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中文摘要：
核主管部件分析(KPCA ) 方法采用开始的几个核主管部件(KPC ) ，它为过程监视显示正常观察的大多数变化信息，但是不能反映差错信息。在这研究，敏感内核主管部件分析(SKPCA ) 被建议改进监视性能的过程，即，错过了察觉率处理 T2 统计、摆平的预言错误 SPE 统计数值和还原剂的不和。T2 统计数值能被用来沿着每 KPC 直接测量变化并且分析察觉表演以及在一个过程捕获最有用的信息。随沿着每 KPC 的 T2 统计数值的变化率的计算， SKPCA 为进程监视选择敏感内核主管部件。一个模仿的简单系统和田纳西伊斯门过程被采用在联机监视上表明 SKPCA 的效率。结果显示监视表演显著地被改进。
英文摘要：
The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.
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