Non-Gaussian process monitoring and fault reconstruction and diagnosis based on SVDD

ZHANG Jianming; GE Zhiqiang; XIE Lei; SONG Zhihuan; WANG Shuqing

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Non-Gaussian process monitoring and fault reconstruction and diagnosis based on SVDD

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- Non-Gaussian process monitoring and fault reconstruction and diagnosis based on SVDD
- CIESC Journal Issue 1, Pages: 168-171(2009)
- 作者机构：
  
  1. 浙江大学智能系统与控制研究所，工业控制技术国家重点实验室
  2. 浙江大学工业控制技术研究所，工业控制技术国家重点实验室
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：05 January 2009，
  
  Published：05 January 2009
- 稿件说明：
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ZHANG Jianming, GE Zhiqiang, XIE Lei, et al. Non-Gaussian process monitoring and fault reconstruction and diagnosis based on SVDD[J]. CIESC Journal, 2009, (1): 168-171.
DOI：

ZHANG Jianming, GE Zhiqiang, XIE Lei, et al. Non-Gaussian process monitoring and fault reconstruction and diagnosis based on SVDD[J]. CIESC Journal, 2009, (1): 168-171. DOI：

摘要

提出一种基于支持向量描述（SVDD）的统计过程监控与故障重构及诊断算法，避免了PCA、PLS等传统统计过程监控方法假设过程数据服从高斯分布的不足。鲁棒故障重构算法通过迭代保证重构后的数据对应的SVDD监控统计量最小化。诊断算法根据故障集中的不同故障重构后监控统计量是否恢复正常，确定实际发生的过程故障。CSTR过程的仿真研究表明了所提出方法的有效性。

Abstract

A novel support vector data description（SVDD）statistical process monitoring and fault reconstruction & isolation approach is proposed to address non-Gaussian multivariate process systems and overcome the deficiencies of traditional PCA and PLS methods.A robust iterative approach is involved to reconstruct the fault signals and minimize the SVDD monitoring statistics.By comparing the reconstructed statistics with different fault candidates，a fault diagnosis strategy is proposed to isolate the actual process fault.A study of the application in CSTR simulation demonstrates the efficiency of proposed method.

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