THE DEVELOPMENT AND INVESTIGATION ANALYSIS OF AN ARX-BASED GENERALIZED LIKELIHOOD RATIO (GLR) STICTION DETECTION METHOD

Authors

  • Nur Amalina Shairah Abdul Samat Faculty of Engineering, Universiti Malaysia Sarawak, Jalan Meranti, 94300 Kota Samarahan, Sarawak, Malaysia
  • Haslinda Zabiri Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia
  • Bashariah Kamaruddin Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia

DOI:

https://doi.org/10.11113/jt.v80.11228

Keywords:

Control valve, stiction detection, ARX, GLR test, statistical hypothesis testing

Abstract

Control valve stiction is one of the main sources of nonlinearity which can result in many deleterious effects on the control loop performance of a process. The study of stiction detection methods has now becoming one of the essential research areas in process control. In this present work, an ARX-based Generalized Likelihood Ratio (GLR) stiction detection method is proposed and its effectiveness is analyzed. The implementation of the proposed method involves three main stages; 1) ARX model identification, 2) GLR test, and 3) statistical hypothesis testing. The proposed detection method was applied to two benchmark simulated case studies. Results showed that the method effectively detect stiction. The presence of stiction is declared if the GLR test statistics,  exceeds the decision threshold limit, , and the null hypothesis is rejected at 5% significance level. On the other hand, if  value lies below , the null hypothesis is accepted and the absence of stiction is confirmed. In addition, it is also observed that the proposed method is reasonably insensitive and robust to the changes in the process gain,  and time constant,  as it generally allows up to ±10% changes in the two parameters for both case studies.

References

Brasio, A. S. R., Romanenko. A., & Fernandes, N. C. P. 2014. Modeling, Detection and Quantification, and Compensation of Stiction in Control Loops: The State of the Art. Industrial & Engineering Chemistry Research (I&EC research). 53: 15020-15040.

Desborough, L. D., & Miller, R. 2002. Increasing Customer Value of Industrial Control Performance Monitoring- Honeywell's Experience. Paper presented at the International Conference on Chemical Process Control.

Yang, J. C., & Clarke, D. W. 1999. The Self-validating Actuator. Control Engineering Practice. 7: 249-260.

Jamsa-Jounela, S. L., Tikkala, V., Zakharov, A., Pozo Garcia, O., Laavi, H., Myller, T., et al. 2012. Outline of a Fault Diagnosis System for a Large-scale Board Machine. International Journal of Advanced Manufacturing Technology. 65: 1741-1755.

Ender, D. B. 1993. Process Control Performances. Not as Good as You Think. Control Engineering. 40: 180-190.

Horch, A. 1999. A Simple Methodfor Detection of Stiction in Control Valves. Control Engineering Practice. 7: 1221-1231.

Horch, A. 2006. United States Patent No.: U. S. Patent.

Daneshwar, M. A., & Noh, N. M. 2015. Detection of Stiction in Flow Control Loops Based on Fuzzy Clustering. Control Engineering Practice. 39: 23-34.

Singhal, A., & Salsbury, T. I. 2005. A Simple Method for Detecting Valve Stiction in Oscillating Control Loops. Journal of Process Control. 15: 371-382.

Zabiri, H., & Ramasamy, M. 2009. NLPCA as a Diagnostic Tool for Control Valve Stiction. Journal of Process Control. 19: 1368-1376.

Choudhury, M. A. A. S., Thornhill, N. F., Shah, S. L., & Shook, D. S. 2006. Automatic Detection and Quantification of Stiction in Control Valves. Control Engineering Practice. 14: 1395-1412.

Stenman, A., Forsman, K., & Gustafsson, F. 2002. A Segmentation-based Approach for Detection of Stiction in Control Valves. Int. J. Adapt. Control Signal Process. 17: 625-634.

Srinivasan, R., & Rengaswamy, R. 2005. Control Loop Performance Assessment 2 - Hammerstein Model Approach for Stiction Diagnosis. Industrial & Engineering Chemistry Research (I&EC research). 44: 6719-6728.

Choudhury, M. A. A. S., Jain, M., & Shah, S. L. 2008. Stiction - Definition, Modelling, Detection and Quantification. Journal of Process Control. 18: 232-243.

Zabiri, H., Maulud, A., Omar, N., & Ramasamy, M. 2009. An N-based Algorithm for Control Valve Stiction Quantification. WSEAS Transactions on Systems and Control. 4(2).

Choudhury, M. A. A. S., Thornhill, N. F., & Shah, S. L. 2005. Modelling Valve Stiction. Control Engineering Practice. 13: 641-658.

Kano, M., Maruta, H., Kugemoto, H., & Shimizu, K. 2004. Practical Model and Detection Algorithm for Valve Stiction. Proceedings of the Seventh IFAC-DYCOPS Symposium, Boston, USA,.

Harrou, F., Nounou, M. N., Nounou, H. N., & Madakyaru, M. 2013. Statistical Fault Detection Using PCA-based GLR Hypothesis Testing. Journal of Loss Prevention in the Process Industries. 26: 129-139.

Svärd, C., Nyberg, M., Frisk, E., & Krysander, M. 2014. Data-Driven and Adaptive Statistical Residual Evaluation for Fault Detection with an Automotive Application. Mechanical Systems and Signal Processing. 45(1): 170-192.

Galicia, H. J., He, Q. P., & Wang, J. 2012. A Comprehensive Evaluation of Statistics Pattern Analysis based Process Monitoring. IFAC Proceedings Volumes. 45(15): 39-44.

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Published

2018-04-29

Issue

Section

Science and Engineering

How to Cite

THE DEVELOPMENT AND INVESTIGATION ANALYSIS OF AN ARX-BASED GENERALIZED LIKELIHOOD RATIO (GLR) STICTION DETECTION METHOD. (2018). Jurnal Teknologi, 80(4). https://doi.org/10.11113/jt.v80.11228