基于非线性动态全局局部保留投影算法的化工过程故障检测

徐静; 王振雷; 王昕

doi:10.11949/0438-1157.20200417

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基于非线性动态全局局部保留投影算法的化工过程故障检测

过程系统工程 | 更新时间：2026-07-24

- 基于非线性动态全局局部保留投影算法的化工过程故障检测
- Fault detection for chemical process based on nonlinear dynamic global-local preserving projections
- 化工学报 2020年71卷第12期页码：5655-5663
- 作者机构：
  
  1.华东理工大学化工过程先进控制和优化技术教育部重点实验室，上海 200237
  2.上海交通大学电工与电子技术中心，上海 200240
- 作者简介：
  
  徐静（1996—），女，硕士研究生，1361118005@qq.com
  王振雷（1975—），男，博士，教授，wangzhen_l@ecust.edu.cn
- 基金信息：
  
  国家重点研发计划项目(2018YFB1701103)
- DOI：10.11949/0438-1157.20200417
  中图分类号： TQ 277
- 收稿：2020-04-21，
  
  修回：2020-06-23，
  
  网络首发：2020-12-17，
  
  纸质出版：2020-12-05
- 稿件说明：
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徐静, 王振雷, 王昕. 基于非线性动态全局局部保留投影算法的化工过程故障检测[J]. 化工学报, 2020, 71(12): 5655-5663

XU Jing, WANG Zhenlei, WANG Xin. Fault detection for chemical process based on nonlinear dynamic global-local preserving projections[J]. CIESC Journal, 2020, 71(12): 5655-5663
徐静, 王振雷, 王昕. 基于非线性动态全局局部保留投影算法的化工过程故障检测[J]. 化工学报, 2020, 71(12): 5655-5663 DOI： 10.11949/0438-1157.20200417.

XU Jing, WANG Zhenlei, WANG Xin. Fault detection for chemical process based on nonlinear dynamic global-local preserving projections[J]. CIESC Journal, 2020, 71(12): 5655-5663 DOI： 10.11949/0438-1157.20200417.

摘要

传统基于核映射的非线性故障检测方法的性能受核函数类型和核参数的调优影响较大，且实际工业环境中对过程变量的非线性阶数存在很多物理限制。针对这一问题，提出一种非线性动态全局局部保留投影（nonlinear dynamic global-local preserving projections，NDGLPP）的故障检测算法。该方法首先使用动态全局局部保留投影算法对数据矩阵进行降维；然后对降维后的矩阵建立二阶多项式映射提取非线性空间的相关特性；接着通过迭代这两个步骤以获得高阶非线性映射；最后，将所提方法应用于乙烯精馏过程和Tennessee Eastman（TE）过程仿真中，验证了检测方法的有效性和可行性。

Abstract

The performance of the traditional nonlinear fault detection method based on kernel mapping is greatly influenced by the type of kernel function and the tuning of kernel parameters. To solve this problem

a method named nonlinear dynamic global-local preserving projections(NDGLPP) is proposed for nonlinear process fault detection. Firstly

dynamic global-local preserving projection algorithm is used to reduce the dimension of data matrix. Since the second order polynomial mapping is established for the reduced dimension matrix to extract the relevant properties of nonlinear space. Then the two steps are iterated to obtain the higher-order nonlinear mapping. Finally

the proposed method is applied to the ethylene distillation process and Tennessee Eastman (TE) process simulation to verify the effectiveness and feasibility of the detection method.

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Keywords

references

Naderi E, Khorasani K. A data-driven approach to actuator and sensor fault detection, isolation and estimation in discrete-time linear systems[J ] . Automatica, 2017, 85 (Suppl. C): 165-178.

Zhang K, Hao H, Chen Z, et al. A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches[J ] . Process Control, 2015, 33 : 112-126.

Ge Z Q, Song Z H , Gao F R. Review of recent research on data-based process monitoring[J ] . Industrial & Engineering Chemistry Research, 2013, 52 (10): 3543-3562.

Yu H, Khan F, Garaniya V. A sparse PCA for nonlinear fault diagnosis and robust feature discovery of industrial processes[J ] . AIChE J., 2016, 62 (5): 1494-1513.

赵帅, 宋冰, 侍洪波. 基于加权互信息主元分析算法的质量相关故障检测[J ] . 化工学报, 2018, 69 (3): 962-973.

Zhao S, Song B, Shi H B. Quality-related fault detection based on weighted mutual in formation principal component analysis[J ] . CIESC Journal, 2018, 69 (3): 962-973.

Cai L, Tian X, Chen S. A process monitoring method based on noisy independent component analysis[J ] . Neurocomputing, 2014, 127 : 231-246.

Jiang B, Zhu X, Huang D, et al. A combined canonical variate analysis and Fisher discriminant analysis(CVA-FDA) approach for fault diagnosis[J ] . Computers & Chemical Engineering, 2015, 77 : 1-9.

Zhang Y, An J, Zhang H. Monitoring of time -varying processes using kernel independent component analysis[J ] . Chemical Engineering Science, 2013, 88 (Suppl. C): 23-32.

