摘要
The pressure signal in the lifting cylinder of the shearer is selected as feature signal, its mean-square deviation is extracted as the feature variable in this paper. The authors put forward a new method of recognizing the shearer’s cutting state based on pattern recognition. According to this, the completed controI software produced a satisfactory experiment result on the artificial longwall face in the laboratory, Finally the authors look forward to the prospect of the introduction of the artificial neural network theory into this field.
The pressure signal in the lifting cylinder of the shearer is selected as feature signal, its mean-square deviation is extracted as the feature variable in this paper. The authors put forward a new method of recognizing the shearer's cutting state based on pattern recognition. According to this, the completed controI software produced a satisfactory experiment result on the artificial longwall face in the laboratory, Finally the authors look forward to the prospect of the introduction of the artificial neural network theory into this field.
