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.
参考文献
| [1] | Dong, M.;Liu, C.;Li, G. .Robust Fault Diagnosis Based on Nonlinear Model of Hydraulic Gauge Control System on Rolling Mill[J].IEEE transactions on control systems technology: A publication of the IEEE Control Systems Society,2010(2):510-515. |
| [2] | 董敏,刘才.板带轧机HAGC系统基于小波变换的传感器故障诊断[J].钢铁研究学报,2006(12):54-58. |
| [3] | Z. K. Zhu;Ruqiang Yan;Liheng Luo;Z. H. Feng;F. R. Kong .Detection of signal transients based on wavelet and statistics for machine fault diagnosis[J].Mechanical Systems & Signal Processing,2009(4):1076-1097. |
| [4] | Z. K. Peng;F. L. Chu;Peter W. Tse .Singularity analysis of the vibration signals by means of wavelet modulus maximal method[J].Mechanical Systems & Signal Processing,2007(2):780-794. |
| [5] | 董敏,刘才.咬钢过程轧机液压压下系统动态建模与故障仿真[J].上海金属,2005(03):26-30. |
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