Sequential control performance diagnosis of steel processes

L.F. Recalde, R. Katebi, H. Yue

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


A sequential method for Control Performance Diagnosis using a classication tree to predict possible root-causes of poor performance is presented. The classication tree methodology is used to combine process pre-assessment (nonlinearities detection, delays estimation and controller assessment), Control Performance Assessment (CPA) and ANalysis Of VAriance (ANOVA) into an integrated framework. A initial process data set is analysed and the results are used as decision thresholds for the classication tree. The methodology is capable to identify root-causes such as:poor tuning, inadequate control structure, nonlinearities, process mismatch and disturbance changes. The proposed methodology is applied to individual loops of a tandem cold rolling mill.
Original languageEnglish
Pages (from-to)2830 – 2835
Number of pages6
JournalIFAC Proceedings Volumes
Issue number3
Publication statusPublished - 24 Aug 2014
EventIFAC World Congress 2014 - Cape Town, South Africa
Duration: 24 Aug 201429 Aug 2014


  • closed loop identification
  • fault detection
  • fault diagnosis
  • tuning
  • filtering


Dive into the research topics of 'Sequential control performance diagnosis of steel processes'. Together they form a unique fingerprint.

Cite this