He X, Cai D, Yan S, et al. Neighborhood preserving embedding [C]// Tenth IEEE International Conference on Computer Vision. IEEE, 2005: 1208-1213.

He X, Niyogi P. Locality preserving projections [J ] . Neural Inform. Process. Syst., 2003, 16 (1): 153-160.

Song B, Tan S, Shi H. Time-space locality preserving coordination for multimode process monitoring[J ] . Chemometrics & Intelligent Laboratory Systems, 2016, 151 : 190-200.

Luo L, Bao S, Gao Z, et al. Tensor global-local preserving projections forbatch process monitoring[J ] . Ind. Eng. Chem. Res., 2014, 53 : 10166-10176.

Luo L, Bao S, Gao Z, et al. Batch process monitoring with tensor global-localstructure analysis[J ] . Ind. Eng. Chem.Res., 2013, 52 : 18031-18042.

Tong C, Yan X. Statistical process monitoring based on a multi-manifold projection algorithm[J ] . Chemometrics & Intelligent Laboratory Systems, 2014, 130 : 20-28.

Zhang M G, Ge Z Q, Song Z H, et al. Global-local structure analysis model and its application for fault detection and identification[J ] . Ind. Eng. Chem. Res., 2011, 50 (11): 6837-6848.

Jiang Q, Yan X. Nonlinear plant-wide process monitoring using MI-spectralclustering and Bayesian inference-based multiblock KPCA[J ] . Process Control, 2015, 32 : 38-50.

Lee J M, Yoo C, Choi S W, et al. Nonlinear process monitoring using kernel principal component analysis[J ] . Chem. Eng. Sci., 2004, 59 (1): 223-234.

Zhang H , Qi Y, Wang L, et al. Fault detection and diagnosis of chemical process using enhanced KECA[J ] . Chemometrics and Intelligent Laboratory Systems, 2017, 161 : 61-69.

Luo L, Bao S, Mao J, et al. Nonlinear process monitoring based on kernel global-local preserving projections[J ] . Journal of Process Control, 2016, 38 : 11-21.

Zhao H T, Lai Z H, Chen Y D. Global - and - local- structure-based neural network for fault detection[J ] . Neural Networks, 2019, 118 : 43-53.

Huang G, Song S, Gupta J N D, et al. Semi- supervised and unsupervised extreme learning machines [J ] . IEEE Transactions on Cybernetics, 2014, 44 (12): 2405-2417.

蔡连芳, 田学民, 张妮. 一种基于改进 KICA 的非高斯过程故障检测方法[J ] . 化工学报, 2012, 63 (9): 2864-2868.

Cai L F, Tian X M, Zhang N. Non-Gaussian process fault detection method based on modified KICA[J ] . CIESC Journal, 2012, 63 (9): 2864-2868.

Yu H Y, Khan F. Improved latent variable models for nonlinear and dynamic process monitoring [J ] . Chemical Engineering Science, 2017, 168 : 328-338.

Ku W F, Robert H S, Christos G. Disturbance detection and isolation by dynamic principal component analysis[J ] . Chemometrics and Intelligent Laboratory Systems, 1995, 30 (1) : 1 79-196.

罗雄麟, 赵晓鹰, 吴博, 等. 乙烯精馏塔异常工况在线侦测与控制[J ] . 化工学报, 2014, 65 (11): 4517-4523.

Luo X L, Zhao X Y, Wu B, et al. Online detection and control of ethylene column abnormal condition[J ] . CIESC Journal, 2014, 65 (11): 4517-4523.

陈德呈.乙烯精馏过程软测量技术应用研究[D]. 上海: 华东理工大学, 2011.

Chen D C. Soft sensing technology in the ethylene distillation process applied research[D]. Shanghai: East China University of Science and Technology, 2011.

汪世杰, 王振雷, 王昕.基于 JIT-MOSVR 的软测量方法及应用[J ] . 化工学报, 2017, 68 (3): 947-955.

Wang S J, Wang Z L, Wang X. Soft-sensor method based on JIT-MOSVR and its application[J ] . CIESC Journal, 2017, 68 (3): 947-955.

Lyman P R, Georgakis C. Plant-wide control of the Tennessee Eastman problem [J ] . Computers and Chemical Engineering, 1995, 19 (3): 321-331.

周乐, 宋执环, 侯北平, 等. 一种鲁棒半监督建模方法及其在化工过程故障检测中的应用[J ] . 化工学报, 2017, 68 (3): 1109-1115.

Zhou L, Song Z H, Hou B P, et al. Robust semi- supervised modelling method and its application to fault detection in chemical processes [J ] . CIESC Journal, 2017, 68 (3): 1109-1115.

仓文涛, 杨慧中. 基于主元子空间富信息重构的过程监测方法[J ] . 化工学报, 2018, 69 (3): 1114-1120.

Cang W T, Yang H Z. A process monitoring method based on informative principal component subspace reconstruction [J ] . CIESC Journal, 2018, 69 (3): 1114-1120.

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