Estimation of distribution function for control valve stiction estimation

2011 ◽  
Vol 21 (8) ◽  
pp. 1208-1216 ◽  
Author(s):  
Fei Qi ◽  
Biao Huang
Keyword(s):  
Distribution Function ◽  
Control Valve ◽  
Estimation Of Distribution ◽  
Valve Stiction

Estimation of Distribution Function in Bivariate Competing Risk Models

2006 ◽  
Vol 48 (3) ◽  
pp. 399-410 ◽  
Author(s):  
P. G. Sankaran ◽  
J. F. Lawless ◽  
B. Abraham ◽  
Ansa Alphonsa Antony
Keyword(s):  
Distribution Function ◽  
Competing Risk ◽  
Risk Models ◽  
Estimation Of Distribution ◽  
Competing Risk Models

Confirmation of control valve stiction in interacting systems

2009 ◽  
Vol 87 (4) ◽  
pp. 632-636 ◽  
Author(s):  
Yu Haoli ◽  
S. Lakshminarayanan ◽  
Vinay Kariwala
Keyword(s):  
Control Valve ◽  
Valve Stiction ◽  
Interacting Systems

scholarly journals Estimating a Distribution Function at the Boundary

2020 ◽  
Vol 49 (1) ◽  
pp. 1-23
Author(s):  
Shunpu Zhang ◽  
Zhong Li ◽  
Zhiying Zhang
Keyword(s):  
Distribution Function ◽  
Kernel Estimation ◽  
Kernel Estimator ◽  
Distribution Functions ◽  
Practical Application ◽  
Real World Applications ◽  
Distribution Kernel ◽  
Estimation Of Distribution ◽  
Simulation Results ◽  
Boundary Correction

Estimation of distribution functions has many real-world applications. We study kernel estimation of a distribution function when the density function has compact support. We show that, for densities taking value zero at the endpoints of the support, the kernel distribution estimator does not need boundary correction. Otherwise, boundary correction is necessary. In this paper, we propose a boundary distribution kernel estimator which is free of boundary problem and provides non-negative and non-decreasing distribution estimates between zero and one. Extensive simulation results show that boundary distribution kernel estimator provides better distribution estimates than the existing boundary correction methods. For practical application of the proposed methods, a data-dependent method for choosing the bandwidth is also proposed.

Control valve stiction compensation by dynamic inversion: a comparative study

2018 ◽  
Vol 24 (3) ◽  
pp. 400-419 ◽  
Author(s):  
Sami Elferik ◽  
Mohammed Hassan ◽  
Mustafa AL-Naser
Keyword(s):  
Finite Impulse Response ◽  
Residual Life ◽  
Control Valve ◽  
Inverse Model ◽  
Optimization Techniques ◽  
Dynamic Inversion ◽  
Control Effort ◽  
Content Type ◽  
Valve Stiction ◽  
Fir Model

Purpose The purpose of this paper is to improve the performance of control loop suffering from control valve stiction. Control valve stiction is considered as of one of the main causes of oscillation in process variables, which require performing costly unplanned maintenance and process shutdown. An adaptive solution to handle valve stiction while maintaining safety and quality until next planned maintenance is highly desirable to save considerable cost and effort. Design/methodology/approach This paper implements a new stiction compensation method built using adaptive inverse model techniques and intelligent control theories. Finite impulse response (FIR) model, which is known to be robust, as a compensator for stiction. The parameters of FIR model are tuned in an adaptive way using differential evolution (DE) technique. The performance of proposed method is compared with other two compensation techniques. Findings The new method showed excellent performance of the DE–FIR compensator compared to other dynamic inversion methods in terms of minimizing process variability, energy saving and valve stem aggressiveness. Research limitations/implications The compensation ability for all compensators reduces with the increase of stiction severity, thus the over shoot case always shows the worst result. In future works, other optimization techniques will be explored to find the appropriate technique that can extend the FIR model size with smallest computation time that can improve the performance of the compensator in over shoot case. In addition, the estimation of the valve residual life based on the level of stiction and effort required by the controller should be considered. Originality/value The presented approach represents an original contribution to the literature. It performs stiction compensation without a need for a prior knowledge on the process or the valve models and guarantees a smooth control of the stem movement with a low control effort. The proposed approach differs from previous adaptive methods as it uses stable FIR models and DE to find the appropriate parameters of the inverse model and handle nonlinear behavior of stiction.

Physics-Based Model of Control Valve Stiction

2008 ◽  
pp. 153-160
Author(s):  
Shoukat M. A. A. Choudhury ◽  
Sirish L. Shah ◽  
Nina F. Thornhill
Keyword(s):  
Control Valve ◽  
Valve Stiction

DETECTION AND QUANTIFICATION OF CONTROL VALVE NONLINEARITIES USING HILBERT–HUANG TRANSFORM

2009 ◽  
Vol 01 (03) ◽  
pp. 425-446 ◽  
Author(s):  
S. BABJI ◽  
P. GORAI ◽  
A. K. TANGIRALA
Keyword(s):  
Power Spectrum ◽  
Fourier Transforms ◽  
Control Valve ◽  
Wave Frequency ◽  
Intrinsic Mode Functions ◽  
Research Activity ◽  
Control Loops ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Valve Stiction

Two of the most important sources of degradation of control loop performance are (i) valve stiction and (ii) tight controller tuning, both of which lead to oscillations in closed–loop outputs. A factor that distinguishes these two sources is the nonlinear signature of the valve stiction; a tightly tuned controller produces oscillations due to a linear source. Detection and isolation of nonlinear fault sources is essential to correctly determine the cause of poor loop performance of control loops. Despite a rich research activity in this area, there is hardly a method which can isolate the simultaneous effects of these two sources. Moreover, the traditional spectral analysis based on Fourier Transforms is largely restricted by the assumption of stationarity in the data to detect and quantify valve nonlinearities. In this work, Hilbert–Huang Transform (HHT) is used to (i) detect valve nonlinearities and (ii) isolate linear and nonlinear fault sources. The key characteristic of HHT is that it represents nonlinearities as intra-wave frequency modulations allowing it to distinguish it from linearities which do not exhibit such modulations. The advantages of HHT-based methods are that (i) nonlinearities translate to a unique signature (ii) nonstationarities in data can be handled in a natural way. It is observed that nonlinearity is captured by a Intrinsic Mode Functions (IMF) obtained from the Empirical Mode Decomposition (EMD) of the process output. The Hilbert–Huang spectrum of these IMFs exhibits intra-wave frequency modulation. The power spectrum of the IMFs shows the presence of harmonics which is used to characterize the valve stiction nonlinearity. Subsequent to detection, quantification is done using the power spectrum of the IMFs. The proposed method is sensitive enough to detect low levels of valve stiction nonlinearities. Results from simulation using one-parameter valve stiction model are presented in support of the proposed methodology. The results demonstrate the advantage and potential of the HHT-based method.

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