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Multistage Condition Monitoring of Batch Process Based on Multi-boundary Hypersphere SVDD with Modified Bat Algorithm

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Abstract

Multistage characteristic has become one of the essential issues of batch process and several stage division approaches have been introduced to monitor the process. As the non-Gaussian and nonlinear problems of batch process, a hybrid intelligent method is developed to monitor the multistage conditions in this paper. The proposed algorithm includes converged stage division (CSD), multi-boundary hypersphere support vector data description (MH-SVDD), and modified bat algorithm (MBA). CSD algorithm is utilized to process the data and make the stage division, which consists of data length processing, three-dimension unfolding, and K-means clustering. MH-SVDD algorithm is to construct two hyperspheres, which can overcome the deficiency of traditional boundary SVDD. The Gaussian kernel function width parameter of MH-SVDD plays a very significant role in multistage fault monitoring, a modified bat algorithm is established to select the optimal parameter. The experimental of the semiconductor etching process is described, and the results demonstrate that the proposed model can gain higher fault monitoring accuracy in multistage condition monitoring of the batch process.

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References

  1. Ge, Z.; Song, Z.; Gao, F.: Review of recent research on data-based process monitoring. Ind. Eng. Chem. Res. 2013(52), 3543–3562 (2013)

    Google Scholar 

  2. Nomikos, P.; Macgregor, J.F.: Monitoring batch processes using multi-way principal component analysis. AIChE J. 40(8), 1361–1375 (2010)

    Google Scholar 

  3. Yi, H.; Ma, H.; Shi, H.: Enhanced batch process monitoring using just-in-time-learning based kernel partial least squares. Chemometr. Intell. Lab. Syst. 123(3), 15–27 (2013)

    Google Scholar 

  4. Yu, J.; Chen, J.Y.; Rashid, M.M.: Multiway independent component analysis mixture model and mutual information based fault detection and diagnosis approach of multiphase batch processes. AIChE J. 59(8), 2761–2779 (2013)

    Google Scholar 

  5. Jaworski, A.; Wikiel, K.; Wikiel, H.: Application of multiblock and hierarchical PCA and PLS models for analysis of ac voltammetric data. Electroanalysis 17(15–16), 1477–1485 (2010)

    Google Scholar 

  6. Lu, N.; Yao, Y.; Gao, F.; et al.: Two-dimensional dynamic PCA for batch process monitoring. AIChE J. 51(12), 3300–3304 (2010)

    Google Scholar 

  7. Qi,Y.; Wang, P.; Gao, X.: Enhanced batch process monitoring and quality prediction using multi-phase dynamic PLS. In: Control Conference, pp. 168–173 (2011).

  8. Ningyun, L.U.; Gao, F.; Wang, F.: Sub-PCA modelling and on-line monitoring strategy for batch processes. AIChE J. 50(1), 255–259 (2010)

    Google Scholar 

  9. Yu, J.: Local and global principal component analysis for process monitoring. J. Process Control 22(7), 1358–1373 (2012)

    Google Scholar 

  10. Li, C.; Pu, W.; Gao, X.: Improved multistage online monitoring strategy for batch process. In: Control Conference, pp. 100–104 (2016).

  11. Yao, Y.; Gao, F.: A survey on multistage/multiphase statistical modelling methods for batch processes. Ann. Rev. Control 33(2), 172–183 (2009)

    Google Scholar 

  12. Yu, J.; Qin, S.J.: Multiway gaussian mixture model based multiphase batch process monitoring. Ind. Eng. Chem. Res. 48(18), 8585–8594 (2009)

    Google Scholar 

  13. Sun, W.; Meng, Y.; Palazoglu, A.; et al.: A method for multiphase batch process monitoring based on auto phase identification. J. Process Control 21(4), 627–638 (2011)

    Google Scholar 

  14. Zhao, C.; Mo, S.; Gao, F.; et al.: Statistical analysis and online monitoring for handling multiphase batch processes with varying durations. J. Process Control 21(6), 817–829 (2011)

    Google Scholar 

  15. Yao, M.; Wang, H.; Xu, W.: Batch process monitoring based on functional data analysis and support vector data description. J. Process Control 24(7), 1085–1097 (2014)

    Google Scholar 

  16. Zhe, Z.; Wen, C.; Yang, C.: Fault detection using random projections and k-nearest neighbor rule for semiconductor manufacturing processes. IEEE Trans. Semicond. Manuf. 28(1), 70–79 (2015)

    Google Scholar 

  17. Yu, J.: Hidden Markov models combining local and global information for nonlinear and multimodal process monitoring. J. Process Control 20(3), 344–359 (2010)

    Google Scholar 

  18. Tax, D.M.J.; Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)

    MATH  Google Scholar 

  19. Jiang, Q.; Yan, X.: Just-in-time reorganized PCA integrated with SVDD for chemical process monitoring. AIChE J. 60(3), 949–965 (2014)

    Google Scholar 

  20. Sun, R.; Tsung, F.: A kernel-distance-based multivariate control chart using support vector methods. Int. J. Prod. Res. 41(13), 2975–2989 (2003)

    MATH  Google Scholar 

  21. Kumar, S.; Choudhary, A.K.; Kumar, M.; et al.: Kernel distance-based robust support vector methods and its application in developing a robust K-chart. Int. J. Prod. Res. 44(1), 77–96 (2006)

    MathSciNet  MATH  Google Scholar 

  22. Ning, X.; Tsung, F.: Improved design of kernel distance–based charts using support vector methods. IIE Trans. 45(4), 464–476 (2013)

