CIESC Journal ›› 2018, Vol. 69 ›› Issue (7): 3159-3166.doi: 10.11949/j.issn.0438-1157.20171629

Previous Articles     Next Articles

DLNS-PCA-based fault detection for multimode batch process

FENG Liwei1,2, ZHANG Cheng1,2, LI Yuan2, XIE Yanhong1,2   

  1. 1 College of Science, Shenyang University of Chemical Technology, Shenyang 110142, Liaoning, China;
    2 Research Center for Technical Process Fault Diagnosis and Safety, Shenyang University of Chemical Technology, Shenyang 110142, Liaoning, China
  • Received:2017-12-12 Revised:2018-02-12 Online:2018-07-05 Published:2018-04-09
  • Supported by:

    supported by the National Natural Science Foundation of China (61490701,61673279), the Project of Education Department in Liaoning Province (L2015432) and the Natural Science Foundation of Liaoning Province (2015020164).

Abstract:

Modern industrial products often require multiple production stages, and the fault detection of multi-stage production process has become an important issue. Multi-stage process data have the characteristics of multi center, different structure of each stage and so on. Aiming at the characteristics, a fault detection method based on double local neighborhood standardization and principal component analysis (DLNS-PCA) is proposed. Firstly, the double local neighborhood set of the sample is found. Secondly, the standard samples are obtained by using the information of the double local neighborhood set. Finally, the PCA method is used to detect the fault on the standard sample set. The DLNS can move the data centers of each stage to the same point, and adjust the degrees of dispersion of data at each stage to make its approximately equal, then multi-stage process data is fused to a single modal data following multivariate Gauss distribution. A fault detection of penicillin simulation process was carried out. The results showed that DLNS-PCA has higher fault detection rate than PCA, KPCA and FD-kNN methods. DLNS-PCA method improves the efficiency of multi-stage process fault detection.

Key words: multi stage process, fault detection, model, principal component analysis, process control

CLC Number: 

