基于ARMA的汽轮机转子振动故障序列的预测

汽轮机转子振动系统是一个确定性复杂系统，振动序列由多种频率成分的分量复合而成，建立尽可能完整与精确的系统振动数学模型是提取故障征兆信息及故障预测的保证．文中根据Bently实验台所采集的碰摩、松动、不对中和不平衡四种典型汽轮机转子振动故障水平方向与垂直方向的数据，剔除趋势项及周期项，所余的随机平稳噪声项经平稳性检验后，建立了汽轮机转子振动故障序列自回归滑移平均(ARMA)模型．计算结果表明，所建立的8个汽轮机转子振动故障ARMA模型一个半周期的预测值的平均误差μ均小于0．55μm，确定性因子r^2均大于0．9915，具有较高的预测精度，为进一步提取故障征兆信息及故障发展趋势预测提供了条件．

Forecasting of Vibration Fault Series of Stream Turbine Rotor Based on ARMA

The vibration system of the steam turbine rotor is a definite complex system, the vibration sequence of which consists of many kinds of frequency components. An integrated and accurate mathematical model is important to the extraction of fault premonition information and the forecasting of faults. In this paper, according to the data in both the horizontal direction and the vertical direction of four typical faults, such as rubbing, loosing, uncountershaft and mass unbalance collected from the Bently experimental set, a ARMA (AutoRegression Moving Average) model is built up for the vibration fault series after the stable examination of the random stationary noise item in which both the tendency and periodicity items are removed. The calculated results indicate that the mean errors of 8 forecasted vibration faults of turbine rotor in half period obtained by the ARMA model are all less than (0.55m,) and the deterministic factors r~2 are all more than 0.9915. This means that the proposed method is of great forecasting precision and is effective in the further extraction of fault symptom information and the further forecasting of the developing trend of vibration faults.