Optimal Kernel Parameter Setting for Faults Detection with Stochastic Methods and Data Preprocessing

In this paper an indirect optimization criterion for parameter setting the kernel-based fault detection process is applied. The procedure analyzed involves the data preprocessing through the Kernel Independent Component Analysis (KICA) method, and the fault detection by using a classifier based on the Kernel Fuzzy C-means (KFCM) algorithm to reduce the classification errors. The main objective of the paper is the adjustment of the kernel parameters to obtain the best possible performance in the fault detection. To achieve this, two different metaheuristic algorithms are used: Differential Evolution and Particle Swarm Optimization. The proposed approach was evaluated by using the Tennessee Eastman (TE) process benchmark.