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

GU Bingbin; XIONG Weili

doi:10.11949/j.issn.0438-1157.20180842

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

Process system engineering | 更新时间：2026-07-24

- Fault diagnosis based on PCA method with multi-block information extraction
- CIESC Journal Vol. 70, Issue 2, Pages: 736-749(2019)
- 作者机构：
  
  1.江南大学物联网工程学院，江苏无锡 214122
  2.江南大学轻工过程先进控制教育部实验室，江苏无锡 214122
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20180842
  CLC： TP277
- Received：21 July 2018，
  
  Revised：2018-11-22，
  
  Online First：16 June 2020，
  
  Published：05 February 2019
- 稿件说明：
移动端阅览
顾炳斌, 熊伟丽. 基于多块信息提取的PCA故障诊断方法[J]. 化工学报, 2019, 70(2): 736-749

GU Bingbin, XIONG Weili. Fault diagnosis based on PCA method with multi-block information extraction[J]. CIESC Journal, 2019, 70(2): 736-749
顾炳斌, 熊伟丽. 基于多块信息提取的PCA故障诊断方法[J]. 化工学报, 2019, 70(2): 736-749 DOI： 10.11949/j.issn.0438-1157.20180842.

GU Bingbin, XIONG Weili. Fault diagnosis based on PCA method with multi-block information extraction[J]. CIESC Journal, 2019, 70(2): 736-749 DOI： 10.11949/j.issn.0438-1157.20180842.

摘要

传统的监控方法往往只利用传感器观测值信息进行过程的故障监测，而忽略了原始数据中包含的其他有效信息。为此，提出一种基于多块信息提取的PCA故障监测算法。首先，对过程变量的累计误差和变化率信息进行定义，从而能够从数据中提取新的特征信息，并基于每种特征将过程划分为3个子块；然后，利用PCA方法对每个子块进行建模与监测，通过贝叶斯方法对监测结果进行融合；最后，提出一种基于加权贡献图的故障诊断方法，分离出引发故障的源变量。通过数值例子与田纳西-伊斯曼（TE）过程监控中的应用证明了所提方法的有效性与可行性。

Abstract

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.

关键词

Keywords

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