Fault diagnosis of rigid cage guide based on wavelet packet and BP neural network

In view of problems that existing fault diagnosis methods of rigid cage guide could not eliminate influences of environmental factors and low recognition rate of joint faults, a method of fault diagnosis of rigid cage guide based on wavelet packet and BP neural network was proposed in order to improve accuracy of identification of fault types of rigid cage guide. Experimental platform of lifting system of vertical shaft was set up to simulate two typical fault types of rigid cage guide including step protrusion and joint failure, and vibration acceleration signal of lifting vessel was collected. Wavelet packet decomposition was applied to carry out energy analysis and extract fault characteristic parameters. The fault characteristic parameters were taken as input of BP neural network, and a new test sample was selected to detect diagnostic effect of the neural network. The experimental results show that the method has high accuracy of fault identification, and the confidence level reaches to 0.91.