Application of the intelligent alarm system for the plant operation

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Computers chem Engng, Vol. 21, Suppl., pp. $625-$630, 1997

Pergamon

© 1997 Elsevier Science Ltd All rights reserved Printed in Great Britain

PII:S0098-1354(97)00119-1

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Application of the intelligent alarm system for the plant operation Fumihiko Yamanaka Development and Engineering Research Center, Mizushima Plant, Mitsubishi Chemical Corporation 3 - 10,Ushiodori Kurashiki, Okayama 712,Japan

Takushi Nishiya Systems Development Laboratory, Hitachi ,LTD. 292 Yoshida-cho, Totsuka-ku, Yokohama, 244 Japan

Abstract: A new alarm system which provides a qualitative description of process data trends is presented in this paper. This description is based on a method of marking process data with significant words. The objectives of this system are (1)automatically to recognize a fluctuation on a process instead of plant operators, and (2)to develop a fundamental technique for data trends monitoring systems by using a qualitative description. Recently, this system was used to catch a fluctuation on a real process before it became dangerous situation. Finally, more applications of using a qualitative description for the plant operators which will be developed in the near future, and the method of detecting a reasonable fluctuations are presented. 1.

INTRODUCTION

condition further deteriorates and finally the interlock system will shutdown the plant.

In chemical plants, it is very important for plant operators to monitor the trend of process data to recognize a fluctuation in the process. They monitor the process condition using some simple alarms like HI,LO,DEV and so on, which come from process control systems. When they notice the alarm, some actions to recover the normal process condition will be taken by them. Sometimes, the operator's action to restore the normal condition after an alarm is too late, so the process condition further deteriorates.(Fig.1)

Table.1 shows the number of troubles classified into groups of causes. Many troubles (e.g. Sensor failure) have some "indications" before they become serious.

Cause of troubles Dirty Process Sensor Failure(Level) Sensor Failure(Flow) Sensor Failure(Pressure) Sensor Failure(Temp.) Leak Human Error Table.1

Fig.l

Numbers 54 38 30 14 7 16 31

Troubles

To detect a small indication, it is useful to watch trend graphs of process data. However, plant operators have to watch many trend graphs which increases their work load. (For example, in our olefin plant, there are about 5,000 points to be monitored by operators. So operators have to check about 600 trend graphs every shift.) Therefore, "monitoring process trends" is a main job of plant operators during the steady state condition. (Fig.2,3) In this paper, we propose a new alarm system that detects a fluctuation automatically and provides a

Alarm Systems

When the alarm occurs, if the operator takes some action, then the process condition will be recovered. But if the operator misses the alarm, the process $625

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qualitative description of process data. The technique which provides similar description using "triangular representation" are well known in these research area(J.T.Y.Cheung and G.Stephanopoulous, 1990). In their representation, the triangular episode was defined by continuous function, and its characteristics was represented using 1*t order or 2 nd order deviations of the episode. The direction of fluctuation was represented by the sign of 1st deviation of episode, and the shape of triangular episode defined by the sign of 2 "d deviation. (e.g. the episode that has [1 st deviation]=+ and [2nd deviation]=0 was defined "Linear Increase", and [1 st deviation]=+ and [2~d deviation]=+ was defined "Concave Upward Monotonic Increase")

Fig.2 Analysis of Operator's Work

Fig.3 Items for Monitoring In this system, the description of trend is based on the method of marking process data with significant words. The objectives of this system are to automatically recognize a fluctuation of a process instead of plant operators, and to develop a fundamental technique for monitoring process data trends by using a qualitative description.

2.

SIGNAL-TO-SYMBOL T R A N F O R M A T I O N

2.1 OUTLINE The method proposed in this paper is composed mainly of two parts, detection of the characteristic points of the pattern and storing patterns to be detected (dictionary) (T.Nishiya,1993). This method does not need to relearn patterns and is very robust in recognizing time-series data. The basic concept of the method is to expand timeseries data into a polynomial representation. The expansion coefficients express the features of the data. Also, using these features, characteristic points (those having extreme curvature) can be extracted. Using characteristic points, the time-series data are transformed into a series of line segments. Lastly, these line segments are compared with the dictionary by a DP (dynamic programming) matching method. The obtained similarity is the same as the correlation factor between time-series data and a word in the dictionary. 2.2 F L O W OF T R A N S F O R M A T I O N A flowchart of the transformation process is shown in Fig.4. First, some previous data sufficient for transformation arc entered into memory. In the next step characteristic points are extracted by convolution with the polynomial expansion filters. Here, each characteristic point has a peak value of the coefficient of degree 2 (peak value of curvature). Each time a new characteristic point is found, a line segment (vector) connecting the new characteristic point and the nearest one is constructed. In this procedure the newest sampling time is always considered to be a characteristic point. At each sampling time, the series of line segments of the sampling data and that stored in the dictionary are compared, and the similarity and the scale (the ratio of the pattern size) is calculated. When the matching (comparison) for all the words stored in the dictionary is finished, if there is a significant word which has larger similarity and scale than the predetermined threshold, that word is reported to the plant operator as an alarm. The above procedure is carried out at every sampling time, and operator support is provided. The following sections explain in detail the polynomial expansion, line segments construction, and matching process with the dictionary.

