valve backlash and stiction detection in integrating processes

Valve backlash and stiction detection in integrating processes

M. Farenzena and J. O. Trierweiler

Group of Intensification, Modelling, Simulation, Control and Optimization of Processes (GIMSCOP) Department of Chemical Engineering, Federal University of Rio Grande do Sul (UFRGS)

Rua Luiz Englert, s/n CEP: 90.040-040 - Porto Alegre - RS - BRAZIL, Fax: +55 51 3308 3277, Phone: +55 51 3308 4163

E-MAIL: {farenz, jorge}@enq.ufrgs.br

Abstract: Valve nonlinearities are responsible for producing limit cycles in the loop and consequently decreasing plant performance. In the case of an integrating plant, once the loop oscillation is detected, the engineer should distinguish between stiction and backlash, but no method is available in the literature to address this class of problem. In this paper, it is proposed a data-driven method to distinguish between loop stiction and backlash for integrating processes. The closed-loop pattern provides enough information to verify when either stiction or backlash is available. The efficacy of the proposed technique is corroborated by simulation case studies. Keywords: Valves, Backlash, Static friction, Integrators, Hysteresis.

1 INTRODUCTION In the last twenty years, both academia and industry have been focus on methods to evaluate in real time control loop performance. Many industrial surveys have provided the reasons for this concern: around 60% of all industrial loops perform poor (Bialkowski, 1993), and improve their performance means reduce loop variability and increase plant reliability. Among the most frequents “loop illnesses”, the valve has one major disorder: around of 30% of all valves have any degree of damage, being responsible for increase significantly loop variability. Two of the most frequent valve injuries are stiction and backlash.

Stiction imposes limit-cycles in the loop, increasing its variability and inserting plant-wide periodical disturbances, what explains why the large majority of the valve related works is focus on stiction. Backlash also increase loop variability, however in the case of open-loop stable plants, backlash only reduces the control loop performance, but does not insert limit-cycles, as stiction. On the other hand, when the plant is an integrator, a limit-cycle similar to stiction is inserted.

If a limit-cycle is detected in a given loop, it is important to distinguish between stiction and backlash, because backlash is an invertible nonlinearity, what allows a perfect compensation without valve replacement. On the other hand, stiction nonlinearity is more difficult to compensate. This is the scope of this work: to propose a data-based method to distinguish between stiction and backlash, in the presence of limit-cycles, for integrators.

In the literature, backlash detection has not received significant interest. Hagglund (2007) has proposed a method to detect backlash for stable loops. Moreover, techniques to quantify and to compensate backlash are also introduced in the same work. Ling et al. (2007) have introduced a nonparametric statistical method to diagnose four valve

problems: backlash, deadband, leakage, and blocking. However, stiction is not considered. Several works follow the same direction, whose scope is to distinguish among several valve problems, but they do not aim to distinguish between stiction and backlash. Bocaniala et al. (2003) have proposed a fuzzy classification solution for fault diagnosis of valve actuators. Düstegör et al. (2006) have suggested a graph method in which each fault is described by a three levels of knowledge. Tudoroiu and Zaheeruddin (2005) have analyzed HVAC systems and methods to detect and to diagnose faults, including backlash, based on frequency and spectral analysis.

This paper is segmented as follows: in section 2 a brief description about the backlash and stiction phenomena will be exposed. In section 3, the method here introduced will be described. In section 4, the proposed method will be applied in a set of cases studies, corroborating its validity. The paper ends with the concluding remarks.

2 STICTION: MODEL AND COMPUTATION

2.1 Deadband: definition According to Fisher-Rosemount (1999), deadband is defined as “the range through which an input signal can be varied, upon reversal of direction, without initiating an observable change in the output signal. Dead band is the name given to a general phenomenon that can apply to any device“. One cause of valve deadband is backlash, whose definition is “the general name given to a form of dead band that results from a temporary discontinuity between the input and output of a device when the input of the device changes direction. Slack, or looseness of a mechanical connection is a typical example.”

The phase plot of input-output behavior of a valve where the backlash is seen can be described as Fig. 1. It has two

Preprints of the 8th IFAC Symposium on Advanced Control of Chemical ProcessesThe International Federation of Automatic ControlFurama Riverfront, Singapore, July 10-13, 2012

© IFAC, 2012. All rights reserved. 320

regions: when the controller comes to rest or change its direction (A), the valves becomes stationary until the deadband is overcame (B), and then the valve starts the motion. The deadband magnitude is given by DB.

Fig. 1: Typical phase plot of a valve with backlash.

Comparing the process output for a loop where a valve with backlash is applied to an open loop stable process and to an integrator, the behavior is completely distinct. In the first case, only the process variability increases with the increasing backlash. Fig. 2 illustrates the process variable (PV) for four different DB for an open loop stable process, with a PI controller. The variability (σ2) is also shown.

