Dynamic Analysis and Monitoring of Process Control Loops
Yoshiyuki Yamashita1
1Department of Chemical Engineering,Tokyo University of Agriculture and Technology
2-24-16 Naka-cho, Koganei, Tokyo 184-8588
JAPANyama_pse@cc.tuat.ac.jp

Abstract
Dynamic analysis and monitoring is of great
importance in industrial process plant. Very often, aqualitative analysis of process data is enough to analyze
the situation of the plant. This paper introduces amethodology to analyze qualitative trend of time series
data at first. Based on the methodology, a method todiagnose a control valve in a process control loop isintroduced. The proposed diagnosis method issuccessfully applied to several data sets in industrial
chemical processes.

1. Introduction
Monitoring of process control loops is a computer aided
task based on the online analysis of process time series
data. For the analysis of time series data, many kinds ofmethods have been developed and used. Typicalexamples of the methods are spectral analysis, ARMA
methodologies and Kalman filters. Among various
methods, simple trend analysis is useful in various
applications such as the monitoring of process plants.
Dash et al. proposed an interval-halving framework
to automatically identify the qualitative shapes of sensor
trends [1]. This method is based on a polynomial-fit anditeratively halving its approximation interval. It is usefulfor offline analysis, but it cannot be used online.
Charbonnier et al. proposed a methodology to extract
online temporal trends from a time series [2]. They used
the method to several applications such as alarm
management in intensive care units, food processmonitoring, and abnormal situation management in asimple process simulation.
In this paper, a method of qualitative trend analysis
for two variables time series data is described at first [3].
Based on this qualitative trend analysis method, amethod of dynamic analysis and monitoring control
loops is shown [4]. Then, application results of themethod to several industrial plants are shown in detail.
The result shows effectiveness of the method as amonitoring method of process control loops.

Fig. 1: Symbolic representation of a time series

2. Description of Qualitative Trend
2.1 Qualitative Trend
Trends or shapes of a time series signal can bequalitatively represented as a sequence of symbolicvalues. Simplest representation is using three symbols:
increasing (I), steady (S), and decreasing (D). Figure 1
shows these three primitives in a time value plane.
Alternatively, three primitives plus (+), zero (0) andminus () are used, corresponding to the signs of theirrespective derivatives. Although, more complex
representation using second derivative is also proposed,
only the three symbols representation is used in thispaper.
Once a time series data is represented in asequence of qualitative symbols, many trend analysis
problems will be solved effectively. For example, fault
diagnosis problem of process systems comes down to a
problem of a simple symbolic pattern matching between
the current trend and abnormal trends. Then various
kinds of pattern matching methods can be used forfurther analysis.
2.3 Qualitative Movements
For the analysis of time series signals it is often useful toconsider two variables simultaneously in a u - y plane.
The time axis is not shown explicitly in that plane, but it
corresponds to the movement of the behavior.
To represent qualitative movement in a u - y plane,
a combination of the three qualitative primitives for eachvariable is introduced. As shown in Fig. 2, eight symbols
can be defined to represent qualitative movement. Other
than these eight symbols, SS can also be used for asymbol without any movement. Movement in a u- yFig. 3: Typical stiction patterns

Fig. 2: Qualitative movements in u-y plane

plane can be represented qualitatively using these ninesymbols [3].
A sequence of these symbols represents amovement pattern in the plane. For example, (IS SD DSSI) represents a clockwise rectangular movement. In thesequence of movements, similar movements arecombined to form episodes. For example, (IS IS SI SI SI)
is equivalent to (IS2 SI3). More sophisticated procedure
to extract qualitative movement patterns is investigated[3]. For the analysis of noisy signal, these kinds ofmethod will be required.
Dynamic analysis of a two variables system can bequalitatively analyzed by using this qualitative
movement representation. As an example of theapplication of this representation to an industrial problem,
diagnosis of process control valves will be described inthe next section.
2.2 Implementation
For the implementation of the method to noisy data,
preprocessing of time series is required before
symbolizing the data [3]. The proposed procedure of the
preprocessing is given below:(a) Temporal Segmentation; A univariate time series issplitting into successive line segments. Parameter
identification is based on the linear least-square
method. The detection whether the linear
approximation is acceptable or not is based on thecumulative sum (cusum).
(b) Classification of Segments; Temporal patterns areclassified into the following six classes: Steady,Increase, Decrease, Positive Step, Negative Step andTransient.
(c) Aggregation of Segments; After the classification ofeach time segment, if possible, consecutive classesof the shapes are aggregated to abstract temporalpatterns.

