Application of support vector machine and acoustic emission to fault diagnosis of gear crack

In order to correctly identify fault classes of gear crack,a gear crack fault diagnosis method was established based on time series analysis and support vector machine.Firstly,the AE signals from the normal and crack gears were analyzed through the time series analysis respectively.Then the AR model′s autoregressive coefficients were obtained which were inputs of support vector machine for neural networks training.Finally,the identification and diagnosis of gears in normal state,slight crack fault and severe crack fault states were accomplished.The experimental results indicate the methods based on time series analysis and support vector machine are effective for monitoring the gear crack fault.