Fig. 2: Process variable behavior for open loop stable processes with variable deadband.

For open loop stable processes, the backlash diagnostics is difficult, because its effect can be confused with a poorly tuned controller. However, the differentiation between stiction and backlash is not difficult, because only stiction can insert a limit-cycle.

On the other hand, if the process is an integrator, the process variable becomes oscillatory, spreading this behavior in all

loops. Fig. 3 shows the process variable for four SISO loops, with variable deadband, when the plant is an integrator.

Fig. 3: Process variable behavior for integrating processes with variable deadband.

Based on Figs. 2 and 3, it is clear the impact of plant open loop behavior, when backlash is present in the loop. When the plant is an integrator, then a limit-cycle is imposed, similar to stiction behavior. It is important to distinguish between these two valve damages, because backlash non-linearity can be inverted, i.e. using a simple algorithm as shown by Hagglund (2007) it can be compensated. On the other hand, stiction cannot be completely inverted and its compensation is more difficult (Srinivasan and Rengaswamy, 2008).

In the next section the stiction phenomenon will be briefly described and a parallel with backlash will be made.

2.2 Stiction – definition and model A valve with stiction has an apparently similar behavior then backlash, as shows the phase plot, described in Fig. 4 (Choudhury et al., 2008) .

When the valve changes its direction (A), the valve becomes sticky. The controller should overcome the deadband (AB) plus stickband (BC), called staticband or apparent stiction. Then, the valve jumps to a new position (D). The stiction model consists of these two parameters: S (staticband) and J (slipjump). Next, the valve starts moving, until its direction changes again or the valve comes to rest, between D and E.

The main difference between backlash and stiction is the slipjump, which is not present in backlash. It represents the abrupt release of potential energy stored in the actuator due to high static friction in the form of kinetic energy as the valve starts to move. The magnitude of the slipjump is crucial to determine the limit-cycle amplitude and frequency.

Generally, the stiction impact over the loop is more aggressive than backlash, because of the jump. It inserts limit-cycles in both open loop stable and integrating processes.

8th IFAC Symposium on Advanced Control of Chemical ProcessesFurama Riverfront, Singapore, July 10-13, 2012

321

Fig. 4: Typical (IO) behavior of a valve with stiction.

In the literature, several models are available to describe stiction behavior. In this work, the 2 parameters model proposed by Kano et al. (2004) will be used, because its simplicity and good agreement with the real stiction behavior.

3 BACKLASH AND STICTION DETECTION This section describes the method here proposed to distinguish between stiction and backlash for integrating processes.

Before, two considerations are taken: the sampling time is fast enough to describe the process dynamics and the loop does not have under-damped behavior. In the first case, if the sampling time is wrongly selected, i.e. slower than necessary, the method will point stiction in all cases. In the second, if the controller is fast tuned, the algorithm will detect stiction.

Fig. 5: Backlash response for an integrating process – peak (I) and intermediate points (II) slope.

The idea behind the method is very simple. If we carefully inspect the peaks (I) and the intermediate points (II) of the response for backlash and stiction phenomena there is a slight difference. Fig. 5 shows this behavior for backlash and Fig. 6 shows for stiction.

Fig. 6: Stiction response for an integrating process – peak (I) and intermediate points (II) slope.

The slope is the core of the method: in the case of backlash the angle in the peak (θpeak) and in the middle (θmiddle) are significantly different (see Fig. 5), while in the case of stiction they are similar (see Fig. 6). Thus, the stiction and backlash can be easily distinguished, through the angles in the peak and in the middle, as illustrated in Fig. 7.

The procedure for the method to distinguish between stiction and backlash is following described:

1. PV data is collected and the white-noise removed;

2. The peaks and valleys are identified and the angles for the peaks (θpeak) and the value between the valley and peak (θmiddle) are computed for each.

3. The mean value for each is computed ( and ). 4. The Backlash Index (BI) is computed based on and :

(1)


322

Fig. 7: Peak and middle angles for an integrating process with backlash.

The angles are measured considering the mean and the extreme points and the neighbor. If the angles are different, in the case of the peak, the mean angle is taken. If the data is corrupted by white-noise, the mean between three points is advised.

The following rules are used to diagnose stiction:

BI ≥ 3 backlash;

BI ≤ 1.5 stiction;

3 > BI > 1.5 no decision.

These values have been based on a large number of simulations. The main advantages of this method are that it is computationally fast and requires only routine operating data. Moreover, no controller tuning parameters and no information about the valve stem are necessary.