    Google Scholar 

  23. Lv, Z.; Yan, X.: Hierarchical support vector data description for batch process monitoring. Ind. Eng. Chem. Res. 55(34), 9205–9214 (2016)

    Google Scholar 

  24. Wang, J.; Liu, W.; Qiu, K.; et al.: Dynamic hypersphere SVDD without describing boundary for one-class classification. Neural Comput. Appl. 3, 1–11 (2017)

    Google Scholar 

  25. Khediri, I.B.; Weihs, C.; Limam, M.: Kernel k-means clustering based local support vector domain description fault detection of multimodal processes. Expert Syst. Appl. 39(2), 2166–2171 (2012)

    Google Scholar 

  26. Sukchotrat, T.; Kim, S.B.; Tsung, F.: One-class classification-based control charts for multivariate process monitoring. IIE Trans. 42(2), 107–120 (2009)

    Google Scholar 

  27. Fuxin, N.: Support vector machine model for predicting sand liquefaction based on Grid-Search method. Chin. J. Appl. Mech. 28(1), 24–28 (2011)

    Google Scholar 

  28. Xiao, Y.; Liu, B.; Hao, Z.; et al.: A K-Farthest-Neighbor-based approach for support vector data description. Appl. Intell. 41(1), 196–211 (2014)

    Google Scholar 

  29. Kaveh, A.; Khayatazad, M.: A new meta-heuristic method: ray optimization. Comput. Struct. 112–113(4), 283–294 (2012)

    Google Scholar 

  30. Pang, S.; Li, S.; Xiao, J.: Application of the algorithm based on the PSO and improved SVDD for the personal credit rating. J. Financ. Eng. 1(04), 1450037 (2014)

    MathSciNet  Google Scholar 

  31. Kong, X.; Zhou, W.; Wang, X.: Parameter optimization for SVDD based on improved krill herd algorithm. Comput. Eng. Appl. 53(22), 142–147 (2017)

    Google Scholar 

  32. Mirjalili, S.; Mirjalili, S.M.; Yang, X.S.: Binary Bat Algorithm 10, 663–681 (2014)

    Google Scholar 

  33. Coelho, L.S.; Askarzadeh, A.: An enhanced bat algorithm approach for reducing electrical power consumption of air conditioning systems based on differential operator. Appl. Therm. Eng. 99(04), 834–840 (2016)

    Google Scholar 

  34. Bora, T.C.; Coelho, L.S.; Lebensztajn, L.: Bat-Inspired optimization approach for the brushless DC wheel motor problem. IEEE Trans. Magn. 48(02), 947–950 (2012)

    Google Scholar 

  35. Wang, J.; Hu, Y.; Shi, H.B.: Fault detection for batch processes based on Gaussian mixture model. Acta Automatica Sinica 41(05), 899–905 (2015)

    Google Scholar 

  36. Zheng, H.; Xiong, W.: Fault monitoring for batch process based on multi-stage ICA-SVDD. J. Nanjing Univ. Sci. Technol. 42(2), 195–203 (2018)

    Google Scholar 

  37. Fong Simon, D.S.; Yang, X.-S.; et al.: Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms. Sci World J 10, 1–16 (2014)

    Google Scholar 

  38. Ge, Z.; Gao, F.; Song, Z.: Batch process monitoring based on support vector data description method. J. Process Control 21(6), 949–959 (2011)

    Google Scholar 

  39. Yang, J.G.; Zhang, J.; Yang, J.X.; et al.: A principal component analysis based fault detection method in etch process of semiconductor manufacturing. Key Eng. Mater. 522, 793–798 (2012)

    Google Scholar 

  40. Jianlin, Wang; Linyu, Ma; Weihuan, Liu; et al.: Batch process monitoring based on kernel similarity support vector data description. J. Chem. Ind. Eng. 68(09), 3494–3500 (2017)

    Google Scholar 

  41. Chen-Chen, Z.; Xiao-Gang, D.; Ying, X.U.: Improved multiway kernel entropy component analysis based fault detection for batch process. Control Eng. China 25(4), 1671–7848 (2018)

    Google Scholar 

  42. Jrobert, J.; Eltoft, T.; Mark, G.; et al.: Kernel maximum entropy data transformation and an enhanced spectral clustering algorithm. Adv. Neural. Inf. Process. Syst. 753(2), 633–640 (2007)

    Google Scholar 

  43. Wang, J.: Research of fault detection method for batch processes based on clustering. East China University of Science and Technology (2014).

  44. Alexander, V.G.; Alexey, V.K.: Exploring equivalence domain in nonlinear inverse problems using covariance matrix adaption evolution strategy (CMAES) and random sampling. J. Math. Sci. 202(4), 553–559 (2016)

    Google Scholar 

  45. Vun Jack, C.; Zainal, S.: Coyote optimization algorithm for the parameter extraction of photovoltaic cells. Solar Energy 10, 194 (2019)

    Google Scholar 

Download references

Acknowledgement

This work is partially supported by China Postdoctoral Science Foundation (No. 2020M673279), National Natural Science Foundation of China (NSFC) under Grant No. 51675450, Sichuan Science and Technology Program (No. 2020JDTD0012) and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No. 18YJC630255).

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Correspondence to Min Zhang.

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Zhang, M., Yi, Y. & Cheng, W. Multistage Condition Monitoring of Batch Process Based on Multi-boundary Hypersphere SVDD with Modified Bat Algorithm. Arab J Sci Eng 46, 1647–1661 (2021). https://doi.org/10.1007/s13369-020-04848-1

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