  • TP277

[1] WISE B M, GALLAGHER N B, BUTLER S W, et al. A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process[J]. Journal of Chemometrics, 1999, 13(3/4):379-396.
[2] CHERRY G A, QIN S J. Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis[J]. IEEE Transactions on Semiconductor Manufacturing, 2006, 19(2):159-172.
[3] GE Z, SONG Z. Semiconductor manufacturing process monitoring based on adaptive substatistical PCA[J]. IEEE Transactions on Semiconductor Manufacturing, 2010, 23(1):99-108.
[4] 张成, 李元. 基于统计模量分析间歇过程故障检测方法研究[J]. 仪器仪表学报, 2013, 34(9):2103-2110. ZHANG C, LI Y. Study on the fault-detection method in batch process based on statistical pattern analysis[J]. Chinese Journal of Scientific Instrument, 2013, 34(9):2103-2110.
[5] RATO T J, REIS M S. Advantage of using decorrelated residuals in dynamic principal component analysis for monitoring large-scale systems[J]. Industrial & Engineering Chemistry Research, 2013, 52(38):13685-13698.
[6] KRESTA J V, MACGREGOR J F, MARLIN T E. Multivariate statistical monitoring of process operating performance[J]. Canadian Journal of Chemical Engineering, 1991, 69(1):35-47.
[7] ZHAO S J, ZHANG J, XU Y M. Performance monitoring of processes with multiple operating modes through multiple PLS models[J]. Journal of Process Control, 2006, 16(7):763-772.
[8] LI G, QIN S J, ZHOU D. Geometric properties of partial least squares for process monitoring[J]. Automatica, 2010, 46(1):204-210.
[9] LEE J M, YOO C K, SANG W C, et al. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science, 2004, 59(1):223-234.
[10] ROSIPAL R, TREJO L J. Kernel partial least squares regression in reproducing kernel Hilbert space[J]. Journal of Machine Learning Research, 2001, 2(3):97-123.
[11] ZHANG Y, LI S, HU Z. Improved multi-scale kernel principal component analysis and its application for fault detection[J]. Chemical Engineering Research & Design, 2012, 90(9):1271-1280.
[12] ZHANG Y, HU Z. Multivariate process monitoring and analysis based on multi-scale KPLS[J]. Chemical Engineering Research & Design, 2011, 89(12):2667-2678.
[13] SHI J Z, JIE Z, YONG M X. Monitoring of processes with multiple operating modes through multiple principal component analysis models[J]. Industrial & Engineering Chemistry Research, 2004, 43(22):7025-7035.
[14] NG Y S, SRINIVASAN R. An adjoined multi-model approach for monitoring batch and transient operations[J]. Computers & Chemical Engineering, 2009, 33(4):887-902.
[15] CHANG K Y, VILLEZ K, LEE I B, et al. Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor[J]. Biotechnology & Bioengineering, 2007, 96(4):687-701.
[16] GE Z, SONG Z. Multimode process monitoring based on Bayesian method[J]. Journal of Chemometrics, 2010, 23(12):636-650.
[17] NATARAJAN S, SRINIVASAN R. Multi-model based process condition monitoring of offshore oil and gas production process[J]. Chemical Engineering Research & Design, 2010, 88(5):572-591.
[18] YU J, QIN S J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models[J]. AIChE Journal, 2008, 54(7):1811-1829.
[19] HE Q P, WANG J. Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes[J].IEEE Transactions on Semiconductor Manufacturing, 2007, 20(4):345-354.
[20] ZHOU Z, WEN C, YANG C. Fault detection using random projections and k-nearest neighbor rule for semiconductor manufacturing processes[J]. IEEE Transactions on Semiconductor Manufacturing, 2015, 28(1):70-79.
[21] HE Q P, WANG J. Principal component based k-nearest-neighbor rule for semiconductor process fault detection[C]//Proceedings of the American Control Conference. Washington, USA, 2008:1606-1611.
[22] VERDIER G, FERREIRA A. Fault detection with an adaptive distance for the k-nearest neighbors rule[C]//International Conference on Computers & Industrial Engineering. Troyes, France:IEEE, 2009:1273-1278.
[23] 冯立伟, 张成, 李元, 等. 基于局部马氏距离的加权k近邻故障检测方法[J]. 通化师范学院学报, 2017, 38(4):57-63. FENG L W, ZHANG C, LI Y, et al. Local Mahalanobis distance based weighted k nearest neighbor rule for fault detection[J]. Journal of Tonghua Normal University, 2017, 38(4):57-63.
[24] LANE S, MARYIN E B, KOOIJMANS R, et al. Performance monitoring of a multi-product semi-batch process[J]. Journal of Process Control, 2001, 11(1):1-11.
[25] WANG G, LIU J, ZHANG Y, et al. A novel multi-mode data processing method and its application in industrial process monitoring[J]. Journal of Chemometrics, 2015, 29(2):126-138.
[26] 马贺贺, 胡益, 侍洪波. 基于距离空间统计量分析的多模态过程无监督故障检测[J]. 化工学报, 2012, 63(3):873-880. MA H H, HU Y, SHI H B. Unsupervised fault detection for multimode processes using distance space statistics analysis[J]. CIESC Journal, 2012, 63(3):873-880.
[27] LEE J M, YOO C K, LEE I B. Fault detection of batch processes using multiway kernel principal component analysis[J]. Computers & Chemical Engineering, 2004, 28(9):1837-1847.
[28] TAN S, WANG F, PENG J, et al. Multimode process monitoring based on mode identification[J]. Industrial & Engineering Chemistry Research, 2011, 51(1):374-388.
[29] 刘毅, 王海清. Pensim仿真平台在青霉素发酵过程的应用研究[J]. 系统仿真学报, 2006, 18(12):3524-3527. LIU Y, WANG H Q. Pensim simulator and its application in penicillin fermentation process[J]. Journal of System Simulation, 2006, 18(12):3524-3527.
[30] WANG G, LIU J, LI Y, et al. Fault detection based on diffusion maps and k nearest neighbor diffusion distance of feature space[J]. Journal of Chemical Engineering of Japan, 2015, 48(9):756-765.

