Design and Implementation of Fault Detection and Prediction System for Mine Electromechanical Equipment
To enhance the safety level of coal production,this study has developed an intelligent monitoring system dedicated to fault detection and prediction for coal mine electromechanical equipment.Built upon industrial Ethernet,the system collects real-time data from various sensors and employs convolutional neural network algorithms to analyze equipment images to identify signs of faults.It also utilizes random forest models to estimate abnormal trends in critical components,aiming for early fault prediction.The system features three-dimensional virtual simulation technology to display the operational status of the equipment and issues warnings when necessary,has improved the efficiency of coal mine equipment monitoring and reduced the risk of production safety accidents.
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