By Mohieddine Jelali, Biao Huang
The presence of non-linearities, comparable to stiction and deadband, locations limits at the functionality of keep watch over valves. certainly, within the method industries, stiction is the commonest valve challenge, and over the past decade various varied concepts for overcoming it were proposed.
Detection and analysis of Stiction up to the mark Loops represents a entire presentation of those tools, together with their ideas, assumptions, strengths and disadvantages. instructions and dealing strategies are supplied for the implementation of every procedure and MATLAB®-based software program should be downloaded from www.ualberta.ca/~bhuang/stiction-book permitting readers to use the the way to their very own info. equipment for the trouble of stiction results are proposed in the basic context of:
• oscillation detection up to speed loops;
• stiction detection and analysis; and
• stiction quantification and analysis of a number of faults.
The cutting-edge algorithms provided during this booklet are confirmed and in comparison in commercial case stories of various foundation – chemical substances, development, mining, pulp and paper, mineral and steel processing. Industry-based engineers will locate the ebook to be worthwhile suggestions in expanding the functionality in their keep watch over loops whereas educational researchers and graduate scholars attracted to regulate functionality and fault detection will find a wealth of static-friction-related learn and valuable algorithms.
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Extra info for Detection and Diagnosis of Stiction in Control Loops: State of the Art and Advanced Methods
S. F. Thornhill, M. L. Shah mv (thick line) and op (thin line) mv vs. op linear pure deadband stiction (undershoot) stiction (no offset) stiction (overshoot) 0 100 200 300 time/s (a) pv (thick line) and op (thin line) pv vs. op linear pure deadband stiction (undershoot) stiction (no offset) stiction (overshoot) 0 100 200 300 time/s (b) Fig. 3 Closed-loop simulation results of a concentration loop using the data-driven stiction model: a) time trends of MV and OP (left), MV–OP mappings (right); b) time trends of PV and OP (left), PV–OP mappings (right) 2 Stiction Modelling 31 Fig.
In the modelling approach described herein, it is assumed that the controller has a filter to remove the high-frequency noise from the signal. 2 gives more discussion on this topic. Then, the control signal is translated to the percentage of valve travel with the help of a linear look-up table. The model consists of two parameters – namely the size of deadband plus stickband S (specified in the input axis) and slip jump J (specified on the output axis). 2 summarises the model algorithm, which can be described as: • First, the controller output (mA) is converted to valve travel percentage using a look-up table.
If it is an integrating process with a PI-controller, the valve signal is integrated twice and appears as a series of parabolic segments. 3a shows the controller output (OP) and valve position (MV). Mapping of MV–OP clearly shows the stiction phenomena in the valve. It is common practice to use a mapping of PV–OP for valve diagnosis; see Fig. 3. However, in this case such a mapping only shows elliptical trajectories with sharp turn-around points. The reason for the latter is that the PV–OP map captures not only the non-linear valve characteristic but also the dynamics of the process.