Skip to main content
SpringerLink
Log in

Valve Stiction Quantification Based on Riemannian Manifold

  • Regular Papers
  • Control Theory and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

With the modern industrial processes getting advanced and complicated, accurate evaluation for the status of valves in control loops has become increasing significant. Nevertheless, control valves often suffer from stiction nonlinearity, which may bring about the malfunction of control loops, eventually resulting in an unanticipated breakdown or unacceptable performance deterioration. Hence, the recent past two decades have also witnessed a huge growth on valve stiction detection strategies for industry process. However, to the best of author’s knowledge, the question of how to quantify further aspects of valve stiction remains unanswered. In particular, most of the existing stiction quantification methods share the same limitation that they may occasionally fail to realize the estimation for the slipjump characteristic of valve stiction, and may be ineffective for the cases in which sticky valves are applied in the cascade control loops. Considering these drawbacks, this paper puts forward a novel quantification method for valve stiction based on the exponential and logarithmic maps attached onto the Riemannian manifold. The key idea is to exploit the relationship between process variables and controller output which constitutes a specific shape (e.g., ellipsoid) on a Riemannian manifold. The method can be capable to deal with three typical stiction features, including deadband, stickband, and slipjump for single control loops and simple cascade loops. The case studies of simulated examples validate that the proposed method is a promising method for quantifying the valve stiction’s deadband plus stickband, and slipjump accurately.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. K. X. Ba, B. Yu, X. D. Kong, H. Zhao, J. S. Zhao, Q. X. Zhu, and C. H. Li, “The dynamic compliance and its compensation control research of the highly integrated valve-controlled cylinder position control system,” International Journal of Control, Automation, and Systems, vol. 15, pp. 1814–1825, 2017.

    Article  Google Scholar 

  2. Y. Wu and L. Yao, “Fault diagnosis and fault tolerant control for manipulator with actuator multiplicative fault,” International Journal of Control, Automation, and Systems, vol. 19, no. 5, pp. 980–987, 2021.

    Article  Google Scholar 

  3. Q. Su, Z. Fan, D. Zhang, and J. Li, “Finite-time fault detection filtering for switched singular systems with all modes unstable: An ADT approach,” International Journal of Control, Automation, and Systems, vol. 17, no. 8, pp. 2026–2036, 2019.

    Article  Google Scholar 

  4. E. P. Management, Control Valve Handbook, Fisher Controls International LLC, 2005.

  5. M. A. A. S. Choudhury, N. F. Thornhill, and S. L. Shah, “Modelling valve stiction,” Control Engineering Practice, vol. 13, no. 5, pp. 641–658, 2005.

    Article  Google Scholar 

  6. M. A. A. S. Choudhury, S. L. Shah, and N. F. Thornhill, “Diagnosis of poor control-loop performance using higher-order statistics,” Automatica, vol. 40, no. 10, pp. 1719–1728, 2004.

    Article  MathSciNet  MATH  Google Scholar 

  7. A. Horch, “Stiction detection based on cross-correlation and signal shape,” Detection and Diagnosis of Stiction in Control Loops: State of the Art and Advanced Methods, 1st ed., Ch. 6, pp. 113–143, Springer-Verlag, London, UK, 2010.

    Google Scholar 

  8. M. Kano, H. Maruta, H. Kugemoto, and K. Shimizu, “Practical model and detection algorithm for valve stiction,” IFAC Proceedings Volumes, vol. 37, no. 9, pp. 859–864, 2004.

    Article  Google Scholar 

  9. H. Zabiri and M. Ramasamy, “NLPCA as a diagnostic tool for control valve stiction,” Journal of Process Control, vol. 19, no. 8, pp. 1368–1376, 2009.

    Article  Google Scholar 

  10. M. Rossi and C. Scali, “A comparison of techniques for automatic detection of stiction: Simulation and application to industrial data,” Journal of Process Control, vol. 15, no. 5, pp. 505–514, 2005.

    Article  Google Scholar 

  11. A. Amiruddin, H. Zabiri, S. S. Jeremiah, W. K. Teh, and B. Kamaruddin, “Valve stiction detection through improved pattern recognition using neural networks,” Control Engineering Practice, vol. 90, pp. 63–84, 2019.

