Online monitoring for industrial processes quality control using time varying parameter model

R. Parvizi Moghadam, F. Shahraki, J. Sadeghi

Research output: Contribution to journalArticlepeer-review

Abstract

A novel data-driven soft sensor is designed for online product quality prediction and control performance modification in industrial units. A combined approach of time variable parameter (TVP) model, dynamic auto regressive exogenous variable (DARX) algorithm, nonlinear correlation analysis and criterion-based elimination method is introduced in this work. The soft sensor performance validation is tested by data set of an industrial SRU. The comparative study indicated the result associated with more robust soft sensor and more appropriate performance index values compared to other methods for SRU soft sensor design in diverse achievements. Due to high prediction accuracy, the low complication of the model and also saving of time, this technique can be very noticeable in industrial processes control.

Original languageEnglish
Pages (from-to)524-532
Number of pages9
JournalInternational Journal of Engineering, Transactions B: Applications
Volume31
Issue number4
DOIs
Publication statusPublished - Apr 2018
Externally publishedYes

Keywords

  • Identification data-based modeling
  • Quality estimation
  • Soft sensor
  • SRU
  • Time varying parameter

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