Learning based on kernel-PCA for abnormal event detection using filtering EWMA-ED.

Autores

  • José M. Bernal de Lázaro
  • Orestes Llanes Santiago
  • Alberto Prieto Moreno
  • Diego Campos Knupp
  • Antonio Silva Neto

DOI:

https://doi.org/10.5540/03.2017.005.01.0114

Palavras-chave:

Fault Detection, kernel PCA, Small-magnitude faults, EWMA.

Resumo

Multivariate statistical approaches have been widely applied to monitoring complex process, however incipient and small−magnitude faults may not be properly detected with the above techniques. In this paper, a learning approach based on kernel-PCA with filtering EWMA-ED is proposed to improve the detection of these types of faults. The proposal was tested on the Tennessee Eastman (TE) process where it is observed a significant decrease in the missing alarms, whereas the latency times are reduced.

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Publicado

2017-04-14

Edição

Seção

Trabalhos Completos - Controle e Teoria de Sistemas