FAULT DIAGNOSIS OF SCRAPER CONVEYOR REDUCER BASED ON IMPROVED FIREFLY ALGORITHM TO OPTIMIZE NEURAL NETWORK - MAO Jun; GUO Hao; CHEN HongYue (Jixie qiangdu)
In order to make accurate diagnosis for the scraper conveyor speed reducer failure study. This paper proposes a improved firefly algorithm to optimize neural network based fault diagnosis method. Firstly, the characteristics of the fault characteristic parameters of the blade conveyor are extracted. The second application feature data sample for fault diagnosis model based on neural network training. Using the improved firefly algorithm to optimize neural network weights and threshold, to speed up the optimum value of, get the optimal model of the network. Preliminary studies suggest that the improved firefly algorithm combined with BP（back propagation） neural network can effectively solve the neural network slow convergence speed, easily falling into the master problem, can make accurate diagnosis for the failure of scraper conveyor speed reducer.