Gear fault diagnosis based on wavelet and neural network

By measuring experimentally the vibration signals of the gear system at different rotating speeds for different faults,the testing signals were obtained.The feature signals of system were analyzed using wavelet de-noising by threshold in different running conditions.Using discrete wavelet transform(DWT),the signals can be decomposed into eight detail components and an approximated component.After reconstruction of each level,their energy distributions were computed in order to extract the feature of the fault signals,these features were used for fault recognition using a neural network.The neural network was researched and analyzed.The results of using wavelet-neural network and neural network were also compared.Based on the research,it indicates that the feature of the denoised signal is superior to the original one.When dealing with various situations,such as the crack at the gear root,the crack at the gear’s reference circle and the wear abrasion fault of tooth surface,the performance rates are over 95%.The proposed method can be effectively used in engineering diagnosis of different faults of gear system.