Concurrent Monitoring and Diagnosis of Process and Quality Faults with Canonical Correlation Analysis

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)7999-8004
Journal / PublicationIFAC-PapersOnLine
Issue number1
Publication statusPublished - Jul 2017
Externally publishedYes


Partial least squares and canonical correlation analysis are latent variable models suitable for quality-relevant monitoring based on process and quality data. Recently, concurrent monitoring schemes are proposed to achieve simultaneous process and quality monitoring. This paper defines and analyzes quality-relevant monitoring based on these popular latent structure modeling methods, and the associated quality-relevant monitoring statistics are defined. Additionally, contribution plots and reconstruction-based contribution diagnosis methods are developed for concurrent fault diagnosis. Multi-dimensional quality-relevant faults can be diagnosed in the same reconstruction framework. Finally, a detailed case study on Tennessee Eastman process is shown to illustrate the diagnosis of process and quality faults and the prognosis of quality-relevant faults.

Research Area(s)

  • Canonical Correlation Analysis, Contribution Plots, Quality-Relevant Diagnosis, Quality-Relevant Fault Prognosis, Reconstruction-based Contribution