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A Method of Ridge-NNG-Based Multivariate Fault Isolation in Presentence of Collinearity

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Intelligent Computing, Networked Control, and Their Engineering Applications (ICSEE 2017, LSMS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 762))

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Abstract

Multivariate fault isolation is a critical step for monitoring industrial chemical and biological processes. For some complex cases with strong correlation variables which commonly exist in the industry process, conventional methods may perform poorly. Therefore, to further improve the fault prediction accuracy, a fault isolation method based on the ridge nonnegative garrote variable selection algorithm (R-NNG) was proposed in this dissertation, it transformed the multivariate fault isolation problem into a variable selection problem in discriminant analysis, which is proven to be capable for handling strongly correlated variables by the application to the benchmark Tennessee Eastman (TE) process.

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References

  1. Zhao, C., Wang, W.: Efficient faulty variable selection and parsimonious reconstruction modelling for fault isolation. J. Process Control 38, 31–41 (2016)

    Article  Google Scholar 

  2. Westerhuis, J., Gurden, S., Smilde, A.: Generalized contribution plots in multivariate statistical process monitoring. Chemom. Intell. Lab. Syst. 51, 95–114 (2000)

    Article  Google Scholar 

  3. Liu, J., Wong, D.S.H., Chen, D.-S.: Bayesian filtering of the smearing effect: fault isolation in chemical process monitoring. J. Process Control 24, 1–21 (2014)

    Article  Google Scholar 

  4. Dunia, R., Qin, S.: Subspace approach to multidimensional fault identification and reconstruction. AIChE J. 44, 1813–1831 (1998)

    Article  Google Scholar 

  5. Qin, S.J.: Statistical process monitoring: basics and beyond. J. Chemom. 17, 480–502 (2003)

    Article  Google Scholar 

  6. Yue, H.H., Qin, S.J.: Reconstruction-based fault identification using a combined index. Ind. Eng. Chem. Res. 40, 4403–4414 (2001)

    Article  Google Scholar 

  7. Breiman, L.: Better subset regression using the nonnegative garrote. Technometrics 37(4), 373–384 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  8. Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Ann. Statist. 32(2), 407–499 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  9. Oh, Y.S., Mo, K.J., Yoon, E.S., Yoon, J.H.: Fault diagnosis based on weighted symptom tree and pattern matching. Ind. Eng. Chem. Res. 36, 2672–2678 (1997)

    Google Scholar 

  10. Chu, Y., Qin, S., Han, C.: Fault detection and operation mode identification based on pattern classification with variable selection. Ind. Eng. Chem. Res. 43, 1701–1710 (2004)

    Article  Google Scholar 

  11. Musulin, E., Yélamos, I., Puigjaner, L.: Integration of principal component analysis and fuzzy logic systems for comprehensive process fault detection and diagnosis. Ind. Eng. Chem. Res. 45, 1739–1750 (2006)

    Article  Google Scholar 

  12. Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)

    Article  Google Scholar 

  13. Downs, J.J., Vogel, E.F.: A plant-wide industrial process control problem. Comput. Chem. Eng. 17, 245–255 (1993)

    Article  Google Scholar 

  14. Kuang, T.H., Yan, Z., Yao, Y.: Multivariate fault isolation via variable selection in discriminant analysis. J. Process Control 35, 30–40 (2015)

    Article  Google Scholar 

  15. Kariwala, V., Odiowei, P.E., Cao, Y., Chen, T.: A branch and bound method for isolation of faulty variables through missing variable analysis. J. Process Control 20, 1198–1206 (2010)

    Article  Google Scholar 

  16. He, B., Yang, X., Chen, T., Zhang, J.: Reconstruction-based multivariate contribution analysis for fault isolation: a branch and bound approach. J. Process Control 22, 1228–1236 (2012)

    Article  Google Scholar 

  17. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 67, 301–320 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  18. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2012)

    MATH  Google Scholar 

  19. Downs, J., Vogel, E.: A plant-wide industrial process control problem. Comput. Chem. Eng. 17, 245–255 (1993)

    Article  Google Scholar 

  20. Chiang, L.H., Kotanchek, M.E., Kordon, A.K.: Fault diagnosis based on Fisher discriminant analysis and support vector machines. Comput. Chem. Eng. 28, 1389–1401 (2004)

    Article  Google Scholar 

  21. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.), 267–288(1996)

    Google Scholar 

  22. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 67, 301–320 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  23. Ricker, L.: Decentralized control of the Tennessee Eastman challenge process. J. Process Control 6, 205–221 (1996)

    Article  Google Scholar 

  24. Yoon, S., MacGregor, J.F.: Fault diagnosis with multivariate statistical models. Part I: using steady state fault signatures. J. Process Control 11, 387–400 (2001)

    Article  Google Scholar 

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Authors and Affiliations

  1. Shanghai Key Lab of Power Station Automation Technology, School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Yimin Guo, Jianguo Wang, Banghua Yang, Shiwei Ma & Minrui Fei

  2. Department of Chemical Engineering, National Tsing-Hua University, Hsin-Chu, 30013, Taiwan

    Yao Yuan

  3. Department of Chemical and Process Engineering, University of Surrey, Guildford, GU2 7XH, UK

    Chen Tao

Authors
  1. Yimin Guo
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  2. Jianguo Wang
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  3. Banghua Yang
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  4. Shiwei Ma
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  5. Minrui Fei
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  6. Yao Yuan
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  7. Chen Tao
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Corresponding author

Correspondence to Jianguo Wang .

Editor information

Editors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, China

    Dong Yue

  2. Shanghai University , Shanghai, China

    Chen Peng

  3. Shanghai University , Shanghai, China

    Dajun Du

  4. Nanjing University of Posts and Telecommunications, Nanjing, China

    Tengfei Zhang

  5. Shanghai University , Shanghai, China

    Min Zheng

  6. Swinburne University of Technology, Melbourne, Victoria, Australia

    Qinglong Han

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© 2017 Springer Nature Singapore Pte Ltd.

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Guo, Y. et al. (2017). A Method of Ridge-NNG-Based Multivariate Fault Isolation in Presentence of Collinearity. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_18

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  • DOI: https://doi.org/10.1007/978-981-10-6373-2_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6372-5

  • Online ISBN: 978-981-10-6373-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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