Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Statistical approach to Control Performance Assessment (CPA) is of great practical importance. This is particularly visible in process industry, where there are many PID loops. They are often assessed with measures derived from the Gaussian probabilistic density function. Standard deviation, variance, skewness or kurtosis form the majority of applied indexes. The review of data originating from process industry shows, however, to the contrary, that these signals have rather non-Gaussian properties and are mostly characterized by fat-tailed distribution disable the ability. Investigations show that strong disturbances may significantly disable the capacity of proper assessment. Standard measures often fail in such cases. It is shown that non-Gaussian measures can help with this problem. Various disturbances are tested and compared. Results show that fat-tailed distributions are an interesting alternative. They are less sensitive to disturbance shadowing and still make possible loop dynamic assessment.
Czasopismo
Rocznik
Tom
Strony
259--290
Opis fizyczny
Bibliogr. 33 poz., rys.
Twórcy
autor
- Institute of Control and Computation Engineering Warsaw University of Technology ul. Nowowiejska 15/19, 00-665 Warszawa, Poland
Bibliografia
- [1] ASTROM, K.J. (1967) Computer control of a paper machine - an application of linear stochastic control theory. IBM Journal 11, 389–405.
- [2] AXENSTEN, P. (2006) Cauchy CDF, PDF, inverse CDF, parameter fit and random generator. http://www.mathworks.com/matlabcentral/ fileexchange/11749-cauchy/.
- [3] BAUER, M., HORCH, A., XIE, L., JELALI, M. and THORNHILL, N. (2016) The current state of control loop performance monitoring – A survey of application in industry. Journal of Process Control 38, 1–10.
- [4] CHOUDHURY, A.A.S., SHOUKAT, A., SHAH, S.L. and THORNHILL, N.F. (2008) Diagnosis of poor control-loop performance using higher-order statistics. Automatica 40(10), 1719–1728.
- [5] CHOUDHURY, A.A.S., SHAH, S.L. and THORNHILL, N.F. (2008) Diagnosis of Process Nonlinearities and Valve Stiction. Springer Berlin, Heidelberg.
- [6] DAS, L., RENGASWAMY, R. and SRINIVASAN, B. (2017) Data mining and control loop performance assessment: The multivariate case. AIChE Journal 63(8), 3311–3328.
- [7] DOMANSKI, P.D. (2015) Non-Gaussian properties of the real industrial control error in SISO loops. Proceedings of the 19th International Conference on System Theory, Control and Computing, IEEE, 877–882.
- [8] DOMANSKI, P.D. (2016) Non-Gaussian and persistence measures for control loop quality assessment. Chaos: An Interdisciplinary Journal of Nonlinear Science 26(4), 043105.
- [9] DOMANSKI, P.D. (2016a) Fractal Measures in Control Performance Assessment. Proceedings of IEEE International Conference on Methods and Models in Automation and Robotics MMAR, Miedzyzdroje, Poland, IEEE, 448–453.
- [10] DOMANSKI, P.D., GOLONKA, S., JANKOWSKI, R., KALBARCZYK, P. and MOSZOWSKI, B. (2016) Control rehabilitation impact on production efficiency of ammonia synthesis installation. Industrial & Engineering Chemistry Research 55(39), 10366–10376.
- [11] DOMANSKI, P.D. (2017) Multifractal properties of process control variables. International Journal on Bifurcation and Chaos 27(6), 1750094.
- [12] DOMANSKI, P.D., L AWRYNCZUK, M. (2017) Assessment of Predictive Control Performance using Fractal Measures. Nonlinear Dynamics 89, 773–790.
- [13] DOMANSKI, P.D., L AWRYNCZUK, M. (2017a) Assessment of the GPC Control Quality using Non-Gaussian Statistical Measures. International Journal of Applied Mathematics & Computer Science 27(2), 291–307.
- [14] GAO, X. and YANG, F. and SHANG, C. and HUANG, D. (2016) A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era. Chinese Journal of Chemical Engineering 24(8), 952–962.
- [15] HARRIS, T. (1989) Assessment of closed loop performance. Canadian Journal of Chemical Engineering 67, 856–861.
- [16] HORCH, A. and ISAKSSON, A.J. (1998) A modified index for control performance assessment. Proceedings of the 1998 American Control Conference 6, 3430–3434.
- [17] JELALI, M. (2007) Control Performance Management in Industrial Automation: Assessment, Diagnosis and Improvement of Control Loop Performance. Springer-Verlag, London.
- [18] KOUTROVELIS, I. A. (1980) Regression-Type Estimation of the Parameters of Stable Laws. Journal of the American Statistical Association 75(372), 918– 928.
- [19] LILLIEFORS, H. (1967) On the Kolmogorov–Smirnov test for normality with mean and variance unknown. Journal of the American Statistical Association 62, 399–402.
- [20] LIU, X., XIE, L., KRUGER, U., LITTLER, T. and WANG, S. (2008) Statistical-based monitoring of multivariate non-Gaussian systems. AIChE Journal 54(9), 2379–2391.
- [21] NESIC, Z., DUMONT, G. A., DAVIES, M. S. and BREWSTER, D. (1997) CD Control Diagnostics Using a Wavelet Toolbox. Proceedings of the IMEKO CD Symposium XB, SAS, IMEKO, 120–125.
- [22] PAULONIS, M.A. and COX, J.W. (2003) A practical approach for large-scale controller performance assessment, diagnosis, and improvement. Journal of Process Control 13(2), 155–168.
- [23] PETERS, E.E. (1996) Chaos and Order in the Capital Markets: A New View of Cycles, Prices, and Market Volatility, 2nd edition. John Wiley & Sons, Inc.
- [24] PILLAY, N. and GOVENDER, P. (2014) A Data Driven Approach to Performance Assessment of PID Controllers for Setpoint Tracking. Procedia Engineering 69, 1130–1137.
- [25] SPINNER, T., SRINIVASAN, B. and RENGASWAMY, R. (2014) Data-based automated diagnosis and iterative retuning of proportional-integral (PI) controllers. Control Engineering Practice 29, 23–41.
- [26] SCHAFER, J. and CINAR, A. (2004) Multivariable MPC system performance assessment, monitoring, and diagnosis. Journal of Process Control 14(2), 113– 129.
- [27] SCHELGEL, M., SKARDA, R. and CECH, M. (2013) Running discrete Fourier transform and its applications in control loop performance assessment. 2013 IEEE International Conference on Process Control, 113–118.
- [28] SEBORG, D.E., MELLICHAMP, D.A., EDGAR, T.F. and DOYLE, F.J. (2010) Process Dynamics and Control. Wiley.
- [29] SHINSKEY, F. G. (2002) Process control: As taught vs as practiced. Industrial and Engineering Chemistry Research 41, 3745–3750.
- [30] SRINIVASAN, B. and RENGASWAMY, R. (2012) Automatic oscillation detection and characterization in closed-loop systems. Control Engineering Practice 20(8), 733–746.
- [31] WEI, W. and ZHUO, H. (2009) Research of performance assessment and monitoring for multivariate model predictive control system. 2009 4th International Conference on Computer Science & Education R. IEEE, 509–514.
- [32] ZHANG, J., JIANG, M. and CHEN, J. (2015) Minimum entropy-based performance assessment of feedback control loops subjected to non-Gaussian disturbances. Journal of Process Control 24(11), 1660–1670.
- [33] ZHOU, Y. F. and WAN, F. (2008) A neural network approach to control performance assessment. International Journal of Intelligent Computing and Cybernetics 1, 617–633.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4a5788fe-9def-4c36-8ff6-2013674ef7be