基于核主元分析的凝汽器系统故障诊断

Kernel principal component analysis based fault diagnosis for condenser systems

采用核主元分析方法建立核主元模型,提取凝汽器系统的非线性冗余信息,在输入空间对数据进行重构,此重构的数据与原始样本数据具有残差,采用序贯概率比对残差进行检验能够及时发现凝汽器系统的故障,并通过综合分析可定位故障源。结合某台600MW机组凝汽器系统的故障进行诊断仿真验证结果表明,该方法能够准确识别故障,所建的故障诊断模型具有一定的准确性和实用性。

By kernel principal component analysis(KPCA),a kernel principal model was built to extract the system's non-linear redundant information,and then reconstruct the data in the input space.The residual error produced by the reconstructed data and original sample data was detected by the sequential probability ratio text(SPRT) to timely notice the fault symptoms,and to identify the fault causes by a comprehensive analysis on the system.The diagnosis simulation for the condenser system of a 600 MW power plant was conducted.The results showed that,this KPCA method can accurately identify the fault and build fault diagnosis model with a certain accuracy and practibility.