Abstract：Fan is one of the key equipment in coal mine safety production. However, the underground working conditions are harsh and the fan often fails, which affects the efficiency of coal mine safety production. Therefore, it is necessary to monitor the main fan in the mine. Taking Xinjing Mine as an example, aiming at the problem of frequent vibration faults of the main fan in the mine, the wavelet analysis technology is used to extract the fault feature signal, and the BP neural network control algorithm is used to train and track the fault, and the fault monitoring system is designed. The research shows that the BP neural network control algorithm can basically achieve the desired effect after 180 iterations, and the fault monitoring system can meet the requirements of fault recognition rate and ensure the safety of coal mine production.
