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Applied Soft Computing 11 (2011) 1057–1066

Contents lists available at ScienceDirect

Applied Soft Computing

journa l homepage: www.e lsev ier .com/ locate /asoc

esign of a pipeline leakage detection using expert system: A novel approach

.A. Laurentysa,∗, C.H.M. Bomfimb, B.R. Menezesa, W.M. Caminhasa

Universidade Federal de Minas Gerais - UFMG, Av. Antonio Carlos, 6627 Belo Horizonte, MG 31270901, BrazilRefinaria Gabriel Passos - PETROBRAS, BR 381 km 427, P.O. Box 21, 32530-000 Betim, MG, Brazil

r t i c l e i n f o

rticle history:eceived 7 November 2008eceived in revised form9 December 2009

a b s t r a c t

Pipeline leakage is a demand from governmental and environmental associations that companies needto comply with. Due the high accuracy on detecting leakage, it is necessary to set procedures that willachieve the leading performance. This paper describes a methodology to set instrumentations systemsto accomplish with the legal requirement keeping high reliability during normal and fail operations

ccepted 7 February 2010vailable online 13 February 2010

eywords:ault detectioneural Networksuzzy systems

conditions. To achieving the described state this paper proposes a set of models acting as Expert systems:each one observing and diagnosing pipeline leakage in real-time. The proposed system also validates theoperations according the business rules applied to it. A set of techniques is applied in order to be possiblethe system executes its function: fuzzy logic, neural network, genetic algorithm and statistic analysis.

The application of the methodology proposed is in operation supervising pipeline in a Brazilianpetroleum installation.

nsembles

. Introduction

Pipeline is the more efficient way to transport fluid, specially,etroleum and its products. High flow capacity allied with lowost in a long term are the attractive of this petroleum productsransport modal. That pipeline usually crosses areas where livearge populations or areas with high importance to environmen-al preservation. The key concern with pipeline is leakage. Even amall leakage can cause a large damage to the environmental or canut people on a great risk. A series of accidents around of the worldut the pipeline on the focus [18,22,26,28,34] demanding safe andreventive actions. Among these actions the installation of a super-isory system [15] to detect and take care of the necessary actionss an important demand. The supervisory system has to minimizehe amount of fluid that will leak before the flow is cut. To performhe supervision is necessary to implement a Failure Detection andsolation (FDI) [25,35] system that will detect abnormal behavioruring its operation. A pipeline behavior model should be built toompose the FDI system. The system is built to minimize the sizef detectable leakage while keeping nuisance alarms low [12,13]. It

s very important to minimize nuisance alarms to assure that oper-tors will rely on that system. Besides that each time a nuisancelarm is issued an operation shutdown occurs. It causes delaysn the transference of products and decrease pipeline availabil-

∗ Corresponding author.E-mail addresses: ka.laurentys@gmail.com, carlosl@superig.com.br

C.A. Laurentys).

568-4946/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.asoc.2010.02.005

© 2010 Elsevier B.V. All rights reserved.

ity. In an economic sense, this will become the pipeline operationanti-economic. Minimal performance of the leakage supervisorysystem is stated by the government in USA [18,22,34]. This pipelineperformance constrains demands high accuracy from the instru-mentation. Reaching the minimal specified performance requirescombination of standard instrumentation with software solution.Combination of hardware and software will comply with the spec-ifications keeping the system costs feasible.

This paper presents a methodology applied to an installationwhich uses the mentioned combination of hardware and softwareto provide a reliable and accurate leakage detection system.

In the context of computational intelligence, several meth-ods have been successfully applied to fault detection: fuzzy logic[16,39], neural network [6], genetic algorithm [9,37] and statisticanalysis [7]. The methodology of this paper also is based on a com-bination of this approaches that will be further detailed in eachexpert system proposed.

The methodology uses the concept of an ensemble to improvethe performance in both areas: detection capacity and confidenceof the leakage detection.

2. Pipeline process description

The typical installation to transfer petroleum or its products

using pipelines [2,29] is presented in Fig. 1. The available measure-ments are the flow, pressure and temperature at pipeline input andits output.

Based on that information all detection shall be achieved. Evenfor a long pipeline there is not more information than that. Although

dx.doi.org/10.1016/j.asoc.2010.02.005http://www.sciencedirect.com/science/journal/15684946www.elsevier.com/locate/asocmailto:ka.laurentys@gmail.commailto:carlosl@superig.com.brdx.doi.org/10.1016/j.asoc.2010.02.005

1058 C.A. Laurentys et al. / Applied Soft Com

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sohnman

Fig. 1. Typical fluxogram of a transference process.

esirable, it is not feasible to install instruments to take more mea-urements due to practical difficulties to maintain and collect thatnformation in a real-time base.

