Neural network fault prediction and its application

In this paper, the forecasting algorithm employs wavelet function to replace sigmoid function in the hidden layer of Back-Propagation Neural Network. And a Wavelet Neural Network prediction model is established to predict Anode Effect (the most typical fault) through forecasting the change rate of cell resistance. The authors have developed forecasting software based on platform of Visual Basic 6.0. The simulation results show that the proposed method not only has greatly improved fault prediction precision and real-time, but also improved the operation efficiency. That means we can increase energy efficiency and the safety of aluminum production process.