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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.
|Pages||2830 – 2835|
|Number of pages||6|
|Publication status||Published - 24 Aug 2014|
|Event||IFAC World Congress 2014 - Cape Town, South Africa|
Duration: 24 Aug 2014 → 29 Aug 2014
|Conference||IFAC World Congress 2014|
|Period||24/08/14 → 29/08/14|
- closed loop identification
- fault detection
- fault diagnosis
FingerprintDive into the research topics of 'Sequential control performance diagnosis of steel processes'. Together they form a unique fingerprint.
- 1 Finished
- 1 Citations
- 1 Paper
Recalde, L. F., Katebi, R. & Tauro, H., 14 Nov 2013, p. 1075-1080. 6 p.
Research output: Contribution to conference › Paper › peer-review