|Table of Contents|

Fault monitoring for batch process based on multi-stage ICA-SVDD(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年02期
Page:
195-
Research Field:
Publishing date:

Info

Title:
Fault monitoring for batch process based on multi-stage ICA-SVDD
Author(s):
Zheng Hao1Xiong Weili12
1.School of Internet of Things Engineering; 2.Key Laboratory of Advanced Process Control forLight Industry of Ministry of Education,Jiangnan University,Wuxi 214122,China
Keywords:
batch processes stage division independent component analysis support vector data description fault monitoring
PACS:
TP301
DOI:
10.14177/j.cnki.32-1397n.2018.42.02.010
Abstract:
In view of the characteristics of multi-stages and the non-Gaussian of the batch process,an improved stage division and fault monitoring method is proposed. Firstly,the stage is divided according to the similarity of each time slice and the k-means algorithm,and then the independent component analysis(ICA)method is used to extract the feature information of non-Gaussian of each stage respectively. Finally,the support vector data description(SVDD)algorithm is introduced to establish a statistical analysis model for the independent components and the remaining Gaussian residual spaces,and the whole process is monitored. The feasibility and effectiveness of the proposed method is verified by an actual fault monitoring application for the semiconductor etch process.

References:

[1] Ge Z,Song Z,Gao F. Review of recent research on data-based process monitoring[J]. Industrial & Engineering Chemistry Research,2013,52(10):3543-3562.
[2]周东华,史建涛,何潇. 动态系统间歇故障诊断技术综述[J]. 自动化学报,2014,40(2):161-171.
Zhou Donghua,Shi Jiantao,He Xiao. Review of intermittent fault diagnosis techniques for dynamic systems[J]. Acta Automatica Sinica,2014,40(2):161-171.
[3]杨青,孙佰聪,朱美臣,等. 基于小波包熵和聚类分析的滚动轴承故障诊断方法[J]. 南京理工大学学报,2013,37(4):517-523.
Yang Qing,Sun Baicong,Zhu Meichen,et al. Rolling bearing fault diagnosis method based on wavelet packet entropy and clustering analysis[J]. Journal of Nanjing University of Science and Technology,2013,37(4):517-523.
[4]Yao Y,Gao F. A survey on multistage/multiphase statistical modeling methods for batch processes[J]. Annual Reviews in Control,2009,33(2):172-183.
[5]Lee J M,Qin S J,Lee I B. Fault detection and diagnosis based on modified independent component analysis[J]. Aiche Journal,2006,52(10):3501-3514.
[6]郑宇杰,杨静宇,吴小俊,等. 基于对称ICA的特征抽取方法及其在人脸识别中的应用[J]. 南京理工大学学报,2006,19(1):116-212.
Zheng Yujie,Yang Jingyu,Wu Xiaojun,et al. Feature extraction based on symmetrical ICA and its application to face recognition[J]. Journal of Nanjing University of Science and Technology,2006,19(1):116-212.
[7]曾生根. 快速独立分量分析方法及其在图像分析中的若干应用研究[D]. 南京:南京理工大学自动化学院,2004:26-40.
[8]高学金,崔宁,张亚潮,等. 基于粒子群优化MICA的间歇过程故障监测[J]. 仪器仪表学报,2015,36(1):152-159.
Gao Xuejin,Cui Ning,Zhang Yachao,et al. Fault detection of batch processes based on MICA optimized with PSO[J]. Chinese Journal of Scientific Instrument,2015,36(1):152-159.
[9]Jia M,Chu F,Wang F,et al. On-line batch process monitoring using batch dynamic kernel principal component analysis[J]. Chemometrics & Intelligent Laboratory Systems,2010,101(2):110-122.
[10]Chen J,Liu K C. On-line batch process monitoring using dynamic PCA and dynamic PLS models[J]. Chemical Engineering Science,2002,57(1):63-75.
[11]Yu J,Qin S J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models[J]. Aiche Journal,2008,54(7):1811-1829.
[12]张淑美,王福利,谭帅,等. 多模态过程的全自动离线模态识别方法[J]. 自动化学报,2016,42(1):60-80.
Zhang Shumei,Wang Fuli,Tan Shuai,et al. A fully automatic offline mode identification method for multi-mode processes[J]. Acta Automatica Sinica,2016,42(1):60-80.
[13]胡永兵,高学金,李亚芬,等. 基于仿射传播聚类子集主元分析的间歇过程监测方法[J]. 化工学报,2016,67(5):1989-1997.
Hu Yongbing,Gao Xuejin,Li Yafen,et al. Subset multiway principal component analysis monitoring for batch process based on affinity propagation clustering[J]. CIESC Journal,2016,67(5):1989-1997.
[14]Tax D M J,Duin R P W. Support vector domain description[J]. Pattern Recognition Letters,1999,20(11-13):1191-1199.
[15]Tax D M J,Duin R P W. Support vector data description[J]. Machine Learning,2004,54(1):45-66.
[16]Lee S W,Park J,Lee S W. Low resolution face recognition based on support vector data description[J]. Pattern Recognition,2006,39(9):1809-1812.
[17]Yao M,Wang H,Xu W. Batch process monitoring based on functional data analysis and support vector data description[J]. Journal of Process Control,2014,24(7):1085-1097.
[18]杨雅伟,宋冰,侍洪波. 多SVDD模型的多模态过程监控方法[J]. 化工学报,2015,66(11):4526-4533.
Yang Yawei,Song Bing,Shi Hongbo. Multimode processes monitoring method via multiple SVDD model[J]. CIESC Journal,2015,66(11):4526-4533.
[19]Ge Z Q,Song Z H. Bagging support vector data description model for batch process monitoring[J]. Journal of Process Control,2013,23(8):1090-1096.
[20]谢彦红,孙呈敖,李元. 基于滑动窗口SVDD的间歇过程故障监测[J]. 信息与控制,2015,44(5):531-537.
Xie Yanhong,Sun Chengao,Li Yuan. Fault monitoring of batch process based on moving window SVDD[J]. Information and Control,2015,44(5):531-537.
[21]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.
[22]陶栋琦,薄翠梅,易辉. 基于多时段MPCA的半导体蚀刻过程监测方法[J]. 传感技术学报,2015(6):798-802.
Tao Dongqi,Bo Cuimei,Yi Hui. Semiconductor etch process monitoring based on multi-stage MPCA[J]. Chinese Journal of Sensors and Actuators,2015(6):798-802.
[23]Ge Z,Gao F,Song Z. Batch process monitoring based on support vector data description method[J]. Journal of Process Control,2011,21(6):949-959.
[24]Li G,Hu Y,Chen H,et al. An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm[J]. Energy & Buildings,2016,116:104-113.

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Last Update: 2018-04-30