Abstract: To improve the operation and maintenance management level of coal mine electromechanical equipment, a supervision platform for coal mine electromechanical equipment based on Internet of Things technology was developed. This paper describes the system from the aspects of overall architecture, fault diagnosis methods, and software and hardware structure, and proposes a fault diagnosis model based on a BP neural network. A system architecture integrating multiple technologies and a layered hardware framework were established. The platform was deployed in a large coal mine, where 315 sensors were installed on 126 key pieces of electromechanical equipment for testing. The results show that the system features stable wireless communication signals, accurate equipment condition perception, and timely anomaly detection, effectively ensuring stable equipment operation and addressing problems in traditional management such as untimely equipment monitoring and difficulty in fault early warning.