Fault detection algorithm of batch process based on improved neighborhood preserving embedding-independent component analysis

被引：0

作者：

Zhao X. ^{[1
,2
,3
]}

Yao H. ^{[1
]}

机构：

[1] College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou

[2] Gansu Provincial Key Laboratory of Advanced Control for Industrial Processes, Lanzhou

[3] National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2021年 / 27卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Batch process; Differential strategy; Fault detection; Gaussian and non-Gaussian; Nonlinear;

D O I：

10.13196/j.cims.2021.04.010

中图分类号：

学科分类号：

摘要：

Aiming at the problem of bad fault detection effect of batch process because of its non-linearity and mixed distribution of Gaussian and non-Gaussian, Multi-way Differencial Neighborhood Preserving Embedding-Weighted and Differencial Independent Component Analysis (MDNPE-WDICA) algorithm for fault detection of batch process was proposed. The original data space was divided into Gaussian and non-Gaussian subspaces by Jarque-Bera testmethod (J-B test). In Gaussian subspace, MDNPE algorithm was proposed by combining differential strategy with NPE algorithm to preserve the local structure invariant and deal with the nonlinearity of data while the dimension of data was reduced, which could overcome the computational complexity caused by the introduction of the kernel function. In non-Gaussian subspace, WDICA algorithm was proposed by combining weighted differential strategy with ICA algorithm to solve the nonlinearity of data while the non-Gaussian information of data was fully extracted, and the local information of data was effectively used. A new monitoring statistic was established by Bayesian inference to realize fault detection for the whole batch process. The simulation results of penicillin production process demonstrated that the proposed algorithm was feasible and effective. © 2021, Editorial Department of CIMS. All right reserved.

引用

页码：1062 / 1071

页数：9

共 25 条

[1]

LU Ningyun, WANG Fuli, GAO Furong, Et al., Statistical modeling and online monitoring for batch processes, Acta Automatica Sinica, 23, 3, pp. 400-410, (2006)

[2]

WANG Jianlin, MA Linyu, QIU Kepeng, Et al., Multi-phase batch process fault detection based on support vector data description, Chinese Journal of Scientific Instrument, 38, 11, pp. 2752-2761, (2017)

[3]

GE Zhiqiang, SONG Zhihuan, GAO Furong, Review of recent research on data-based process monitoring, Industrial & Engineering Chemistry Research, 52, 10, pp. 3543-3562, (2013)

[4]

CHANG Peng, WANG Pu, GAO Xuejin, Fault monitoring batch process based on statistics pattern analysis of T-KPLS, CIESC Journal, 66, 1, pp. 265-271, (2015)

[5]

TONG Chudong, LAN Ting, SHI Xuhua, Fault detection and diagnosis of dynamic processes using weighted dynamic decentralized PCA approach, Chemometrics and Intelligent Laboratory Systems, 161, 15, pp. 34-42, (2017)

[6]

KONG Xiangyu, CAO Zehao, AN Qiusheng, Et al., Review of partial least squares linear models and their nonlinear dynamic expansion models, Control and Decision, 33, 9, pp. 1537-1548, (2018)

[7]

WANG Li, SHI Hongbo, Online batch process monitoring based on kernel ICA, CIESC Journal, 61, 5, pp. 1183-1189, (2010)

[8]

HUI Yongyong, ZHAO Xiaoqiang, Batch process monitoring with Gaussian and non-Gaussian joint indicator based on WICA-WGNPE, Chinese Journal of Scientific Instrument, 39, 1, pp. 190-199, (2018)

[9]

GAO Xuejin, CUI Ning, ZHANG Yachao, Et al., Fault detection of batch processes based on MICA optimized with PSO, Chinese Journal of Scientific Instrument, 36, 1, pp. 152-159, (2015)

[10]

CHANG Peng, QIAO Junfei, WANG Pu, Et al., Monitoring non-Gaussian and non-linear batch process based on multi-way kernel entropy component analysis, CIESC Journal, 69, 3, pp. 1200-1206, (2018)

← 1 2 3 →