Fault identification method based on SPA similarity factor

ZHANG Hanyuan; TIAN Xuemin; DENG Xiaogang

doi:10.3969/j.issn.0438-1157.2013.12.035

您当前的位置：

首页 >

文章列表页 >

Fault identification method based on SPA similarity factor

更新时间：2025-12-10

- Fault identification method based on SPA similarity factor
- CIESC Journal Issue 12, Pages: 4503-4508(2013)
- 作者机构：
  
  中国石油大学(华东)信息与控制工程学院,山东,青岛,266580
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2013.12.035
  CLC： P277
- Online First：05 December 2013，
  
  Published：05 December 2013
- 稿件说明：
移动端阅览
ZHANG Hanyuan, TIAN Xuemin, DENG Xiaogang. Fault identification method based on SPA similarity factor[J]. CIESC Journal, 2013, (12): 4503-4508.
DOI：

ZHANG Hanyuan, TIAN Xuemin, DENG Xiaogang. Fault identification method based on SPA similarity factor[J]. CIESC Journal, 2013, (12): 4503-4508. DOI： 10.3969/j.issn.0438-1157.2013.12.035.

摘要

传统的主元分析（PCA）相似系数法没有充分利用数据的高阶统计量等有用的过程信息，导致故障识别效果较差。针对此问题，提出一种统计量模式分析（SPA）相似系数法。该方法首先使用SPA将原始数据转换到统计量空间中，然后在统计量空间中利用PCA获取主元方向，计算主元之间的相似性识别故障。在连续搅拌反应器（CSTR）过程上的仿真结果说明提出的SPA相似系数法比传统的PCA相似系数法能更有效地识别故障。

Abstract

The traditional principal component analysis (PCA) similarity factor method does not make full use of the higher-order statistics of the process data

which results in degraded fault identification performance.In order to solve this problem

a statistics pattern analysis (SPA) similarity factor method is proposed in this paper.Firstly

the original process data are transformed into the statistics space by using the SPA.Then

the PCA is adopted to obtain the principal component directions in the statistics space.Finally

the similarity between the principal components is calculated to identify faults.Simulation results on the continuous stirring tank reactor (CSTR) process show that the proposed SPA similarity factor method is more effective than the traditional PCA similarity factor method in terms of identifying faults.

关键词

Keywords

references

Views

594

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Fault detection method based on minimum sufficient statistics pattern analysis

A chemical process quality-related fault detection method based on twin-space parallel regression

Fault detection using grouped support vector data description based on maximum information coefficient

Research on quality-related fault detection method based on VAE-OCCA

Fault diagnosis of proton exchange membrane fuel cell based on nonlinear dynamic model

Related Author

SUN Shuanzhu

DONG Shun

JIANG Yefeng

ZHOU Ting

LI Yiguo

SONG Bing

GUO Tao

SHI Hongbo

Related Institution

Jiangsu Electric Power Co. Ltd.

School of Energy and Environment, Southeast University

Jiangsu Frontier Electric Technology Co. Ltd.

China Railway 14th Bureau Group Corporation Limited

Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education

⁰