Kernel PCA Performance in Processes with Multiple Operation Modes

Kernel PCA, as a multivariate statistical process monitoring (MSPM) tool, is a powerful technique capable of coping with non linear relations between variables, thus outperforming classical linear techniques when non linearities are present in data. In real industrial chemical processes, multiple plant operating modes often lead to multiple nominal operation regions, and MSPM techniques that do not take account of this fact show increased false alarm and missing alarm rates. The existence of multiple operation modes is often more frequent than clearly expressed strong non linear relations between the variables involved. Non linear relations do exist, but the small variability allowed in key variables during normal plant operation prevents these non linear relations from being expressed in the data. In this work, a fault detection tool based on Kernel PCA is tested in such multiple operation modes environments, with final objective of implementing the tool in a real industrial installation. Robustness of the tool for coping with a certain percentage of outliers is particularly examined. The tool is applied to three case studies: (i) a two dimensional toy example, (ii) a realistic simulation usually used as a bench-mark example, known as the Tennessee Eastman Process, (iii) real data from a methanol plant. Hence, the feasibility of implementing fault detection tools based on this technique in the field of chemical industrial processes is discussed.