Methods of Electromechanical Equipment Status Monitoring in Coal Mines Based on Deep Learning
In order to reduce the false dismissal rate of electromechanical equipment detection in coal mines,a method for electromechanical equipment status monitoring in coal mines based on deep learning is proposed.It ob-tains the information of electromechanical equipment status in coal mines,uses deep learning to extract the feature vector set of electromechanical equipment status monitoring in coal mines,fits the information of equipment status,carries out the group-based monitoring of electromechanical equipment status in coal mines,so as to achieve elec-tromechanical equipment status monitoring in coal mines.Experimental results demonstrate that the false dismissal rate of this method is within 0.2%,and most of them have a value of 0,which can timely and accurately alert abnor-mal states.
Deep learningMechanical and electrical equipment in coal minesCondition monitoring methodFalse dismissal rate
NETL
NSTL
万方数据
