Study On The Rolling Mill'S Fault Diagnosis And Prediction Based On Chaos Phase Reconstruction

At present, more and more industry and mine corporations are constructed, the social and economic benefit bright out by the detection and diagnosis of the key equipment are more considered gradually. The change from prevention maintenance to prescient maintenance will be inevitable, it conforms the trend of machine fault's diagnosis.For time series, the phase reconstruction can recover some of the system's characteristics. Basing on it, nonlinear model of the power system can be created in order to do some prediction. Relying on the state of machine system before the moment that have been slected, the movement state after the moment can be predicted in the bound of the max prediction.First, chaos's complex characteristic and the method of identifying them are analysed. Depending on these analysis, a new method on the phase reconstruction of chaos is proposed: the appropriate delay time is determined using the mutual information function; the best embedding dimension is determined using CAO method.Second, the time series are calculated in an allowable bound using the Volterra progression, relying on the delay time and the embedding dimension through the method of phase reconstruction, so the prediction on the fault of equipment will be completed.Third, in order to solute the question on the weak fault signal of large machine, intermittent chaos of the Duffing oscillator is used to detect the weak fault signal buried in the strong noise surroundings. The method is applied to detect the fault of the rolling mill's rotors and gears and analysing the experiment result.At last, all theories are applied to predict the fault of speed-down machine: the characteristic signal contained in the predicted time series is detected by Duffing oscillator and the dignosis result is given using RBF nerve network. The prediction and analysis to machine fault state are completed.