Design and Implementation of Online Monitoring and Diagnosis System for Mining Electrical Equipment
This study designed an online monitoring and diagnostic system for electrical equipment in non-metallic mines.The system was designed to perform real-time online monitoring of operational parameters for key electrical equipment and utilize deep learning models to analyze data patterns,achieving the diagnosis and early warning of potential faults.Ex-perimental results indicated that the system significantly improves the accuracy of anomaly monitoring and fault diagnosis for electrical equipment,reduces the risk of sudden faults,and thus enhances the reliability and safety of non-metallic mine production.