DOI QR코드

DOI QR Code

Innovative Solutions for Design and Fabrication of Deep Learning Based Soft Sensor

  • Khdhir, Radhia (Department of Computer Science College of Science and Arts in Qurayyat, Jouf University) ;
  • Belghith, Aymen (Computer Science Department College of Informatics and Computing Saudi Electronic University)
  • Received : 2022.02.05
  • Published : 2022.02.28

Abstract

Soft sensors are used to anticipate complicated model parameters using data from classifiers that are comparatively easy to gather. The goal of this study is to use artificial intelligence techniques to design and build soft sensors. The combination of a Long Short-Term Memory (LSTM) network and Grey Wolf Optimization (GWO) is used to create a unique soft sensor. LSTM is developed to tackle linear model with strong nonlinearity and unpredictability of manufacturing applications in the learning approach. GWO is used to accomplish input optimization technique for LSTM in order to reduce the model's inappropriate complication. The newly designed soft sensor originally brought LSTM's superior dynamic modeling with GWO's exact variable selection. The performance of our proposal is demonstrated using simulations on real-world datasets.

Keywords

References

  1. W. Yan, D. Tang, and Y. Lin, "A data-driven soft sensor modeling method based on deep learning and its application," IEEE Transactions on Industrial Electronics, vol. 64, no. 5, pp. 4237-4245, 2017. https://doi.org/10.1109/TIE.2016.2622668
  2. Z. Ujevic, I. Mohler, and N. Bolf, "Soft sensors for splitter product property estimation in CDU," Chemical Engineering Communications, vol. 198, pp. 1566-1578, 2011. https://doi.org/10.1080/00986445.2011.556692
  3. K. Sun, J. Liu, J.-L. Kang, S.-S. Jang, D. S.-H. Wong, and D.-S. Chen, "Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote," Journal of Process Control, vol. 24, pp. 1068-1075, 2014. https://doi.org/10.1016/j.jprocont.2014.05.010
  4. S. Gao, J. Yang, and J. Wang, "D-FNN based modeling and BP neural network decoupling control of PVC stripping process," Mathematical Problems in Engineering, vol. 2014, Article ID 681259, 13 pages, 2014.
  5. A. Nawaz, A. S. Arora, C. M. Yun, H. Cho, S. You, and M. Lee, "Data authorization and forecasting by a proactive soft sensing tool-anammox based process," Industrial & Engineering Chemistry Research, vol. 58, no. 22, pp. 9552-9563, 2019. https://doi.org/10.1021/acs.iecr.9b00722
  6. S. Zhang, X. Chen, and Y. Yin, "An ELM based online soft sensing approach for alumina concentration detection," Mathematical Problems in Engineering, vol. 2015, Article ID 268132, 8 pages, 2015.
  7. K. Sun, X. Wu, J. Xue, and F. Ma, "Development of a new multi-layer perceptron based soft sensor for SO2 emissions in power plant," Journal of Process Control, vol. 84, pp. 182-191, 2019. https://doi.org/10.1016/j.jprocont.2019.10.007
  8. E. Heidari, M. A. Sobati, and S. Movahedirad, "Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN)," Chemometrics and Intelligent Laboratory Systems, vol. 155, pp. 73-85, 2016. https://doi.org/10.1016/j.chemolab.2016.03.031
  9. K. G. Sheela and S. N. Deepa, "Neural network based hybrid computing model for wind speed prediction," Neurocomputing, vol. 122, pp. 425-429, 2013. https://doi.org/10.1016/j.neucom.2013.06.008
  10. Y. He, Y. Xu, Z. Geng, and Q. Zhu, "Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network," Chinese Journal of Chemical Engineering, vol. 23, no. 1, pp. 138-145, 2015. https://doi.org/10.1016/j.cjche.2014.10.004
  11. M. Zabadaj, K. Chreptowicz, J. Mierzejewska, and P. Ciosek, "Two-dimensional fluorescence as soft sensor in the monitoring of biotransformation performed by yeast," Biotechnology Progress, vol. 33, no. 2, pp. 299-307, 2017. https://doi.org/10.1002/btpr.2381
  12. J. Rehrla, A. P. Karttunenb, N. Nicolaic, T. Hormanne, and M. Hornd, "Control of three different continuous pharmaceutical manufacturing processes: use of soft sensors," International Journal of Pharmaceutics, vol. 543, no. 1-2, pp. 60-72, 2018. https://doi.org/10.1016/j.ijpharm.2018.03.027
