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

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


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.


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

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DOI: https://doi.org/10.5540/03.2017.005.01.0114


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