THESIS
2015
xiv, 74 pages : illustrations (some color) ; 30 cm
Abstract
Control valve is one of the most widely used actuator in process control. Its reliability is of
great importance for the reliable performance of control loops. Valve stiction is a common
problem which control loops usually suffer from. It prevents valve from responding to the
control signal accurately and immediately, and even results in further performance
degradation and oscillations in control loops. In view of the shortcomings of the existing
valve stiction models and compensation methods, a reliability-based stochastic valve stiction
model and an improved stiction compensation signal have been proposed in this research.
In the modeling part, reliability engineering is firstly introduced and valve reliability is
discussed in two dimensions: the lifetime dimension and working...[
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Control valve is one of the most widely used actuator in process control. Its reliability is of
great importance for the reliable performance of control loops. Valve stiction is a common
problem which control loops usually suffer from. It prevents valve from responding to the
control signal accurately and immediately, and even results in further performance
degradation and oscillations in control loops. In view of the shortcomings of the existing
valve stiction models and compensation methods, a reliability-based stochastic valve stiction
model and an improved stiction compensation signal have been proposed in this research.
In the modeling part, reliability engineering is firstly introduced and valve reliability is
discussed in two dimensions: the lifetime dimension and working-condition dimension. After
that, a stochastic valve stiction model is proposed based on the working-condition dimension
of a 3D reliability model. Then, its identification algorithm with given SISO (single-input
single-output) process model is presented and demonstrated by simulation. Besides, an
iterative optimization procedure is also proposed for simultaneous identification of both the
stiction model and the process model. This procedure is demonstrated by simulations as well.
In the compensating part, an improved Knocker compensation signal is proposed. This
improved signal is based on the stochastic valve stiction model presented in this study and
aimed to compensate more accurately with variable amplitude and adjustable pulse width.
Different compensating performances of different compensation parameters are simulated and discussed. Suggestions for compensation signal parameter tuning are provided for
performance optimization.
It can be concluded that the reliability-based stochastic valve stiction model provides a
more comprehensive and flexible expression of stiction problem. The improved compensation
signal, based on the stochastic stiction model, is capable of compensating more accurately
compared with traditional Knocker signal. Therefore, this research shows a new opportunity
to solve valve stiction problem.
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