Abstract: Mining motors are subject to frequent failures due to large loads and harsh working environments,which seriously affect the stable production of coal mining enterprises. Motor failures are mostly sudden, making them difficult to detect in time. In order to help grasp the motor operating status and ensure the safe and stable operation of the motor,a remote intelligent online monitoring system for mining motors is designed for the need for the existing motor monitoring system design and function. The requirements of the distributed remote intelligent monitoring system for motors are analyzed,and the overall scheme of the system is designed,which contains three parts:the embedded motor online monitoring system, the fault diagnosis system and the data visualization platform. The embedded motor online monitoring system uses embedded-based circuitry to detect the main parts' motor voltage,current and temperature data,and to start timely protection when the system operation data exceeds the limit,thus preventing motor damage. The system integrates a display,an internal memory chip,and a data interface to display,store and send all kinds of motor data. The fault diagnosis system establishes a motor turn-to-turn short circuit fault diagnosis model by combining data on motor fault characteristics with the support vector machine (SVM) method. The model can diagnose whether the motor has an inter-turn short-circuit fault by using the motor operation data so that corresponding measures can be taken before the motor is completely damaged to reduce the loss caused by equipment damage and downtime. The data visualization platform is built on a remote server using a Web application. The platform communicates with the embedded motor monitoring system through the Internet and realizes real-time monitoring and fault diagnosis of motor status while realizing cloud backup of motor operation data.