Total PLS Based Contribution Plots for Fault Diagnosis

Gang LI, Si-Zhao QIN, Yin-Dong JI, Dong-Hua ZHOU*

*Corresponding author for this work

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

75 Citations (Scopus)


Multivariate statistical process monitoring (MSPM) is an efficient data-driven fault detection and diagnosis approach for complex industrial processes. Partial least squares or projection to latent structures (PLS) is one of the latent projection structures used in MSPM, which uses process data X and quality data Y together. In this paper, we discuss a new fault diagnosis approach based on total projection to latent structures (T-PLS). Four kinds of monitoring statistics are used in T-PLS, and a new definition of variable contributions to T2 of PLS is proposed. Then, definitions of variable contributions to all statistics are derived to identify the faults. Control limits for contribution plots are calculated to identify whether a variable is in abnormal situation or not. Further, the proposed method separates the identified variables into faulty variables related to Y and unrelated to Y more clearly than conventional method. A case study on Tennessee Eastman process (TEP) indicates the efficiency of the proposed approach.
Original languageEnglish
Pages (from-to)759-765
Number of pages7
JournalZidonghua Xuebao/ Acta Automatica Sinica
Issue number6
Publication statusPublished - 20 Jun 2009
Externally publishedYes

Bibliographical note

Supported by National Basic Research Program of China (973 Program) (2009CB320602), National Natural Science Foundation of China (60721003, 60736026), and Changjiang Professorship by Ministry of Education of P. R. China.


  • Contribution plots
  • Data-driven
  • Fault diagnosis
  • Total projection to latent structures (T-PLS)


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