3 Citations (Scopus)


This paper introduces a new structure kernel principal component analysis (KPCA) that can successfully model symbolic interval-valued data for fault detection. In the proposed structure, interval KPCA (IKPCA) method is proposed to deal with interval-valued data. Two IKPCA models are proposed. The first model is based on the centers and ranges of intervals IKPCA CR and the second model is based the upper and lower bounds of intervals IKPCA UL . Residuals are generated and fault detection indices are computed. The aim of using IKPCA is to ensure robustness to false alarm without affecting the fault detection performance. The proposed fault detection approach is carried out using simulation example and Tennessee Eastman Process (TEP). The obtained results demonstrate the effectiveness of the proposed technique.

Original languageEnglish
Pages (from-to)36-45
Number of pages10
JournalChemical Engineering Science
Publication statusPublished - 21 Sep 2019



  • Fault detection
  • Interval-valued data
  • Kernel PCA
  • Tennessee Eastman process

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

Cite this