Supervised Diagnosis of Quality and Process Faults with Canonical Correlation Analysis

Qinqin ZHU, S. Joe QIN*

*Corresponding author for this work

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

35 Citations (Scopus)

Abstract

Concurrent monitoring schemes that achieve simultaneous process and quality-relevant monitoring have recently attracted much attention. In this Article, we formulate a supervised fault diagnosis framework based on canonical correlation analysis (CCA) with regularization, which includes quality-relevant and quality-irrelevant fault diagnosis. Monitoring indices based on regularized concurrent CCA models are introduced to perform quality-relevant, potentially quality-relevant, and quality-irrelevant monitoring. Additionally, contribution plots and generalized reconstruction-based contribution methods are developed, along with their implications for the diagnosis based on the various monitoring indices. Finally, the Tennessee Eastman process is used to illustrate the supervised monitoring and diagnosis of quality-relevant and quality-irrelevant disturbances, and the 15 known disturbances are classified into two categories based on whether they have an impact on product quality variables.
Original languageEnglish
Pages (from-to)11213-11223
Number of pages11
JournalIndustrial and Engineering Chemistry Research
Volume58
Issue number26
Early online date25 Apr 2019
DOIs
Publication statusPublished - 3 Jul 2019
Externally publishedYes

Bibliographical note

This work was supported in part by the Natural Science Foundation of China (61490704), the Fundamental Disci plines Program of the Shenzhen Committee on Science and Innovations (20160207, 20170155), and the Texas−Wiscon sin−California Control Consortium.

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