Research on Fault Diagnosis System of Crusher Based on BP Neural Network

Ore crushing is an important part of mining production technology. Aiming at the problems of the low efficiency of the crusher fault diagnosis and the difficulty of rapid and accurate diagnosis. This paper proposed a fault diagnosis method for crusher based on BP neural network. Building fault diagnosis model based on BP neural network. Training and optimizing the fault diagnosis model with known fault types and fault sample data. Finally, the optimized crusher fault diagnosis model is verified by test data. The results showed that, the fault diagnosis model based on BP neural network can effectively judge the fault status of the crusher in real time. It realizes the fault diagnosis mode with prevention as the main maintenance as the auxiliary. It satisfied the requirements of fault diagnosis for crusher. The experimental results showed that the method in this paper improves the actual productivity of mining production and ensure the stability of mine production.