Abstract
Stiction is a major problematic phenomenon affecting industrial control valves. An approach for detection and quantification of valve stiction using an one-stage optimization technique is proposed. A Hammerstein Model that comprises a complete stiction model and a process model is identified from industrial process data. Some difficulties in the identification approach are pointed out and strategies to overcome them are suggested, namely the smoothing of discontinuity points. A simulation study demonstrates the application of the proposed technique.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Stenman, A., Gustafsson, F., Forsman, K.: A segmentation-based method for detection of stiction in control valves. Int. J. Adapt. Control 17(7-9), 625–634 (2003)
Srinivasan, R., Rengaswamy, R., Narasimhan, S., Miller, R.: Control loop performance assessment. 2. Hammerstein model approach for stiction diagnosis. Ind. Eng. Chem. Res. 44(17), 6719–6728 (2005)
Lee, K., Ren, Z., Huang, B.: Novel closed-loop stiction detection and quantification method via system identification. In: International Symposium on Advanced Control of Industrial Processes (2008)
Choudhury, S., Jain, M., Shah, S.L.: Stiction – Definition, modelling, detection and quantification. J. Process Contr. 18, 232–243 (2008)
Jelali, M.: Estimation of valve stiction in control loops using separable least-squares and global search algorithms. J. Process Contr. 18(7-8), 632–642 (2008)
Ivan, L., Lakshminarayanan, S.: A new unified approach to valve stiction quantification and compensation. Ind. Eng. Chem. Res. 48(7), 3474–3483 (2009)
Karra, S., Karim, M.: Comprehensive methodology for detection and diagnosis of oscillatory control loops. Control Eng. Pract. 17(8), 939–956 (2009)
Lee, K., Tamayo, E., Huang, B.: Industrial implementation of controller performance analysis technology. Control Eng. Pract. 18(2), 147–158 (2010)
Qi, F., Huang, B.: Estimation of distribution function for control valve stiction estimation. J. Process Contr. 21(8), 1208–1216 (2011)
Srinivasan, B., Spinner, T., Rengaswamy, R.: A reliability measure for model based stiction detection approaches. In: Symposium on Advanced Control of Chemical Processes, pp. 750–755 (2012)
Babji, S., Nallasivam, U., Rengaswamy, R.: Root cause analysis of linear closed-loop oscillatory chemical process systems. Ind. Eng. Chem. Res. 51(42), 13712–13731 (2012)
Ljung, L.: System identification: theory for the user. Prentice-Hall, New Jersey (1999)
Vandenberghe, L.: Convex optimization techniques in system identification. Technical report, University of California (2014)
Eskinat, E., Johnson, S., Luyben, W.: Use of Hammerstein models in identification of nonlinear systems. AIChE J. 37(2), 255–268 (1991)
Choudhury, A., Thornhill, N., Shah, S.: Modelling valve stiction. Control Eng. Pract. 13(5), 641–658 (2005)
Kano, M., Maruta, H., Kugemoto, H., Shimizu, K.: Practical model and detection algorithm for valve stiction. In: IFAC Symposium on Dynamics and Control of Process Systems, pp. 859–864. Elsevier, United Kingdom (2004)
He, Q., Wang, J., Pottmann, M., Qin, J.: A curve fitting method for detecting valve stiction in oscillating control loops. Ind. Eng. Chem. Res. 46(13), 4549–4560 (2007)
Chen, S., Tan, K., Huang, S.: Two-layer binary tree data-driven model for valve stiction. Ind. Eng. Chem. Res. 47(8), 2842–2848 (2008)
Zabiri, H., Mazuki, N.: A black-box approach in modeling valve stiction. J. Eng. Appl. Sci. 6(5), 277–284 (2010)
Wang, J., Sano, A., Chen, T., Huang, B.: A blind approach to identification of Hammerstein systems. In: IFAC World Congress on Block-oriented Nonlinear System Identification, pp. 293–312. Springer, London (2010)
Karthiga, D., Kalaivani, S.: A new stiction compensation method in pneumatic control valves. Int. J. Electron. Comput. Sci. Eng., 2604–2612 (2012)
Wu, Z., Bai, F., Yang, X., Zhang, L.: An exact lower order penalty function and its smoothing in nonlinear programming. Optimization 53(1), 51–68 (2004)
Wu, Z., Lee, H., Bai, F., Zhang, L.: Quadratic smoothing approximation to l 1 exact penalty function in global optimization. J. Ind. Manag. Optim. 1(4), 533–547 (2005)
Meng, K., Li, S., Yang, X.: A robust SQP method based on a smoothing lower order penalty function. Optimization 58(1), 23–38 (2009)
Goldfeld, S., Quandt, R.: Nonlinear methods in econometrics. North-Holland Publishing Company (1972)
Tishler, A., Zang, I.: A switching regression method using inequality conditions. J. of Econometrics 11(2-3), 259–274 (1979)
Zang, I.: Discontinuous optimization by smoothing. Math. Oper. Res. 6(1), 140–152 (1981)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Brásio, A.S.R., Romanenko, A., Fernandes, N.C.P. (2014). Stiction Detection and Quantification as an Application of Optimization. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8580. Springer, Cham. https://doi.org/10.1007/978-3-319-09129-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-09129-7_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09128-0
Online ISBN: 978-3-319-09129-7
eBook Packages: Computer ScienceComputer Science (R0)