煤矿机电设备故障检测与预测系统的设计与实现

Design and Implementation of Fault Detection and Prediction System for Mine Electromechanical Equipment

为了提高煤炭生产的安全水平,开发了面向煤矿机电设备的故障检测与预测的智能监控系统。该系统基于工业以太网构建,实时收集多种传感器数据,利用卷积神经网络算法分析设备图像,以识别故障迹象,结合随机森林模型估计关键部件的异常趋势,以提前预测故障。其采用三维虚拟仿真技术展示设备运行状况,并在必要时发出预警,提升了煤矿设备监测的效率,降低了生产安全事故的风险。

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.