    Article  Google Scholar 

  12. W. K. Teh, H. Zabiri, Y. Sarnyudia, S. S. Jeremiah, B. Kamaruddin, A. A. A. M. Amiruddin, and N. M. Ramli, “An improved diagnostic tool for control valve stiction based on nonlinear principle component analysis,” Industrial and Engineering Chemistry Research, vol. 57, pp. 11350–11265, 2018.

    Article  Google Scholar 

  13. M. A. A. S. Choudhury, S. L. Shah, and N. F. Thornhill, “Automatic detection and quantification of stiction in control valves,” Control Engineering Practice, vol. 14, no. 12, pp. 1395–1412, 2006.

    Article  Google Scholar 

  14. M. Choudhury, M. Jain, and S. Shah, “Stiction -definition, modelling, detection and quantification,” Journal of Process Control, vol. 18, no. 3–4, pp. 232–243, 2008.

    Article  Google Scholar 

  15. Y. J. Yoo, “Fault detection method using multi-mode principal component analysis based on Gaussian mixture model for sewage source heat pump system,” International Journal of Control, Automation, and Systems, vol. 17, no. 8, pp. 2125–2134, 2019.

    Article  Google Scholar 

  16. Z. K. Hu, W. H. Gui, C. H. Yang, P. C. Deng, and S. X. Ding, “Fault classification method for inverter based on hybrid support vector machines and wavelet analysis,” International Journal of Control, Automation, and Systems, vol. 9, no. 4, pp. 797–804, 2011.

    Article  Google Scholar 

  17. R. B. di Capaci, M. Vaccari, and C. Vaccari, “Enhancing MPC formulations by identification and estimation of valve stiction,” Journal of Process Control, vol. 81, pp. 31–39, 2019.

    Article  Google Scholar 

  18. R. B. di Capaci, C. Vaccari, and G. Pannocchia, “Model predictive control design for multivariable processes in the presence of valve stiction,” Journal of Process Control, vol. 71, pp. 25–34, 2018.

    Article  Google Scholar 

  19. D. Zheng, X. Sun, S. K. Damarla, A. Shah, J. Amalraj, and B. Huang, “Valve stiction detection and quantification using a K-means clustering based moving window approach,” Industrial and Engineering Chemistry Research, vol. 60, no. 6, pp. 2563–2577, 2021.

    Article  Google Scholar 

  20. M. Jelali and B. Huang, Detection and Diagnosis of Stiction in Control Loops, Springer London, 2010.

    Book  Google Scholar 

  21. S. Amari and H. Nagaoka, Methods of Information Geometry, American Mathematical Society, 2007.

  22. B. Huang and L. S. Hu, “A geometrically inspired quantification approach for valve stiction using Riemannian logarithmic map,” Measurement, vol. 199, 111562, 2022.

    Article  Google Scholar 

  23. N. F. Thornhill, B. Huang, and H. Zhang, “Detection of multiple oscillations in control loops,” Journal of Process Control, no. 13, pp. 91–100, 2003.

  24. Q. Ye and W. Zhi, “Discrete Hessian Eigenmaps method for dimensionality reduction,” Journal of Computational and Applied Mathematics, vol. 278, pp. 197–212, 2015.

    Article  MathSciNet  MATH  Google Scholar 

  25. T. Hastie and W. Stuetzle, “Principal curves,” Journal of the American Statistical Association, vol. 84, no. 406, pp. 502–516, 1989.

    Article  MathSciNet  MATH  Google Scholar 

  26. J. Richard and W. Dean, Applied Multivariate Statistical Analysis, 6th ed., Pearson Education, 2014.

  27. P. Fletcher, C. Lu, and S. Joshi, “Statistics of shape via principal geodesic analysis on Lie groups,” Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 95–101, 2003.

  28. R. Barnard, K. Pearce, and K. Richards, “A monotonicity property involving 3F2 and comparisons of the classical approximations of elliptical arc length,” SIAM Journal on Mathematical Analysis, vol. 32, no. 2, pp. 403–419, 2000.