To diminish this problem long pipeline has intermediate mea-urements. However that installations are far from each other moreften they are more than 100 km apart. In a pipeline showed we

ave one installation which acts as an origin point and we haveothers that are destination points. That installation is a com-on when we have one refinery or base in Brazil surrounded bynumber of wholesale companies. This pipeline has a short length,ormally less than 30 km, and delivery only one product.

Fig. 2. Network and sys

puting 11 (2011) 1057–1066

A network of programmable controller is used to collect all data.Fig. 2 shows the architecture of this network and the details ofthe Digital Control System (DCS) and process computer connec-tion. These data are sent to a DCS where we have a first level ofthe FDI system. That implementation assures a high level of avail-ability although we miss the accuracy when detecting a leakage.This fact occurs because of the DCS has some limitations of accu-racy. Connect to the DCS we have a process computer that uses aSupervisory Control and Data Acquisition System (SCADA) [14] tointerface with the movement management system. On this processcomputer a FDI is in operation. FDI issues all detection and diagno-sis to the SCADA and DCS. The decision to stop or keep the operationin progress is taken nowadays by the operator. That is not unusualchoice for this FDI system [21]. The FDI can take that decision andautomatically shutdown the pipeline operation too. It is enough toenable this functionality into DCS once the process computer is ableto send commands to DCS logic.

3. A short review on pipeline leakage detection system

Working with pipeline leakage detection problem we needto use an approach that takes in count the available measure-

ments. There is a compromise between cost and detection capacity.Although we can install a lot of sensor to improve our system thatis not always feasible due a technological edges or costs. There arevarious methods to detect leaks (e.g. acoustic, hydraulic transientanalysis, volume balance). Considering available measurement as

tem architecture.

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C.A. Laurentys et al. / Applied S

howed in Fig. 2 it takes from the process a series of measurementnd generates a residue that will characterize the operation of theipeline. As stated in a set of works [4,3,36] normally mass bal-nce is applied to analyze the operation. This mass balance can beotten from the integration of the corrected flow or from the uncor-ected flow. Corrected flow is a calculated flow when we appliedressure and temperature correction on it [24]. It is called net flowoo. Uncorrected flow is a flow taken direct from the measurementnstrument in the actual line condition. It is called gross flow too. Inll cases is necessary to define the window time we need to use inur detection system. If we choose to use a short window to inte-rate the flow most the time we will decrease the accuracy of ourystem. In the other hand if we use a long time window we increasehe accuracy however we will have an increase in the time to detecthe leakage. Because the minimal performance specification weave to detect the leakage engineers design the system combin-

ng accuracy with leakage detection system to achieve the requirederformance. Measurement accuracy is an issue on this system [33].s the flow meter accuracy increases ability to detect small leak-ges will increase too. However to assure we will have the bestvailable performance from the installation a good combination ofardware and knowledge shall be used [21]. Although mass or vol-me approaches are the most common technique described in theorks on pipeline leakage detection we can see some alternatives

or that in the literature. Most of them are to observe the processsing another Expert systems not based on the balance directly.tate space technique is one proposed detection system [12,23]or pipeline leakage detection. Linear model is derived from the

omentum balance equation. Residue generate from this modelnd its actual measured value will be used to provide the failureetection. The system will give the alarm concerning the exis-ence of a leakage and will estimate the location of it. Howeverhat approach has some drawbacks. Filters that depend on the leakize and location used in the models [12] that converge to matchhe leakage missing the capacity to detect it. A bank of Kalman fil-ers were tested [12] to solve the same problem. However somearameters from the pipeline (e.g. roughness, junctions, presencef valves) are not known precisely making the location problemery difficult to be solved. Another way [23] uses an auto-regressiveoving average with exogenous inputs (ARMAX) [17,8] structure

o build the models. A simple approach is to use just the flow at thenlet and outlet [12] of the pipeline (mass balance technique). Aross-correlation estimation of the inlet and outlet measurementsre used to detect the leakage. This cross-correlation consideredow transport delay and the instrument offset parameter. Impor-

Fig. 3. Ensemble architecture applied

puting 11 (2011) 1057–1066 1059

tant results are presented on this paper. Observing this paper wecan see how accurate is the measurement system: the differencebetween the 2 m is only 0.15% of the total flow. That indicateswe can have a highly sensitive leakage detection system. Anotherexample of leakage detection algorithm uses a pressure pattern asan indication of the existence of a leakage [21]. Such algorithms arehelpful even when we have a multiphase flow.

