Decentralized Fault Diagnosis of Large-scale Processes Using Multiblock Kernel Principal Component Analysis

Ying-Wei ZHANG, Hong ZHOU, S. Joe QIN*

*Corresponding author for this work

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

39 Citations (Scopus)


In this paper, a multiblock kernel principal component analysis (MBKPCA) algorithm is proposed. Based on MBKPCA, a new fault detection and diagnosis approach is proposed to monitor large-scale processes. In particular, definitions of nonlinear block contributions to T2 and the squared prediction error (SPE) statistics are first proposed in order to diagnose nonlinear faults. In addition, the relative contribution, which is the ratio of the contribution to the corresponding upper control limit, is considered to find process variables or blocks responsible for faults. The proposed method is applied to fault detection and diagnosis in the Tennessee Eastman process. The proposed decentralized nonlinear approach effectively captures the nonlinear relationship in the block process variables and shows superior fault diagnosis ability compared with other methods. Copyright © 2010 Acta Automatica Sinica. All rights reserved.
Original languageEnglish
Pages (from-to)593-597
Number of pages5
Journal自动化学报/Acta Automatica Sinica
Issue number4
Publication statusPublished - 20 Apr 2010
Externally publishedYes

Bibliographical note

Supported by National Basic Research Program of China (973 Program) (2009CB320604) and the 111 Project.


  • Fault detection
  • Multiblock kernel methods
  • Nonlinear component analysis
  • Principal component analysis (PCA)
  • Process monitoring


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