The use of neural networks for the life prediction of insulating material of electric rotating machines

The life of the insulating systems of electric rotating machines is strongly dependent upon electrical and thermal features of the insulating material used. The subject of the diagnostic prediction is to specify the condition of insulation used. At present days, the most popular prediction tools are the methods of artificial intelligence, and one method is the neural networks. This paper is concentrated on the use of neural networks in the life prediction of Relanex insulating material that is applied as insulation of electrical machine windings. In this case the condition of insulating in a time step k+1 is predicated from input quantity in time steps k, k-1, k-2, etc. Anyway the prediction means forecasting of quantity in future from N previous measurement this or other quantities in the past. The first part of the paper describes the use of artificial neural networks which forecast the life of insulating material for windings in electric rotating, the description of train and test data and setting of neural network for prediction. The second part shows the simulation of insulating material behavior with neural networks. Tables evaluate the application of this tool for the solution of the problems investigated. We have used the above-mentioned neural networks for the prediction of insulating materials that were programmed in Matlab 6 environment.