4. Ensemble concept applied to the problem in anindustrial installation

Section 3 describes the usual approach to leakage detection andits main issue: define the time window [33]. The ideal solution isa combination of the two choices: short and long time window. Byusing a short time window the decision making would have to beextremely fast for huge leakages. By using a long time window itcould be decision making about the little leakages would be hard.So the idea of ensemble [10,11] makes sense in this application.Ensemble is a combination of Expert systems, neural networks inmost of the cases, each one able to solve the classification problemusing a particular expertise. In the neural network application thisparticular expertise can be since a different way to training the net-work to a different network architecture. Combining the solutionof each Expert system makes the solver of the problem of leakagedetection more accurate and faster. It is better then trying to use justone of the available algorithms with just one single time windowto solve all problem. The system that will be described was builtaround a set of Expert systems. Each Expert system has a differentfeature that gives to it a different capacity to diagnosis the process.They compound an ensemble that will be responsible not only todetect the leakage but make sure that all the operation rules willbe enforced. The main rule is that there will be only one receiver ata time. Parallel operation is forbidden. Even transference betweentwo bases when the system is operating is not allowed. In order toperform all the required supervisions six Expert systems are used:

1. receiver identifier;2. leakage detector based on average error;3. leakage detector based on fuzzy average error;4. leakage detector based on a gross totalizer;

5. leakage detector based on a net totalizer;6. received flow estimator.

Expert system 1 uses a fuzzy system to give the informationthat there is an operation in progress and to inform which bases

to pipeline leakage detection.

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tions were diagnosed precisely during the test period. Receiver basechanges were identified and informed to the supervisory system.One aspect important is all functions to interface with operator,including alarm function, is implemented in an available supervi-

060 C.A. Laurentys et al. / Applied S

re receiving the sent product. Once we have that we can detect ifur business rules are being accomplished.

Expert systems 2 and 3 use an information based on a differenceetween sent and received flow to detect the existence of a leakage.he main difference between the two Expert systems is the secondne uses a transformed error. The transformation is performed byfuzzy system.

Expert systems 4 and 5 totalize the sent and received flow alonghe operation and monitor the error to issue a diagnosis.

Expert system 6 is a flow estimator. It uses a pressure informa-ion from the side of the receiver to give a flow estimation to besed in case of flow transmitter failure [32].

As described we have four Expert systems to detect leakage.hey are correlated two by two. Expert systems 2 and 3 use flowrror and Expert systems 4 and 5 use totalizer error to perform theirasks.

Fig. 3 presents the ensemble architecture applied to pipelineeakage detection. The Expert system 1 is the kernel of the solution.ystem depends upon it to start the supervision. An alternative tot is the pipeline operator manually starts supervision. Once thexpert systems are activate they give the individual diagnosis. Oneecider takes it and produces the final diagnosis from the system.he aggregation is based on one characteristic concept appliedhen building this system. When Expert systems found that the

peration is normal they can be individually wrong. However ifust one found that the operation is in a failure the diagnosis isight with a confidence of almost a 100%.

. Building Expert system 1

Expert system 1 is the most important one. It was built based onfuzzy system. This fuzzy system [38] shall ensemble the operatorhen identifying that the pipeline is operating. We need to identifyhich rules the operator uses to represent the knowledge needed

n our system. It’s not a simple task [19] although we have a simplerocess. The rules we need in our system are:

if there is not sent flow there is not an operation in progress;if there is an operation in progress then there is (are) a receiverbase(s) with non-zero flow(s);if there is an operation in progress and just one receiver base thenthe operation is valid;if there is an operation in progress and two receiver bases for ashort period of time then there is a changing in the receiver base.This is the most common case. It occurs when the is a receiv-ing company switching. For example company 1 is receiving andthere is a switching to company 2.

Those rules are implemented using a fuzzy system, which has asnput the sent flow and the error between sent and received flow.hat means the fuzzy system has a number of input equals to theumber n of the receiver bases plus one. Output of the fuzzy system

s a n + 1 vector, where each element assumes value [0, 1]. First nosition of the vector assumes value 1 when the correspondentase is operating. Output n + 1 inform that the system is not ableo find the receiver base. That is a valid diagnosis of a failure too. Aow meter failure or a leakage can cause it. The architecture of thisxpert system is showed in Fig. 4.