4 CASE STUDIES The objective of this section is to evaluate the proposed method to distinguish between stiction and backlash for integrating processes, using only routine operating data. In the first analysis, the stiction should be diagnosed, considering several plants, controllers, and stiction parameters, as described in Tab. 1. A SISO loop is used with a PI controller and integrating plant. The stiction model is inserted between controller and plant (Hammerstein model). In all cases, the sampling time was equal to 0.1.

Tab. 1: Loop parameters used in the stiction diagnostics.

Param Description Value

KC Controller gain [0.7:0.1:1.5 2:0.5:4] τI Controller integral

constant [1:0.2:2.4] τ

S Staticband [1:0.5:6] J Slipjump [0.5:0.1:1]S τ Process time constant [10:10:100]

Based on Tab. 1, a total of 52800 different scenarios have been analyzed. In 99.8% of all cases the stiction was correctly pointed, while in the remaining 0.2%, no conclusion is stated by the method. None of cases the method has detected incorrectly backlash.

In the complementary analysis, the backlash should be diagnosed under a wide variety of loop parameters. The values used for backlash detection are shown in Tab 2.

Tab. 2: Loop parameters used in the backlash diagnostics.

Param Description Value

KC Controller gain [0.7:0.1:1.5 2:0.5:4] τI Controller integral

Constant [1:0.2:2.4] τ

DB Deadband [0.5:0.5:6] τ Process time constant [10:10:100]

In this analysis, in all cases the algorithm detected the backlash correctly. None of the cases the conclusion was null or misleading.

In the second analysis, the influence of the sampling time will be evaluated. As previously stated, if this value is high, then the backlash detection will be spoiled. Using the same procedure, the deadband and process time constant will be varied according to Tab. 2. The controller gain will be set equal to 2 and the integral constant is equal to the process time constant. Tab. 3 shows the influence of sampling time (st) in the backlash detection.

Tab. 3: Percentage of backlash detection with variable sampling time.

st % of detection

0.1 100%

0.2 100%

0.5 100%

0.75 90%

1.0 90%

2.0 78%

5.0 38%


323

Tab. 3 shows the impact of sampling time over the method’s accuracy, where increasing sampling times cause reduction in backlash detection. However, it is clear that the method does not require an extremely fast sampling time.

5 CONCLUSIONS In this work, we have presented a novel solution to the problem of stiction and backlash detection in integrating loops. The detection method is based on process variable patterns of a valve with backlash and stiction. The slope in peaks and in the middle of the curve provides the necessary information for detection. This procedure can be automated using the proposed Backlash Index

The efficacy of the proposed technique was corroborated through several simulation case studies. When backlash or stiction should be diagnosed, a wide variety of plants, controllers, and stiction or backlash scenarios have been evaluated. In the first, only stiction is seen in the loop and in the second only backlash. In both cases, the percentage of correct detection was around 100%. No incorrect diagnostic was provided in all cases.

ACKNOWLEDGMENTS

The authors are very grateful for the grants from PETROBRAS.

REFERENCES

BIALKOWSKI, W. L. (1993) Dreams versus reality: A view from both sides of the gap. Pulp and Paper Canada, 94, 19-27.

BOCANIALA, C. D., SA DA COSTA, J. & LOURO, R. (2003) A fuzzy classification solution for fault diagnosis of valve actuators. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science).

CHOUDHURY, M. A. A. S., JAIN, M. & SHAH, S. L. (2008) Stiction - definition, modelling, detection and quantification. Journal of Process Control, 18, 232-243.

DÜSTEGÖR, D., FRISK, E., COCQUEMPOT, V., KRYSANDER, M. & STAROSWIECKI, M. (2006) Structural analysis of fault isolability in the DAMADICS benchmark. Control Engineering Practice, 14, 597-608.

FISHER-ROSEMOUNT, A. (1999) Control Valve Handbook.

HAGGLUND, T. (2007) Automatic on-line estimation of backlash in control loops. Journal of Process Control, 17, 489-499.

KANO, M., MARUTA, H., KUGEMOTO, H. & SHIMIZU, K. (2004) Practical model and detection algorithm for valve stiction. IN IFAC (Ed.) 7th IFAC DYCOPS. Boston, USA.

LING, B., ZEIFMAN, M. & LIU, M. (2007) A practical system for online diagnosis of control valve faults. Proceedings of the IEEE Conference on Decision and Control.

SRINIVASAN, R. & RENGASWAMY, R. (2008) Approaches for efficient stiction compensation in process control valves. Computers & Chemical Engineering, 32, 218-229.

TUDOROIU, N. & ZAHEERUDDIN, M. (2005) Fault detection and diagnosis of the valve actuators in HVAC systems, using frequency analysis. ICIECA 2005: International Conference on Industrial Electronics and Control Applications 2005.


324

valve backlash and stiction detection in integrating processes

Documents