热力参数的KPCA-RBF网络建模及传感器故障诊断方法

针对复杂恶劣环境下机组热力参数的数据监测及传感器故障诊断问题,建立了融合机理分析、核主元分析(kernel principle component analysis,简称KPCA)与径向基神经网络(radial basis function,简称RBF)的发电机组热力参数预测及传感器故障检测模型。首先,根据机理分析得到完备的辅助变量集,并利用核主元分析提取辅助变量的特征信息以有效处理发电机组中高维、强耦合的非线性数据;其次,将主元变量集输入径向基神经网络进行学习,实现热力参数的重构;最后,基于预测模型与窗口移动法实现传感器的故障诊断,并对故障数据进行及时修复和准确替换。以燃气轮机排气温度为例进行验证的结果表明,该预测模型具有更高的精度和泛化能力,能在传感器故障发生初期及时发现并识别故障类型,检测效果优良。

Thermal Parameters Modeling Method and Sensor Fault Diagnosis Based on KPCA-RBF Network

In light of the problems of data monitoring and sensor fault diagnosis for thermal parameters in power plants, this paper builds an applied model based on mechanism analysis, kernel principal component analysis (KPCA), and radial basis function (RBF) neural network. First, auxiliary parameters related to the variable under study were obtained according to mechanism analysis. Then, KPCA was used to extract the high order nonlinear characteristics of the input variables, due to the high dimensionality, nonlinearity and strong coupling among them. Components were used to study and realize the reconstruction of thermal parameters through the RBF neural network. Last, sensor fault diagnosis was realized based on the prediction model and window moving method, and the fault data were able to be accurately replaced in time. Taking gas turbine outlet temperature as an example, the results show that this model performs with higher precision and generalization ability. Importantly, it can detect sensor faults and identify the type of fault in early stages, attaining a preferable detection effect.