Induction machine faults diagnosis by statistical neural networks with selection variables based on Principal component analysis

The early diagnosis of induction machine faults has become an exigency in all industrial process, as it reduces the breakdowns occurrence. Therefore, the importance of motor defect detection and their classification has recently result numerous research studies on the application of artificial neural network. In this paper, an effectual radial basis function neural networks (RBFN) and probabilistic neural networks (PNN) based method with dimensionality diminution is suggested for online motor current signal analysis. In order to minimize huge databases, Principal Component analysis is developed for selecting convenient training parameters. The classification performance comparison of RBFN and PNN is applied for the proposed method.