Improved fault diagnosis in multivariate systems using regression-based reconstruction

D. Lieftucht, M. Volker, C. Sonntag, U. Kruger, George Irwin, S. Engell

Research output: Contribution to journalArticle

18 Citations (Scopus)
13 Downloads (Pure)


Subspace monitoring has recently been proposed as a condition monitoring tool that requires considerably fewer variables to be analysed compared to dynamic principal component analysis (PCA). This paper analyses subspace monitoring in identifying and isolating fault conditions, which reveals that the existing work suffers from inherent limitations if complex fault senarios arise. Based on the assumption that the fault signature is deterministic while the monitored variables are stochastic, the paper introduces a regression-based reconstruction technique to overcome these limitations. The utility of the proposed fault identification and isolation method is shown using a simulation example and the analysis of experimental data from an industrial reactive distillation unit.
Original languageEnglish
Pages (from-to)478-493
Number of pages16
JournalControl Engineering Practice
Issue number4
Publication statusPublished - Apr 2009

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Improved fault diagnosis in multivariate systems using regression-based reconstruction'. Together they form a unique fingerprint.

  • Cite this