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] Input of a na|~lla! dots [

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[Extracttn, inflection ~

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[~,,r*.t..u*o by .... l.. ot li . . . . I.snts I Coopq[ S oith ~Ic~locory /ur sstchtngJ t Finished for s n event pattern I

Dictionary File ]

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We now give an example. When the defined interval is (a,b)=(-15,0), the feature extraction filters arc shown in Fig. 5. In Fig. 5, (a) and (b) are obtained for the two cases when o of the exponential function (2) is 2.0 and 4.0 respectively. The time-series data is expanded into the polynomial by (6), and the approximated value fe(t) at time t is obtained by substituting x=0 to (4), i.e.,

[ E~nluatlon of sinllsrtt~ and scsle I

Hm(t )

fe(t)= ~_,am(t)-'-~ YtS

I

[

Continue?

m=0

%t z ~m

(7)

= ~-~am(l)Wm(O) m=0

Fig.4 Flow diagram of the process state change detection by the signal-to-symbol transformation method

This approximated value fe(t) is used to calculate the value at the characteristic points.

2.3 POLYNOMIAL EXPANSION OF THE TIMESERIES DATA A polynomial of degree m, Hm(x), satisfying (1), is used to expand the time-series data.

e.5

b

E{Hm(X)H.(x)}E(x)=O(m:/:n)

(l)

x=a

where a and b are constants to define the range, and E(x) is

an exponential function,

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(2)

(a) E(x)~(- :¢2) ,a=2.O

When Hm(x) satisfies t~60

b

~_.,{nm(x)}2E(x):Am

(3)

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the time-series data f(t) is expanded into a polynomial around time t

f(t + X)- ~'~a (tl nm(x) m , , . A/X=

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(4)

m

where b

. H,n(X )

am(t)= E f ( t + x) "~'E(x) x=a ~A m

(5)

A filter Wm(x) to obtain the polynomial coefficients (hereafter called a feature extraction filter) is given by

H'(x)

(6)

Fig.5

Examples of the filter for polynomial expansion

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2.4 EXTRACTING THE CHARACTERISTIC POINTS AND CONSTRUCTING THE LINE SEGMENTS Extraction of the characteristic points and construction of the line segments are as follows: (a) By calculating the center of gravity of the interval for which the coefficient of the polynomial of degree 2 has the same positive or negative sign, the time of the characteristic point (the time that the original data has extreme curvature) can be extracted. (b) Approximate the time-series data to a series of line segments joining the characteristic points.

I

,T)'~tsi,ai ---~, )

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"-- i=1

El Si12 i=l

From the minimum value of the sum of square of the error, the similarity is obtained as I

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2.5 COMPARING WITH THE DICTIONARY Here we will explain the structure of the dictionary and the definition of the similarity and the scale. (1) STRUCTURE OF THE DICTIONARY The dictionary contains the event name which should be detected from the process, and has a corresponding series of line segments (vectors). Some examples are •shown in Table 2. The events shown with ( ) are the opposite events shown by the dotted lines. In the dictionary any other pattern can be registered.

(9)

t

a;

2

*=

The similarity S satisfies - 1 . 0 < S < I . 0 , and has the same form as the general correlation factor. (3) COMPARING WITH THE DICTIONARY The vectors of the process data are compared with the vectors of the events in the dictionary by using DP (dynamic programming). When the similarity S and the scale K satisfy the conditions, S > S

(S: lower limit of the similarity)

(11)

K _) K_ (K__:lower limit of the scale factor) both vector series are considered to be corresponding.

Fixed

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down)

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Rise end down

(Down to fixed) (Down and vise,

Table.2 Examplesof the Dictionary (2) SIMILARITY AND SCALE We define a series of line segments registered in the dictionary as si(i=l,2,.",I), and a series of line segments of the process data as aj(j=l,2,.",J). The line segments (vectors) are multiplied by the scale K, the sum of square of the error between both series of line segments (vectors) yields I

E=ElK*s,-ai[ 2

(8)

APPLICATION TO THE PLANT

The proposed method has been applied to the plant operator support system using a real time process computer on our main plant in Mizushima. At first, we performed field tests of this system. The objective of the field tests is to consider how to tune parameters to detect important features in the trend. In this system, two parameters; the threshold and the sampling period are to determine. After some trials, we def'med rules to tune these parameters as follows: (1) Threshold : defined by the standard deviation of process data at the steady state, threshold must be larger than three times of the standard deviation. (2) Sampling period : defined by the purpose of using this system. If it is necessary to detect fast fluctuation like as sensor failure, real-time detection will useful. Otherwise, If it is necessary to detect very slow indications like as leak, long-period detection will useful. So, we define this parameter as follows; real-time detection---1 to 3 minutes long-period detection---5 minutes to 15 minutes

i=1

The scale K minimizing (31) can be obtained as

After these field tests, these thresholds were defined by above rules using steady state data sets, and this system became a real-time application.

