Abstract:

The traditional kernel principal component analysis is popularly used for fault detection, however, it only concentrates on the global structure of data sets and ignores the local structure when it is used to extract the nonlinear features. To solve the problem, a new method named modified kernel principal component analysis is proposed for nonlinear process fault detection and diagnosis. The idea of locality preserving is incorporated into the optimization goal of the traditional kernel principal component analysis, taking the excellence of kernel principal component analysis and manifold learning into account. The new projection space enjoys the similar global structure and the local structure, and thus, more feature information can be extracted. The modified kernel principal component analysis is used to map the data space into the feature space. Next, the feature information is classified through Fisher discriminant analysis. A monitoring statistic is established using the distance of each sample in feature space and its control limit is determined through kernel density estimation. When a fault is detected, the source of performance deterioration can be located by using a diagnosis method based on data set similarity. Finally, the results of Tennessee Eastman simulation experiment show its better effectiveness.

Key words: modified kernel principal component analysis, manifold learning, Fisher discriminant analysis, fault detection, diagnosis, simulation

摘要：

针对传统基于核主成分分析的故障检测方法提取非线性特征时只考虑全局结构而忽略局部近邻结构保持的问题, 提出基于改进核主成分分析的故障检测与诊断方法。改进核主成分分析方法将流形学习保持局部结构的思想融入核主成分分析的目标函数中, 使得到的特征空间不仅具有原始样本空间的整体结构, 还保持样本空间相似的局部近邻结构, 可以包含更丰富的特征信息。在此基础上, 本文使用改进核主成分分析方法把原始变量空间映射到特征空间, 使用费舍尔判别分析在特征空间中构建距离统计量并通过核密度估计确定其控制限, 进一步利用相似度的性能诊断方法识别发生的故障类型。采用Tennessee Eastman过程故障检测数据集进行的仿真实验表明所提方法可以取得较好的效果。

关键词: 改进核主成分分析, 流形学习, 费舍尔判别分析, 故障检测, 诊断, 仿真实验