JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (5): 44-50.doi: 10.6040/j.issn.1672-3961.0.2017.171

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A fault detection method based on modified canonical correlation analysis

CHEN Zhiwen1, PENG Tao1*, YANG Chunhua 1, HE Zhangming2, YANG Chao1, YANG Xiaoyue1   

  1. 1. School of Information Science and Engineering, University of Central South, Changsha 410083, Hunan, China;
    2. College of Science, National University of Defense Technology, Changsha 410083, Hunan, China
  • Received:2017-04-18 Online:2017-10-20 Published:2017-04-18

Abstract: In order to improve the effectiveness of the fault detection(FD)method based on standard canonical correlation analysis(CCA), the original residual generation was modified. By analyzing the statistical characteristics of the residual signal and changing the residual generation mode, the improved residual generation method did not depend on the selection of the number of principal components, so that the fault detection performance would be free of such a selection. The proposed method was further applied to the Tennessee Eastman benchmark process, in which four typical faults were simulated. The achieved results showed that the proposed method could successfully detect the faults. Due to the different fault sensitivity of the two test statistics, it could be found that the fault detectability of the two test statistics were different.

Key words: data-driven, canonical correlation analysis, Tennessee Eastman process, fault detection

CLC Number: 

  • TP206+.3
[1] 周东华,叶银忠. 现代故障诊断与容错控制[M].北京:清华大学出版社,2000.
[2] GERTLER J. Fault detection and diagnosis in engineering systems[M]. New York: Marcel Dekker, 1998.
[3] DING S X. Model-based fault diagnosis techniques-design schemes, algorithms and tools[M]. 2nd ed. London: Springer-Verlag, 2013,
[4] GE Zhiqiang, SONG Zhihuan, GAO Furong. Review of recent research on data-based process monitoring[J]. Industrial Engineering Chemical Research, 2013, 52(10):3543-3562.
[5] ZHANG Kai, HAO Haiyang, CHEN Zhiwen, et al. A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches[J]. Journal of Process Control, 2015, 33:112-126.
[6] YIN Shen, DING S X, HAGHANI A. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process[J]. Journal of Process Control, 2012, 22:1567-1581.
[7] QIN S J. Survey on data-driven industrial process monitoring and diagnosis[J]. Annual Reviews in Control, 2012, 36(2):220-234.
[8] YIN Shen, LIU Lei, HOU Jian. A multivariate statistical combination forecasting method for product quality evaluation[J]. Information Science, 2016, 355-356:229-236.
[9] YIN Shen, WANG Guang, GAO Huijun. Data-driven process monitoring based on modified orthogonal projections to latent structures[J]. IEEE Transactions on Control System Technology, 2016, 24(4):1480-1487.
[10] MAJID N A, TAYLOR M P, CHEN J J, et al. Aluminium process fault detection by multiway principal component analysis[J]. Control Engineering Practice, 2011, 19(4):367-379.
[11] THORNIHILL N F, HORCH A. Advances and new directions in plant-wide disturbance detection and diagnosis [J]. Control Engineering Practice, 2007, 15(10):1196-1206.
[12] ZHOU Donghua, LI Gang, QIN S J. Total projection to latent structure for process monitoring[J]. AIChE J, 2010,56(1):168-178.
[13] MACGREGOR J F, KOURTI T. Statistical process control of multivariate processes[J]. Control Engineering Practice, 1995, 3(3):403-414.
[14] KANO M, HASEBE S, HASHIMOTO I, et al. A new multivariate statistical process monitoring method using principal component analysis [J]. Computer Chemometrics Engineering, 2001, 25(7-8):1103-1113.
[15] ZHANG Yingwei, ZHOU Hong, QIN S J, et al. Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares[J]. IEEE Transactions on Industrial Informatics, 2010, 6(1):3-10.
[16] DING S X. Data-driven design of fault diagnosis and fault-tolerant control systems[M]. London: Springer-Verlag, 2014.
[17] CHEN Zhiwen, ZHANG Kai, DING S X, et al. Improved canonical correlation analysis-based fault detection methods for industrial processes[J]. Journal of Process Control, 2016, 41:26-34.
[18] 彭开香, 马亮, 张凯. 复杂工业过程质量相关的故障检测与诊断技术综述[J]. 自动化学报, 2017, 43(2): 1-17. PENG Kaixiang, MA Liang, ZHANG Kai. Review of quality-related fault detection and diagnosis techniques for complex industrial processes [J]. Acta Automatica Sinica, 2017, 43(3): 349-365.
[19] CHEN Zhiwen, DING S X, ZHANG Kai, et al. Canonical correlation analysis-based fault detection methods with application to alumina evaporation process [J]. Control Engineering Practice, 2016, 46:51-58.
[20] ANDERSON T W. An introduction to multivariate statistical analysis[M]. Second edition. New York: John Wiley and Sons, LTD, 1984.
[21] DOWNS J, FOGEL E. A plant-wide industrial process control problem[J]. Computer Chemistry Engineering, 1993, 17(3):245-255.
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