In order to built the fuzzy system sent flow and error betweenent and received flow are fuzzyfied using three memberships func-

ions. These represent non-dimensional value in the sense of low,

edium and high value. Medium and high value stand for condi-ions, which shall be monitored. Outputs use just two membershipunction. One close to zero and another close to one. Zero meanshere is no operation to be monitored and 1 for the existence of

Fig. 4. Expert system 1 architecture.

operation in progress. All the membership functions are triangular.A Mandami fuzzy system was chose. A total of six rules are used.Those rules are:

• if sent flow is high and (sent flow–received flow) is low then thebase is operating;

• if sent flow is high and (sent flow–received flow) is medium thenthe base is operating;

• if sent flow is high and (sent flow–received flow) is high then thebase is not operating;

• if sent flow is medium and (sent flow–received flow) is mediumthen the base is not operating;

• if sent flow is medium and (sent flow–received flow) is low thenthe base is operating;

• if sent flow is low then the base is not operating.

System was validated against actual data and diagnoses werevery precise in all situations. Fig. 5 show output from the system.Vertical axis is the system output. Output assumes integers values[0, 1, 2, 3, 4, 5, 6, 7]. Zero stands for no operation in progress. Out-puts from 1 to 6 indicate each existing base in operation. Output7 means that the system was not able to find any base in opera-tion although there is sent flow. As mentioned it can be a sensorfailure or a severe leakage. That situation was just simulated usingthe actual data since we did not have such event. Fuzzy systemcould model the system rules very well. Normal operation situa-

Fig. 5. Fuzzy system for Expert system 1 performance.

C.A. Laurentys et al. / Applied Soft Computing 11 (2011) 1057–1066 1061

stpas

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Fig. 6. Typical solution for a fault detection and diagnose system.

ory and DCS system. This option was taken in order to minimizehe number of interfaces the pipeline operator needs to access toerform his or her tasks. The important issue here is that the oper-tor is not able to deal with more than two computational systemsimultaneously.

. Building Expert system 2

This Expert system was defined in the sense of parity equa-ions [8,25]. Main idea is to observe the process and create theelationship between observed measurements and state space rep-esentation. In this process, due a fast response, it can be consideredll the time in a succession of steady state condition [20] for theipeline leakage detection problem. If necessary, when pipeline is

ong, a time delay shall be added to accommodate the transportime [17,23]. Once we have the measurements at each end thetrategy presented in Fig. 6[5,25] was used in order to formulatehe problem solution. Observing the process and revising the lit-

rature solution based on the difference between in and out flowrises. That difference can be seen in Fig. 7. First approach tried wasse fast Fourier transformer (FFT) to detect the step transition whenleakage starts. This could be useful in case of rupture of small

onnections like drains or vents. High order terms in a FFT could

Fig. 8. Error between sent and received flow and standar

Fig. 7. Error between sent and received flow with a leakage simulation of 1.4 timesstandard deviation of the error.

change in an amount that a detectable behavior could be modeled.That strategy failed due a filter effect. The only term that changesenough to be modeled for leakage detection is the first term of theFourier transformer. That term is the average value of the samples.Wavelets were used due the fact they can work better with shorttime transient [30]. It failed in the same way as FFT. Thus that Expertsystem is implemented based on an average value of the error. Maindefinition for it is the size of the window as stated in 3. That defi-nition took in account the heuristic that signal should be smoothlynot too much in order to keep a compromise with detection time.Applying this heuristic we get the average error as in Fig. 8. In thesame figure we can see the evolution of the standard deviation of

the error took in the same window as the average. The problem nowis just to set an absolute threshold value and timing to generate analarm [13] based on this error treatment. That was done based ona visual observation of the average and testing the chosen values

d deviation of the error with a leakage simulation.

1062 C.A. Laurentys et al. / Applied Soft Computing 11 (2011) 1057–1066

ystem

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(alarm point) ≥ 0 (5)(time to alarm) ≥ minimum time (6)(time to alarm) ≤ maximum time (7)

Fig. 9. Expert s

gainst validation data. The architecture of this Expert system isresented in Fig. 9. Real-time operation of this Expert system wille presented later.

. Building Expert system 3

This Expert system is an advance in relation to Expert system. It starts from the error averages and applies a fuzzy transforma-ion on it. That transformation improves the separation betweenlasses giving better visual information in a supervisory system.hat transformation starts from the idea we can ensemble thetatistic distribution using membership functions [12]. That mem-ership function has as parameter the standard deviation of therror. It defines the size of the membership function. For the stud-ed problem the idea is that error is a stochastic process and it hasverage equal zero when we are in a normal operation. When weave a leakage that average can assume values from a vicinity ofero, 0+, until the total flow. Unfortunately exists instrument error1,12,32] that prevents us to detect small changes close to zero. Thuse can detect leakages starting from one standard deviation of the

rror with confidence good enough to practical use in a leakageetection protection system. This system is in charge of stoppinghe operation when an abnormal condition is detected. Fuzzy sys-em was modeled using a Mandami model with three membershipsunctions. The first one covers the considered normal region with aidth of one standard deviation of the error. The second covers the

egion of two standard deviation of the error. The last one covershe remaining region. Resulting fuzzy error surface is presented inig. 10.

