An Intelligent Motor Rotary Fault Diagnosis System Using Taguchi Method

This paper applies the Taguchi method to filter out the number of input neurons and increases the training efficiency of the dynamic structural neural networks. In order to avoid that omitting the harmonics may affect the fault diagnosis result, this work establishes an index for the fault identification which is based on the features of the first and second harmonics. Together with the identification results of dynamic structural neural network, the diagnosis can be done. The experimental results indicate the proposed method can reduce the iterations dramatically.