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

  • José Manuel Bernal-de Lázaro Universidad Tecnológica de La Habana José Antonio Echeverría, CUJAE
  • Adrián Rodríguez-Ramos Universidad Tecnológica de La Habana José Antonio Echeverría, CUJAE
  • Carlos Cruz-Corona Universidad de Granada
  • Antônio José da Silva-Neto Instituto Politécnico Do Estado Do Rio De Janeiro, IPRJ
  • Orestes Llanes-Santiago Universidad Tecnológica de La Habana José Antonio Echeverría, CUJAE

Resumen

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.

Publicado
2019-04-06
Cómo citar
Bernal-de Lázaro, J. M., Rodríguez-Ramos, A., Cruz-Corona, C., da Silva-Neto, A. J., & Llanes-Santiago, O. (2019). Optimal Kernel Parameter Setting for Faults Detection with Stochastic Methods and Data Preprocessing. REVISTA CEREUS, 11(1), 195-209. Recuperado a partir de http://ojs.unirg.edu.br/index.php/1/article/view/2704