Due to incipient fault features of gear being not obvious

a method based on time series analysis and radial basis function neural networks is proposed.First the vibratory signals in normal and fault states have been analyzed by time series analysis respectively

so state features can be extracted effectively by the time series model’s autoregressive coefficients.Then the autoregressive coefficients make up the eigenvectors which are taken as inputs for neural networks training.Consequently the identification and diagnosis of gears in different working conditions

such as normal

crack

gear tooth broken

and partial pitting etc.have been accomplished.The diagnosis result shows that the method based on time series analysis and RBF neural network is feasible for multiple or early fault classification.