Abstract
This paper introduces the safe situation of coal production and the current situation of data driven applications in coal mine safety early warning. Through the analysis of coal mine monitoring data and coal mine safety accidents, this paper presents a fault diagnosis method based on principal component method and BP neural network. The principal component method is used to extract the information of coal mine fault state from monitoring data. By establishing a BP neural network model, the extracted information is then used as fault sample input of the neural network. Research indicates, this fault diagnosis method takes the advantages on principal component method and BP neural network. Besides, it can effectively extract the coal mine fault state characteristics to achieve fault early warning.
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