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4.

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DETECTION EXAMPLES

This Intelligent Alarm System was applied in our main olefin plant in Mizushima about 3 years ago. We (application engineers) took a long time to tune the parameters, and about 2 years ago, this system began to work efficiently. Recently, this system detected some fluctuations before it became dangerous situation. Two examples are shown below. (1) One example is an oscillatory level controller caused by a change in the process dynamics. Process trend and example of detecting result are shown in Fig.6. This alarm system detected the "Hunting" of the process variable of that controller, but the other alarm systems detected no alarms because its value was inside the HiLo limits. The plant operator realized this phenomenon early by the "Hunting" alarm, so they could quickly restore normal operation. This example was detected real-time in which the threshold was three times the standard deviation and the sampling period was 1 minute.

Fig.7

Example2(Naphtha Feed Line)

In this Example, we can see a large MV(Manipulate Value)'s fluctuation without a change in the PV(Process Value)'s. (MV changes from about 57% to 62%) If the MV increases, the control loop performance degrades, and it cannot deal with big set-point changes, big disturbance and so on. When operators catch this phenomenon, they change the control status from AUTO to MANUAL, and flush this line, and then MV returns to normal. This is a good example of detecting a fault which may become a root of serious problem.

75.00

(HI)

55.00

(LO) Fig.6

Example 1 (Level Controller )

(2) Sometimes, the manipulated variable of the naphtha feed control line of cracking furnace suddenly changed by a blockage of the valve or sensor. Plant operators could not realize this phenomena without watching the trend screen, but using this system, this phenomena can be detected automatically and operators can take action to recover the process condition. This example was detected by using 8 hour period detection. The threshold was 3-times the standard deviation and the sampling time was 5 minutes. An example of detecting this phenomena is shown in Fig.7.

5.

CONCLUSIONS

In this paper, we have shown a method of Signal-toSymbol transformation technology and its application. Many technologies like this system have been developed at many universities(J.T.Y.Cheung, 1990 : James R. Whiteley, 1996). We showed the effectiveness of these techniques through the trial use in real-time operation. This technology and system contain the effectiveness as below: (1)Using this alarm system, plant operators can detect the "hidden fluctuations" which existed in the back of the CRT displays. (2)Plant operators can catch the fluctuation as a "Word" with which they are very familiar. (3)Early detection of plant fluctuations. This system has a good performance and effectiveness, but there are some unresolved issues including : (1) Parameter Tuning : the normalization of the methods to determine parameters is important. For example, there are about 5000 process variables to be monitored in our main olefm plant. Therefore, we must consider how to set the initial parameters for each point automatically, and how to tune these parameters. Some techniques to solve this problem under consideration are based on signal processing techniques like "Wavelet". (2) Operator Support System : more useful guidance

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The authors wish to express their sincere thanks to all the members of this project for their valuable discussions and suggestions during the development and application process. REFERENCES

(1)T.Nishiya: A Signal-to-Symbol Transformation Method of Time-Series Data for Detecting Signs of Process Change, Proe. of IEEE International Workshop on Neuro-Fuzzy Control: Instrumentation and Control Applications, pp. 345-351, March 1993 (2)K.Kawaguchi, S.Oishi and T.Nishiya: Intelligent Plant Operation Support System for Plant and Process Control, Hitachi Review, Vol.41, No.l, pp.45-50, 1992 (3)J. T. Y. Cheung and G. Stephanopoulous: Representation of Process Trends-Part 1. A Formal Representation Framework, Computers chem. Engng., Vo1.14, No.4/5, pp.495-510, 1990 (4)J. T. Y. Cheung and G. Stephanopoulous: Representation of Process Trends-Part 2. The Problem of Scale and Qualitative Scaling, Computers chem. Engng., Vol.14, No.4/5, pp.511-539, 1990 (5)Jmaes R. Whiteley, James F. Davis and Stanley C. Ahait: Observations and Problems Applying ART2 for Dynamic Sensor Pattern Interpretation, IEEE Transactions on Systems, Man, and Cybernetics-Part A: System and Humans, Vol.26, No.4, pp.423-437, July 1996