Gong-kuang zidonghua (Dec 2023)
Research on data-driven collaborative control method for mining and transportation in fully mechanized mining face
Abstract
Currently, research on the collaborative control of shearers and scraper conveyors has preliminarily established a collaborative control mechanism for mining and transportation systems. But none of them have taken into account the uncertainty and coupling features of factors that affect the stable operation of mining and transportation systems in unstructured fully mechanized mining face environments. And the coal flow state and scraper conveyor load current are affected by the underground electrical system and cannot truly reflect the changes in scraper conveyor load. In order to solve the above problems, a collaborative control method for mining and transportation in fully mechanized mining face based on scraper conveyor load current intensification and random self-attention capsule network (RSACNN) is proposed. Based on the electrical coupling features of the electric motor current of the scraper conveyor, a current intensification model is used to preprocess the original scraper conveyor current and obtain the current component that can reflect the real load of the coal flow system. There is a highly nonlinear and uncertain relationship between the operating state parameters of the mining and transportation system in the fully mechanized mining face and the traction speed of the shearer. It is difficult to establish an accurate mathematical model. In order to solve the above problem, based on capsule neural network (CNN), the features of fine-grained features such as sudden changes in the operating state of the mining and transportation system in the fully mechanized mining face can be preserved. A collaborative control model for mining and transportation in the fully mechanized mining face based on RSACNN is established. The verification results show that compared with the self-attention capsule neural network (SACNN) method and the CNN method, the proposed RSACNN method has higher precision in predicting the traction speed of the shearer. The fitting values between the predicted speed and the actual speed have increased by 0.032 05 and 0.075 04 respectively. The average absolute error decreases by 17.7% and 22.6% respectively. The average absolute percentage error decreases by 49.9% and 71.5% respectively. The root mean square error decreases by 13.3% and 34.6% respectively.
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