A Wavelet and Neural Networks Based on Fault Diagnosis for HAGC System of Strip Rolling Mill

The fault diagnosis of HAGC (Hydraulic Gauge Control) system of strip rolling mill is researched. Taking the advantage of the accompanying characteristics of the closed-loop control system, rolling force forecasting model is built based on neural networks. The comparison results of the prediction and the actual signal are taken as residual signals. Wavelet transform is used to obtain the components of high and low frequency of the residual signal. Wavelet decomposition results make fault feature clear and time-domain positioning accurately. Fault numerical criterion is established through Lipschitz exponent. By analyzing the varied fault features which correspond to varied fault reasons, the fault diagnosis of HAGC system is implemented successfully.