CIESC Journal ›› 2020, Vol. 71 ›› Issue (5): 2151-2163.DOI: 10.11949/0438-1157.20191518

• Process system engineering • Previous Articles     Next Articles

New fault detection and diagnosis strategy for nonlinear industrial process based on KECA

Mingyue DENG1(),Jianchang LIU1(),Peng XU1,Shubin TAN1,Liangliang SHANG2   

  1. 1.College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
    2.College of Electrical Engineering, Nantong University, Nantong 226019, Jiangsu, China
  • Received:2019-12-17 Revised:2020-02-14 Online:2020-05-05 Published:2020-05-05
  • Contact: Jianchang LIU

基于KECA的非线性工业过程故障检测与诊断新方法

邓明月1(),刘建昌1(),许鹏1,谭树彬1,商亮亮2   

  1. 1.东北大学信息科学与工程学院,辽宁 沈阳 110819
    2.南通大学电气工程学院,江苏 南通 226019
  • 通讯作者: 刘建昌
  • 作者简介:邓明月(1995—),女,硕士研究生,dengmingy@yeah.net
  • 基金资助:
    国家自然科学基金项目(6177310)

Abstract:

A new method for non-linear process fault detection and diagnosis based on kernel entropy component analysis (KECA) is proposed. Firstly, the score vectors and nonlinear feature space are obtained using KECA. Since KECA can reveal the underlying cluster structure in the data in the form of the angular structure, an angle-based monitoring index referred to as vector of angle (VoA) is designed. This index measures the structural difference between the transformed data by the angle variance of each score vector, and realizes fault detection according to the change. Then, in order to effectively identify faults after detection, KECA similarity factor is constructed to measure the similarity of feature space to identify fault patterns. Finally, the feasibility and validity of the proposed method are demonstrated by the nonlinear numerical case and Tennessee Eastman (TE) process.

Key words: fault detection and diagnosis, kernel entropy component analysis, VoA monitoring index, process control, similarity factors, model, safety

摘要:

提出了一种基于核熵成分分析(kernel entropy component analysis,KECA)的非线性过程故障检测与诊断新方法。该方法首先利用KECA获取过程数据的得分向量及非线性特征子空间;然后鉴于KECA可以以角结构的方式揭示数据中潜在的集群结构,设计了基于角度的监测指标VoA。该指标通过各得分向量之间的角度方差来描述变换后数据间的结构差异,并根据角度方差的变化情况实现故障检测;接着,为了在检测到故障后有效地进行故障识别,构建了KECA相似度因子来度量特征子空间的相似程度以识别故障模式;最后,以非线性数值案例及Tennessee Eastman过程进行仿真测试研究,结果验证了所提方法的可行性及有效性。

关键词: 故障检测与诊断, 核熵成分分析, VoA监测指标, 过程控制, 相似度因子, 模型, 安全

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