基于贡献率法的非线性工业过程在线故障诊断

在过去几十年，核主成分分析（KPCA）已经广泛应用在数据驱动的过程监测领域. 大量的应用案例显示该算法简单、易用且有效. 然而，核函数的引入使得KPCA不能直接利用传统的贡献图方法进行故障诊断. 本文在重新审视和分析现有KPCA相关诊断方法的基础上，提出了一类新的贡献率方法，该方法能较清晰地解释故障变量. 在此基础上，建立了一套面向非线性在线故障诊断的框架. 最后，将该诊断框架应用到CSTR过程，结果显示该方法较传统的线性方法更有效.

Online Contribution Rate Based Fault Diagnosis for Nonlinear Industrial Processes

Over past decades, kernel principal component analysis (KPCA) appeared quite popularly in data-driven process monitoring area. Enormous work has been done to show its simplicity, feasibility, and effectiveness. However, the introduction of kernel trick makes it impossible to directly employ traditional contribution plots for fault diagnosis. In this paper, on the basis of revisiting and analyzing the existing KPCA-relevant diagnosis approaches, a new contribution rate based method is proposed which can explain the faulty variables clearly. Furthermore, a scheme for online nonlinear diagnosis is established. In the end, a case study on continuous stirred tank reactor (CSTR) benchmark is applied to access the effectiveness of the new methodology, where the comparisons with the traditional linear method are involved as well.