基于OVMD与SVR的水电机组振动趋势预测

Vibration trend prediction of hydroelectric generating unit based on OVMD and SVR

为更好地预测水电机组振动趋势,研究提出了一种基于最优变分模态分解(OVMD)与支持向量回归(SVR)的水电机组振动趋势预测模型。首先基于中心频率观察法与残差指标最小化准则确定OVMD的分解参数,采用OVMD将非平稳振动序列分解为一系列模态函数,并对各模态函数分别进行相空间重构,构建状态矩阵,进而得到SVR回归预测模型的输入、输出,再采用交叉验证的网格搜索策略优化各SVR模型的参数,并分别进行回归预测,最后对所有SVR预测结果进行求和,得到原始振动趋势的预测值。研究对某大型混流式水电机组的振动监测数据进行预测试验,并进行对比分析,结果表明该模型可有效预测水电机组振动趋势。

To achieve better results in predicting the vibration trend of hydroelectric-generating units, a novel trend-prediction model based on optimal variational mode decomposition （OVMD） and support-vector regression （SVR） was proposed. Firstly, center-frequency observation method and residual minimization criteria were employed to determine the parameters of OVMD ; the non-stationary vibration series were decomposed into a set of mode functions, after which the state matrix corresponding to each mode was obtained with phase-space reconstruction. Then, the inputs and outputs of SVR models were deduced. Each SVR model was trained and tested with a grid search based on cross validation. Finally, prediction values of the original vibration series were calculated with the accumulation of outputs from all SVR models. The successful application in predicting the vibration trend for a large, mixed-flow hydroelectric-generating unit, as well as comparative analysis with other methods, attests to the effectiveness of the proposed model.