3. Diagnosis of Process Control Loops
3.1 Control Loop Monitoring
Process Monitoring is very important to ensure safety,
product quality, energy conservation and so on. A usual
process plant has hundreds or thousands of control loops.
Quite a few loops have oscillatory behavior. Industrial
engineers highly demand a tool to facilitate loop
monitoring. Poor control performance can be caused by
improper controller tuning, external disturbances, andso-called stiction in a control valve, which is often the
only moving part in the loop.
Input-output characteristics of a valve in
oscillating loop are investigated by visual inspection of
typical oscillating data sets. The valve output is the valve
position; the valve input is the controller output. If acontrol valve has high static friction, the valve position
does not change from small changes of the controller
output. The controller then increases the set-point to thevalve. Then the valve breaks off and moves to a newposition with a slip-jump. Figure 4 shows idealized
typical shapes for the dynamic input-output
characteristics of a sticky valve [4, 5].
3.2 Index to Detect Valve Stiction
Several researchers have investigated methods todiagnose likely causes of oscillations in process control
loops. Horch proposed a method of diagnosing
oscillations based on the cross correlation between thecontroller output and the process output [6]. We
proposed several shape-based methods to diagnose
stiction using input-output characteristics of controlvalves [7].
A fragment of the movement sequence can berepresented by a sequence of two successive patterns.
For example, if pattern II follows the pattern IS, themovement is represented as (IS II). Using thisrepresentation, typical movement for valve stiction canbe represented as four fragments (IS II), (DS DD), (IS SI),and (DS SD) as shown in Fig. 3. All sticky motions ofvalve stiction, IS and DS, should be a part of thesepatterns. The degree of stiction can be evaluated bycounting the time period of IS and DS in these fourfragments of patterns. Subsequently, an index can bedefined as

 IS II   IS SI   DS DD   DS SD total

whereis the duration of the observation period,andandare time periods for patterns (IS II)
and (DS DD), respectively. This index will become large
if the valve has severe stiction. As an extreme case, theindex becomes close to unity if the valve does not
move for changes of controller output. If the signals arerandom, the value of is likely to become 0.25 because
it represents two out of eight patterns. Therefore, one caninfer that the loop is likely to have valve stiction if theindex value is greater than 0.25. Before applying thismethod, it is recommended to select adequate samplinginterval so as to reduce the effect of noise. If an
oscillating frequency of the signal is available, asampling interval corresponding to about 16 times of theoscillating frequency is recommended.
To qualify the stiction, the width of the sticky
movement can also be calculated for the found typical
sticky patterns. The width is defined by the mean valueof the maximum differences of controller-output during
each IS and DS period in sticky patterns.
3.2 Industrial Example
This section presents an evaluation of the proposed
method using real-world data sets collected from anindustrial chemical plan [4]. All these data sets have1440 samples at one-minute intervals; all the samples
were used for the analysis. All the variables arenormalized based on their standard deviation and meanvalue. A normalized time series of four single input
single output (SISO) control loops is shown in Fig. 4. In
this figure solid lines are flowrates, which correspond tovalve positions. Dotted lines represent for controller
outputs. Input-output characteristics of the valve for eachloop are also depicted in the same figure.
Consider first the level control case with valve
stiction (Loop 1). An input-output plot of the valve andthe loop are shown in the figure. To clarify details, fewer
points are indicated in these figures. Table 1 lists
numerical values of the detection indices. The index for
this case has a relatively large value, correctly indicating
stiction. This loop is for a level control, which is an
integrator process; the cross-correlation method
proposed by Horch [6] can not be used for this loop.
The next example illustrates a flow control loopwith valve stiction (Loop 2). Table 1 lists the numericalFig. 4: Industrial data setsvalue of the detection index. The index has relatively
large value and indicates stiction correctly.
Loop 3 has fluctuations caused by a tuning problem.
Calculated index can be found in Table 1. In this example,
the index shows a small value and indicates no stiction.
As the result, the cause of the oscillation is diagnosed as
improper controller tuning or external disturbances.
Although the true cause is the poor tuning, in this case,
we need another methodology to distinguish these twocauses.
Loop 4 is an example of external disturbances in theflowrate. They are visible around 900 and 1150 min. The
calculated detection index is shown in Table 1. In thisexample, index shows a small value and indicates nostiction. The first half part of this data set can beconsidered a case of normal operation because it has nolarge disturbance. Therefore, we consider this firsthalf-length data set as Loop 4a. In this case, becomesvery small and indicates no stiction.
Table 1. Result of the diagnosis
Case
Loop 1: Level control(stiction)
Loop 2: Flow control(stiction)
Loop 3: Level control(tuning problem)
Loop 4: Flow control(disturbances)
Loop 4a: Flow control(normal)width
0.62

1.37

0.48

1.12

0.12

1.01

0.14

0.48

0.07



7. Conclusions

References

In this paper, a methodology to analyze time series
data is described at first. Especially, a methodology toextract qualitative movement patterns of time series data
in a two variables plane was presented. Nine primitives
are used for the qualitative representation of a movement.
The method contains the following three steps:
segmentation of the movements of the two signals intoline segments, classification of the segments into nineprimitives, and aggregation of the primitives. Thisqualitative description formalism is a general method
and can be used to identify various kinds of dynamic
movements in an input-output plot. If this formalism candescribe knowledge regarding the movement of a
specific phenomenon, it will be easy to detect thephenomenon.
By using this qualitative description method, amethod to diagnose valve stiction in a process control
loop was introduced. The diagnosis method was applied
to several industrial plant data. The result shows that thecharacteristic behavior of the signals is well represented
by the extracted qualitative movements. It also showsthat this method performed excellently for all cases
including flow control loops and level control loops.
Detection of sticky valve is useful to find the valve
for maintenance. Valve maintenance can reduce thestatic friction forces and will improve performance of thecontrol loop. Alternatively, stiction compensation
methods can mitigate the problem. One of the wellkonwn compensation methods is to add short pulses,
which is called knocker, to the control signal [8].
Diagnosis of valve stiction will help decision making forthe actions in plant operation and will effectively
improve performance of the control loops.

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