam, and then they were converted into acoustic pressure signals. Secondly, an AN response model of different characteristic frequencies was applied to process these signals, whose output constituted the auditory spectrum for feature extraction. Meanwhile, a feature selection method integrated with variance was used to reduce redundant information of the original features. Finally, a support vector machine was employed as the classifier model in this work. The proposed method was tested and evaluated on experimental datasets collected from the Tashan Coal Mine in China. In addition, its recognition accuracy was compared with other coal-gangue recognition methods based on commonly used features. The results show that our proposed method can reach a superior recognition accuracy of 99.23% and presents better generalization ability.
Highlights
There are about 1341 billion tons of proven coal resources in China, of which thick coal seam accounts for 44 percent [1]
As compared with the mean of mel-frequency cepstrum coefficient (MFCC), a higher which recognition accuracy is achieved based auditory spectrum features, which can be explained by the consideration of more auditory characteristics in the auditory nerve (AN) response model
The method was conducted on the same datasets and classes as in the proposed method
Summary
There are about 1341 billion tons of proven coal resources in China, of which thick coal seam accounts for 44 percent [1]. (1) Inspired by the important roles of auditory in coal-gangue recognition during an actual top-coal caving process, we proposed an original feature extraction method integrated with an auditory nerve (AN) response model and auditory spectrum. In this method, the AN response model was used to process coal and gangue’s acoustic pressure signals, and each characteristic frequency of such model’s output constituted the auditory spectrum. (3) Through the analysis of the AN-based support vector machine (SVM) recognition results and comparison experiments, we demonstrated that more accuracy and generality than traditional extracting feature methods on the basis of signal processing have been achieved in this proposed method.
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