  13. Z. Ge, "Supervised latent factor analysis for process data regression modeling and soft sensor application," IEEE Transactions on Control Systems Technology, vol. 24, no. 3, pp. 1004-1011, 2015. https://doi.org/10.1109/TCST.2015.2473817
  14. B. Maschler, D. White, and M. Weyrich, "Anwendungsfalle und Methoden der kunstlichen Intelligenz in der anwendungsorientierten Forschung im Kontext von Industrie 4.0," University of Stuttgart, 2020, DOI: 10.18419/opus-10740.
  15. S. Yin, W. Ji, and L. Wang, "A machine learning based energy efficient trajectory planning approach for industrial robots," Procedia CIRP, vol. 81, pp. 429-434, 2019, DOI: 10.1016/j.procir.2019.03.074.
  16. T. Muller, N. Jazdi, J.-P. Schmidt, and M. Weyrich, "Cyber-Physical Production Systems: enhancement with a self-organized reconfiguration management," Procedia CIRP, 2020 (accepted).
  17. A. Mayr et al., "Machine Learning in Production - Potentials, Challenges and Exemplary Applications," Procedia CIRP, vol. 86, pp. 49-54, 2019, DOI: 10.1016/j.procir.2020.01.035.
  18. G. Hussain, M. Jabbar, J.-D. Cho, and S. Bae, "Indoor positioning system: a new approach based on LSTM and two stage activity classification," Electronics, vol. 8, no. 4, p. 375, 2019. https://doi.org/10.3390/electronics8040375
  19. L. Yang, Y. Li, J. Wang, and Z. Tang, "Post text processing of Chinese speech recognition based on bidirectional LSTM networks and CRF," Electronics, vol. 8, no. 11, p. 1248, 2019. https://doi.org/10.3390/electronics8111248
  20. J. Chen, Q. Jin, and J. Chao, "Design of deep belief networks for short-term prediction of drought index using data in the Huaihe river basin," Mathematical Problems in Engineering, vol. 2012, Article ID 235929, 16 pages, 2012.
  21. S. Xingjian, Z. Chen, H. Wang et al., "Convolutional LSTM network: a machine learning approach for precipitation nowcasting," in Proceedings of Advances in Neural Information Processing Systems, pp. 802-810, Montreal, Canada.
  22. Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, "LSTM network: a deep learning approach for short-term traffic forecast," IET Intelligent Transport Systems, vol. 11, no. 2, pp. 68-75, 2017. https://doi.org/10.1049/iet-its.2016.0208
  23. J. Liu, A. Shahroudy, D. Xu et al., "Spatio-temporal lstm with trust gates for 3d human action recognition," in Proceedings of European Conference on Computer Vision, pp. 816-833, Amsterdam, Netherlands.
  24. X. Yuan, L. Li, and Y. Wang, "Nonlinear dynamic soft sensor modeling with supervised long short-term memory network," IEEE Transactions on Industrial Informatics, vol. 16, no. 5, pp. 3168-3176, 2019. https://doi.org/10.1109/tii.2019.2902129
  25. Q. Sun and Z. Ge, "Probabilistic sequential network for deep learning of complex process data and soft sensor application," IEEE Transactions on Industrial Informatics, vol. 15, pp. 2700-2709, 2018. https://doi.org/10.1109/tii.2018.2869899
  26. C. Bennett, J. F. Dunne, S. Trimby, and D. Richardson, "Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks," Mechanical Systems and Signal Processing, vol. 85, pp. 126-145, 2017, DOI: 10.1016/j.ymssp.2016.07.015.
  27. S. Trimby, J. F. Dunne, C. Bennett, and D. Richardson, "Unified approach to engine cylinder pressure reconstruction using time-delay neural networks with crank kinematics or block vibration measurements," International Journal of Engine Research, vol. 18, no. 3, pp. 256-272, 2017, DOI: 10.1177/1468087416655013.
  28. R. Johnsson, "Cylinder pressure reconstruction based on complex radial basis function networks from vibration and speed signals," Mechanical Systems and Signal Processing, vol. 20, no. 8, pp. 1923-1940, 2006, DOI: 10.1016/j.ymssp.2005.09.003.
  29. H. Mrabet, E. Giacoumidis, I. Dayoub, and Aymen Belghith, "A Survey of Applied Machine Learning Techniques for Optical Orthogonal Frequency Division Multiplexing Based Networks", Emerging Telecommunications Technologies, ETT 2022, ISSN: 2161-3915, Wiley, November 2021, DOI: https://doi.org/10.1002/ett.4400.