[1] TANG Junmiao, YU Haizhen, SHI Xuhua, TONG Chudong. Dynamic monitoring of chemical processes based on latent variable auto-regressive algorithm [J]. CIESC Journal, 2019, 70(3): 987-994.
[2] TIAN Tao, LIU Bing, SHI Meisheng, AN Yaxiong, MA Jun, ZHANG Yanjun, XU Xinxi, ZHANG Donghui. Experiment and simulation of PSA process for small oxygen generator with two adsorption beds [J]. CIESC Journal, 2019, 70(3): 969-978.
[3] SHI Bowen, YIN Yanyan, LIU Fei. Optimal control strategies combined with PSO and control vector parameterization for batchwise chemical process [J]. CIESC Journal, 2019, 70(3): 979-986.
[4] XU Baochang, ZHANG Hua, WANG Jinshan. Partial approximate least absolute deviation for nonlinear system identification based on radial basis function [J]. CIESC Journal, 2019, 70(2): 653-660.
[5] WANG Zhizhen, ZOU Zhiyun. Nonlinear predictive control strategies of pH neutralization process based on neural networks [J]. CIESC Journal, 2019, 70(2): 678-686.
[6] ZHANG Zhuang, DENG Chun, SUN Hailan, FENG Xiao. Modeling and material balance analysis of desalination systems [J]. CIESC Journal, 2019, 70(2): 646-652.
[7] HUANG Xiuhui, WANG Jun, CUI Guomin. Dynamic simulation and analysis of control strategies of acetic acid dehydration tower in PTA plant [J]. CIESC Journal, 2019, 70(2): 625-633.
[8] YE Zhencheng, ZHOU Huanlan, RAO Debao. Hybrid modeling and optimization of acetylene hydrogenation process [J]. CIESC Journal, 2019, 70(2): 496-507.
[9] WANG Shenhan, KANG Lunwei, ZHANG Bingjian, CHEN Qinglin, PAN Ming, HE Chang. Energy minimization in hybrid desalination system of reverse osmosis and pressure retarded osmosis [J]. CIESC Journal, 2019, 70(2): 617-624.
[10] JI Wenpeng, YANG Huizhong. Multi-manifold soft sensor based on modified expanding search clustering algorithm [J]. CIESC Journal, 2019, 70(2): 723-729.
[11] GU Bingbin, XIONG Weili. Fault diagnosis based on PCA method with multi-block information extraction [J]. CIESC Journal, 2019, 70(2): 736-749.
[12] MU Rui, LE Gaoyang, YANG Huizhong. Estimation method of dissolved gas quantity in COD determination based on O3/UV [J]. CIESC Journal, 2019, 70(2): 730-735.
[13] GENG Zhiqiang, JING Shaoxing, BAI Ju, WANG Zhongkai, ZHU Qunxiong, HAN Yongming. Improved intelligent warning method based on MWSPCA-CBR and its application in petrochemical industries [J]. CIESC Journal, 2019, 70(2): 572-580.
[14] XUE Yongfei, WANG Yalin, SUN Bei, LI Qianzhong, SUN Jiazhou. Improved state transfer algorithm-based kinetics parameter estimation for cascaded plug flow reactors [J]. CIESC Journal, 2019, 70(2): 607-616.
[15] ZHANG Xiaohan, WANG Pingjiang, GU Xiangbai, XU Yuan, HE Yanlin, ZHU Qunxiong. Research on principal components extraction based robust extreme learning machine(PCE-RELM) and its application to modeling chemical processes [J]. CIESC Journal, 2019, 70(2): 475-480.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!