The kernel principal component analysis (KPCA) is widely used as a fault monitoring tool for complex nonlinear chemical processes in recent years. The cumulative contribution rate that extracts the kernel principal is usually obtained relied on a fixed model, which cannot be employed for time-varying chemical processes. Hence, a novel adaptive kernel principal component analysis (AKPCA) integrating grey relational analysis (GRA) (AKPCA-GRA) is proposed to dynamically monitor the fault occurrence. A moving window integrating the threshold method is used to adaptively extract the kernel principal for chemical processes. Then the corresponding T² and Q statistics calculated by the selected kernel principal based on the AKPCA decides whether the fault has occurred. Moreover, the GRA method is used to analyze and calculate the correlation coefficient of abnormal features obtained based on the APKCA method, which provides the operational guidance for the nonlinear chemical process to find out the variable causing the fault. Finally, the proposed method is verified using the Tennessee Eastman (TE) process. The case results demonstrate that the proposed method outperforms the KPCA, the KPCA based on the threshold and the moving window principal component analysis, the support vector machine (SVM) and the Logistic Regression (LR) in terms of the missed alarm rate (MAR) and the false alarm rate (FAR), which can effectively analyze the variables causing the fault.

近年来，核主成分分析（KPCA）被广泛用作复杂非线性化学过程的故障监测工具。提取核主体的累积贡献率通常依赖于固定模型获得，不能用于时变化学过程。因此，提出了一种集成灰色关联分析（GRA）（AKPCA-GRA）的新型自适应核主成分分析（AKPCA）来动态监测故障发生。结合阈值方法的移动窗口用于自适应地提取化学过程的核原理。那么对应的T ²选择的内核主体根据 AKPCA 计算出的 Q 统计信息决定故障是否发生。此外，利用GRA方法对基于APKCA方法得到的异常特征的相关系数进行分析计算，为非线性化工过程找出引起故障的变量提供了操作指导。最后，使用田纳西伊士曼 (TE) 过程验证了所提出的方法。案例结果表明，所提出的方法在漏报率（MAR）方面优于KPCA、基于阈值和移动窗口主成分分析的KPCA、支持向量机（SVM）和逻辑回归（LR）和误报率（FAR），可以有效地分析引起故障的变量。