    Article  MathSciNet  MATH  Google Scholar 

  29. M. L. Daniels and E. P. Armendáriz, “Tangents to conic sections,” Tangent, 2011.

  30. L. H. C. E. Russel and R. D. Braatz, Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes, Springer, 2000.

  31. A. Singhal and T. Salsbury, “A simple method for detecting valve stiction in oscillating control loops,” Journal of Process Control, vol. 15, no. 4, pp. 371–382, 2005.

    Article  Google Scholar 

  32. S. Elferik, M. Hassan, and M. Alnaser, “Adaptive valve stiction compensation using differential evolution,” Journal of Chemical Engineering of Japan, vol. 51, no. 5, pp. 407–417, 2018.

    Article  Google Scholar 

  33. V. Akavalappil and T. K. Radhakrishnan, “Comparison of current state of control valve stiction detection and quantification techniques,” Transactions of the Institute of Measurement and Control, vol. 44, no. 3, pp. 562–579, 2021.

    Article  Google Scholar 

  34. V. M. Panaretos, T. Pham, and Z. Yao, “Principal flows,” Journal of the American Statistical Association, vol. 109, pp. 424–436, March 2014.

    Article  MathSciNet  MATH  Google Scholar 

  35. S. Buss and J. Fillmore, “Spherical averages and applications to spherical splines and interpolation,” ACM Transactions on Graphics, vol. 20, no. 2, pp. 95–126, 2001.

    Article  Google Scholar 

  36. N. Courty, T. Burger, and P. Marteau, “Geodesic analysis on the Gaussian RKHS hypersphere,” Proc. of the European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 299–313, 2012.

  37. X. Pennec, “Intrinsic statistics on Riemannian manifolds: Basic tools for geometric measurements,” Journal of Mathematical Imaging and Vision, vol. 25, pp. 127–154, July 2006.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, Room 2-535, 800 Dongchuan Road, Shanghai, 200240, China

    Bo Huang & Li-Sheng Hu

  2. Turbine Plant, Shanghai Electric Power Generation Equipment Co.,Ltd., Shanghai, 200240, China

    Yunhong Peng & Zhiwei You

Authors
  1. Bo Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li-Sheng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yunhong Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiwei You
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li-Sheng Hu.

Additional information

Bo Huang received his B.S. degree in automation from the Huazhong University of Science and Technology, Hubei, China, in 2018. He is currently working toward a Ph.D. degree from the Department of Automation, Shanghai Jiao Tong University, Shanghai, China. His current research interests are in the fields of industrial process monitoring, fault detection, and Riemannian manifold theory.

Li-Sheng Hu received his B.S. degree in engineering science, and a Ph.D. degree in industrial automation, both from Zhejiang University, China, in 1986 and 1998, respectively. He was a postdoctoral fellow in Automation Engineering, Shanghai Jiao Tong University, Shanghai, China, from 1998 to 2000, and in Chemical Engineering, University of Alberta, Canada, from 2002 to 2003. He is now a Professor in the Department of Automation, Shanghai Jiao Tong University. His current research interests are in the areas of robust model prediction control, process monitoring, control performance limitation, performance assessment, and industrial fault detection.

Yunhong Peng received his B.S. degree in mechatronics from Yanshan University, Hebei, China, in 2001, and an M.S. degree in automation engineering, Shanghai Jiao Tong University, Shanghai, China, in 2009. He is now working in Shanghai Electric Power Generation Equipment Co., Ltd. Turbine Plant, Shanghai, China. His current research interests are in the areas of automation and maintenance of steam turbine platform, and industrial process monitoring.

Zhiwei You received his B.S. degree in thermal energy and power engineering from Dalian University of Technology, Liaoning, China, in 2007, and an M.S. degree in automation engineering, Shanghai Jiao Tong University, Shanghai, China, in 2020. He is now working in Shanghai Electric Power Generation Equipment Co., Ltd. Turbine Plant, Shanghai, China. His current research interests are in the areas of automation and maintenance of steam turbine platform, and industrial process monitoring.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, B., Hu, LS., Peng, Y. et al. Valve Stiction Quantification Based on Riemannian Manifold. Int. J. Control Autom. Syst. 21, 171–187 (2023). https://doi.org/10.1007/s12555-021-1100-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-021-1100-2

Keywords

Search

Navigation