Fully mechanized mining face automatic coal drawing system based on neural network

The utility model relates to a fully mechanized mining face automatic coal drawing system based on a neural network. The fully mechanized mining face automatic coal drawing system based on the neural network comprises a neural network processor (1), an electrohydraulic control system (2) and a plura...

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Bibliographic Details
Main Authors FAN ZHIZHONG, XU GANG, MAO DEBING, SONG YONGBIN
Format Patent
LanguageEnglish
Published 21.01.2015
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Summary:The utility model relates to a fully mechanized mining face automatic coal drawing system based on a neural network. The fully mechanized mining face automatic coal drawing system based on the neural network comprises a neural network processor (1), an electrohydraulic control system (2) and a plurality of hydraulic support pressure monitoring devices (3), wherein the neural network processor (1) receives data signals transmitted by the hydraulic support pressure monitoring devices (3) and forms command signals, which are transmitted to the electrohydraulic control system (2); after the electrohydraulic control system (2) receives the command signals transmitted by the neural network processor (1) or given out by human, the electrohydraulic control system (2) controls a coal drawing port (44) of a fully mechanized mining face hydraulic support (4) to be opened and closed; the hydraulic support pressure monitoring devices (3) are installed on the fully mechanized mining face hydraulic support (4) and are used for monitoring the state of pressure born by the hydraulic support from top coal (5) in real time and transmitting the monitoring data to the neural network processor (1) in real time. The fully mechanized mining face automatic coal drawing system based on the neural network can effectively solve the problem of poor adaptability of the automatic coal drawing system to the change of top coal thickness and the difference of top coal roof conditions of the top coal of the hydraulic support.
Bibliography:Application Number: CN20142563894U