基于小波分析的汽轮机振动预测研究

针对电厂汽轮机转子振动时间序列的预测比较困难，提出采用小波分解实现趋势预测。小波分解将非平稳时间序列分解成多层近似意义上的平稳时间序列，采用自回归模型对分解后的时间序列进行预测，从而得到原始时间序列的预测值。以某电厂振动信号进行预测结果表明，该算法局部及整体效果优于神经网络模型预测法，验证了该模型对转子振动时间序列预测的精确性。

Steam turbine vibration prediction research based on wavelet analysis

As the power plant prediction of steam turbine rotor vibration time series is difficult, the wavelet decomposi-tion to realize trend prediction is proposed. Some non-stationary time series can be decomposed into several approximate stationary time series with wavelet decomposition. Decomposed time series are forecasted with auto-regression model, to obtain forecasting results of the original time series. Experiments with a power plant vibration signal show that the local and overall effect of the algorithm is better than neural network approaches. The result shows rotor vibration time series forecasting accuracy of this model.