Aiming at the problems of single-point perception limitation

low fault diagnosis accuracy

and delayed early warning in the traditional monitoring methods of underground coal mine belt conveyors

an intelligent monitoring system for underground coal mine belt conveyors based on multi-sensor fusion technology and integrated with the mine disaster monitoring system is proposed.Based on cloud services and Browser/Server(B/S)architecture

the system achieves comprehensive collection of the belt conveyor’s operating parameters and environmental information by deploying various sensors such as vibration

temperature

and rotational speed.It adopts a three-level fusion strategy (data layer

feature layer

and decision layer) to improve data reliability

and combines a pre-trained AI fault diagnosis model to realize real-time monitoring and early warning of various faults of the belt conveyor.The system is applied to the belt conveyor in the main inclined shaft of Gaojialiang coal mine

and the results show that the problem recognition accuracy of the belt conveyor exceeds 90%.Experimental verification shows that the system can effectively improve the fault diagnosis accuracy and early warning timeliness

providing a strong guarantee for underground coal transportation in coal mines.