Fault diagnosis of mine ventilator based on improved BP neural network

In view of characteristics of complicated correlation of mine ventilator failure and symptom, a fault diagnosis method using BP neural network optimized by dynamic adaptation cuckoo search algorithm was proposed. The optimal initial parameters of neural network are solved by using global search ability of dynamic adaptation cuckoo search algorithm. Then, the BP neural network is trained to obtain the final fault diagnosis model. The example analysis results show that the method can effectively achieve fault diagnosis of mine ventilator and has the characteristics of fast convergence and high precision, and the diagnosis accuracy of the test sample is 92.5%.