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

Qinqin ZHU, Qiang LIU, S. Joe QIN

Research output: Journal PublicationsJournal Article (refereed)peer-review

10 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)7999-8004
Number of pages6
Issue number1
Publication statusPublished - Jul 2017
Externally publishedYes

Bibliographical note

This work was supported in part by the Natural Science Foundation of China (61304107, 61490704, 61573022, 61673097), the Fundamental Research Program of the Shenzhen Committee on Science and Innovations, the Texas-Wisconsin-California Control Consortium, the International Postdoctoral Exchange Fellowship Program (20130020), and the China Postdoctoral Science Foundation (2013M541242).


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


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