CIESC Journal ›› 2019, Vol. 70 ›› Issue (2): 736-749.doi: 10.11949/j.issn.0438-1157.20180842

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Fault diagnosis based on PCA method with multi-block information extraction

Bingbin GU1(),Weili XIONG2()   

  1. 1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China
    2. Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, Jiangsu, China
  • Received:2018-07-21 Revised:2018-11-22 Online:2019-02-05 Published:2018-12-04
  • Contact: Weili XIONG;


Traditional monitoring methods only use sensor observation information to perform process fault monitoring, while ignoring other valid information contained in the original data. Aiming to this problem, a PCA fault monitoring algorithm based on multi-block information extraction is proposed. Firstly, two kinds of information of the cumulative error and the change rate of process variables are defined, so that new feature information can be extracted from the data. The process is divided into three sub-blocks based on each feature, and each sub-block is processed by the PCA method. Modeling and monitoring are carried out, and monitoring results are integrated by Bayesian method. Finally, a fault diagnosis method with weighted contribution graph is proposed to find the source variable which causes the fault. The validity and feasibility of the proposed method are demonstrated by numerical examples and the application of Tennessee-Eastman (TE) process monitoring.

Key words: principal component analysis, algorithm, model, fault diagnosis, information extraction, multi-block modelling

CLC Number: 

  • TP277


Comparison between traditional multi-block modeling method and multi-block information modeling method"

Table 1

Sub-block division results"



Fault monitoring flowchart based on MBI-PCA"


Monitoring result of fault 1"


Isolation result of fault 1"


Monitoring result of fault 2"


Isolation result of fault 2"

Table 2

Missing alarm rates of TE process/%"



Monitoring result of fault 5"


Isolation result and related variable curves of fault 5"


Monitoring result of fault 11"


Isolation result and related variable curves of fault 11"


Comparison between PCA and MBI-PCA of fault 21"


Monitoring results of fault 0"

Table 3

Comparison of some state of multi-block monitoring methods"

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