Advertisement

Cluster Computing

, Volume 22, Supplement 6, pp 14855–14866 | Cite as

On-line modeling and monitoring for multi-operation batch processes with infinite data types

  • Yajun WangEmail author
  • Fuming Sun
  • Dongjuan Li
Article
  • 43 Downloads

Abstract

For complex industrial processes with frequent operating characteristics, process data types will be infinite due to the randomness and uncertainty of operation. Additionally, the process data follow serious non-Gaussian distribution. In this paper, an efficient q-nearest-neighbor standardization principal component analysis (q-NNS PCA) based on-line modeling method is proposed to handle complex data distributions and incursive frequent operation shifts. Due to the limitation of initial modeling data, the modeling data structure needs to be continuously replenished with accumulation of new normal batches. The on-line modeling method is proposed to avoid the complexity of model updating and establishment of massive offline models as well as the difficulty of the multiple models selection. For each test sample online, the q-NNS method can search its modeling data coming from the same operation to establish monitoring model, which settles non-Gaussian distribution problem. The proposed method is illustrated with a 120t ladle furnace (LF) steelmaking process. The comparison of monitoring results demonstrates that the proposed method is superior to multiple PCA and MKPCA methods and can achieve accurate and prompt detection of various types of faults in multi-operation processes.

Keywords

On-line modeling and monitoring Non-Gaussian distribution q-nearest-neighbor standardization PCA Multi-operation process 120t ladle furnace steelmaking process 

Notes

Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 61503169, 61572244, 61603164), Liaoning province innovation talent project (LR2016057), the Natural Science Foundation of Liaoning province (Grant: 2015020102).

References

  1. 1.
    Wang, Y.J., Jia, M.X., Mao, Z.Z.: A fast monitoring method for multiple operating batch processes with incomplete modeling data types. J. Ind. Eng. Chem. 21, 328–337 (2015)CrossRefGoogle Scholar
  2. 2.
    Pourbabaee, B., Meskin, N., Khorasani, K.: Sensor fault detection, isolation, and identification using multiple-model-based hybrid kalman filter for gas turbine engines. IEEE Trans. Control Syst. Technol. 24(4), 1184–1200 (2016)CrossRefGoogle Scholar
  3. 3.
    Villez, K., Habermacher, J.: Shape anomaly detection for process monitoring of a sequencing batch reactor. Comput. Chem. Eng. 91, 365–379 (2016)CrossRefGoogle Scholar
  4. 4.
    Wang, Y.J., Sun, F.M.: Multiple dynamic kernel clustering based online monitoring for batch processes. CIESC J. 65(12), 4095 (2014)Google Scholar
  5. 5.
    Zhao, S.Y., Huang, B., Liu, F.: Detection and diagnosis of multiple faults with uncertain modeling parameters. IEEE Trans. Control Syst. Technol. 25(5), 1873–1881 (2017)CrossRefGoogle Scholar
  6. 6.
    Zhu, J., Ge, Z., Song, Z.: Non-Gaussian Industrial process monitoring with probabilistic Independent component analysis. IEEE Trans. Autom. Sci. Eng. 14(2), 1309–1319 (2017)CrossRefGoogle Scholar
  7. 7.
    Zhang, S.M., Zhao, C.H., Wang, S., Wang, F.L.: Pseudo time-slice construction using variable moving window-k nearest neighbor (VMW-kNN) rule for sequential uneven phase division and batch process monitoring. Ind. Eng. Chem. Res. 56(3), 728–740 (2017)CrossRefGoogle Scholar
  8. 8.
    Jong-Min, L., ChangKyoo, Y., In-Beum, L.: Fault detection of batch processes using multi-way kernel principal component analysis. Comput. Chem. Eng. 28, 1837–1847 (2004)CrossRefGoogle Scholar
  9. 9.
    Kim, M.H., Yoo, C.K.: Multivariate monitoring for time-derivative non-Gaussian batch process. Korean J. Chem. Eng. 25(5), 947–954 (2008)CrossRefGoogle Scholar
  10. 10.
    Choi, S.W., Lee, I.B.: Nonlinear dynamic process monitoring based on dynamic kernel PCA. Chem. Eng. Sci. 59, 5897–5908 (2004)CrossRefGoogle Scholar
  11. 11.
    Yu, J., Qin, S.J.: Multiway gaussian mixture model based multiphase batch process monitoring. Ind. Eng. Chem. Res. 48, 8585–8594 (2009)CrossRefGoogle Scholar
  12. 12.
    Liu, J.L.: Modeling a large-scale nonlinear system using adaptive Takagi-Sugeno fuzzy model on PCA subspace. Ind. Eng. Chem. Res. 46, 788–800 (2007)CrossRefGoogle Scholar
  13. 13.
    Wang, X., Kruger, U., Irwin, G.W.: Process monitoring approach using fast moving window PCA. Ind. Eng. Chem. Res. 44, 5691–5702 (2005)CrossRefGoogle Scholar
  14. 14.
    Zhao, C.H., Wang, F.L., Gao, F.R., et al.: Adaptive monitoring method for batch processes based on phase dissimilarity updating with limited modeling data. Ind. Eng. Chem. Res. 46, 4943–4953 (2007)CrossRefGoogle Scholar
  15. 15.
    Petković, M., Rapaić, M.R., Jeličić, Z.D., et al.: On-line adaptive clustering for process monitoring and fault detection. Expert Syst. Appl. 39, 10226–10235 (2012)CrossRefGoogle Scholar
  16. 16.
    Tong, C.D., Palazoglu, A., Yan, X.F.: An adaptive multimode process monitoring strategy based on mode clustering and mode unfolding. J. Process Control 23, 1497–1507 (2013)CrossRefGoogle Scholar
  17. 17.
    Ma, Y.X., Shi, H.B., Wang, M.L.: Adaptive local outlier probability for dynamic process monitoring. Chin. J. Chem. Eng. 22(7), 820–827 (2014)CrossRefGoogle Scholar
  18. 18.
    Zhang, X., Xu, Z.: Hesitant fuzzy agglomerative hierarchical clustering algorithms. Int. J. Syst. Sci. 46(3), 562–576 (2015)CrossRefGoogle Scholar
  19. 19.
    Almeida, J.A.S., Barbosa, L.M.S., Pais, A.A.C.C., et al.: Improving hierarchical cluster analysis: a new method with outlier detection and automatic clustering. Chemom. Intell. Lab. Syst. 87, 208–217 (2007)CrossRefGoogle Scholar
  20. 20.
    Kleiner, F.W.: Tree decomposition-based indexing for efficient shortest path and nearest neighbors query answering on graphs. J. Comput. Syst. Sci. 82(1), 23–44 (2016)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Lv, W., Mao, Z., Yuan, P.: Ladle furnace steel temperature prediction model based on partial linear regularization networks with sparse representation. Steel Res. Int. 83, 288–296 (2012)CrossRefGoogle Scholar
  22. 22.
    Zhou, P., Song, H.D., Wang, H., et al.: Data-driven nonlinear subspace modeling for prediction and control of molten iron quality indices in blast furnace ironmaking. IEEE Trans. Control Syst. Technol. 25(5), 1761–1774 (2017)CrossRefGoogle Scholar
  23. 23.
    Li, Y., Mao, Z., Wang, Y., et al.: Model predictive control synthesis approach of electrode regulator system for electric arc furnace. J. Iron. Steel Res. Int. 11, 20–25 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronics and Information EngineeringLiaoning University of TechnologyJinzhouChina
  2. 2.School of Chemical and Environmental EngineeringLiaoning University of TechnologyJinzhouChina

Personalised recommendations