Fuzzified error average is presented in Fig. 11. This shows how

he transformations increase the distance between normal andbnormal region, even only in a visual effect. That visual effect givesetter information to support an operator decision in situations inhich the leakage is too small and the fuzzy error becomes erratic.

he same problem of adjust the Expert system to generate an alarm

Fig. 10. Fuzzy error surface.

2 architecture.

when the operation goes to an abnormal region remains. Althoughthe separation increases we need a threshold and a time window togenerate the alarm. Simple alarm like that is already a trick problemto be solved [12] once we have an overlapped region which leaveus to commit mistakes when generating a diagnose. Failure detec-tion system needs to deal with that in a better way than feel andadjust. The solution is transform this problem in an optimizationproblem which objective function is the number of right patternclassification. That function has as parameter the alarm thresholdand the time window (how many seconds to alarm). Better solu-tions were got from the solver formulated as a genetic algorithmic.The optimization problem is defined as following:

x∗ = argmaxx f (x∗) (1)defining

f (x) = num rights edist (2)where

num rights: number of right pattern classification:

dist = num rights − min rightsconstant

(3)

and min rights: % specified minimum rights pattern classificationspecified.

Subject to

(alarm point) ≤ 1 (4)

Fig. 11. Fuzzified error average.

C.A. Laurentys et al. / Applied Soft Computing 11 (2011) 1057–1066 1063

Table 1Comparison between two proposed searches: two parameters and three parameters search.

Alarm set Timing Number of patterns Right classification %Rigth classification %Nuisance alarms %Missing alarms

First strategy0.788 2 - 2197 94.9 5.5 4.10.795 4 - 2207 95.4 4.6 4.10.787 4 - 2199 95.1 3.7 5.80.782 3 - 2192 94.7 4.2 5.80.795 9 - 2217 96.1 1.2 6.7

Second strategy0.786 2 2 2199 95.0 5.2 2.8

94.8 6.6 1.994.8 5.9 2.695.3 0.9 7.699.2 1.6 0.0

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8. Building Expert systems 4 and 5

These Expert systems are classical in this application. They arebased on integration of the sent and received flow. One uses just

0.684 3 1 21940.712 2 1 21940.707 14 10 21950.606 25 13 2273

n improvement was gotten when the problem dimension wasncrease from two to three. The second approach, is based on aact that if we have two normal distributions of which the differ-nce is a single bias it is likelihood we can get any value from thatrocess for any distribution we have at a given time. Distributionsan be with or without leakage with overlapped regions as men-ioned before. However when the distance from the medium valueositively increases the likelihood the value belongs to a distri-ution with zero error diminished and the likelihood that valueelongs to a distribution with higher error increases. To improvehis classifier it is necessary to take the overlapped distributions inccount. Overlapped distribution compromise our classifier caus-ng classification error [31]. The proposed solution was stated as:nd a structure that maximize the likelihood of a decider to take aight classification of a pattern. That structure is derived from thesual one which takes a threshold alarm value and a time windowo generate the alarm. In the original structure there is a demandor all sample values violate the threshold for a time long enoughefore we generate an alarm. The likelihood of the values remainiolating the threshold too long is not high enough to assure theequired detection performance. New structure added the conceptf if we have a number of threshold violations inside a defined timeindow we can classify that pattern faster and as precise as before.etter than that we can keep the nuisance alarm low. Then, if weecrease the time window, we can keep the same required perfor-ance for the amount of leakage we can detect. Follow we present

he optimization problem definition in this way (first approach):

∗ = argmaxx f (x∗)

efining

(x) = num rights edist

herenum rights: number of right pattern classification:

ist = num rights − min rightsconstant

nd min rights: % specified minimum rights pattern classificationpecified.

Subject to

alarm point) ≤ 1

alarm point) ≥ 0

window to alarm) ≥ minimum window (8)window to alarm) ≤ maximum window (9)number of violations of the alarm point) ≥ 0 (10)

Fig. 12. Expert system 3 architecture.

(number of violations of the alarm point)

≤ window to alarm (11)These two problems were solved and Table 1 shows data to com-pare the first and second approaches described.

Both searches use the same data set, same limits for commonparameter and same minimum right pattern classification specifi-cation. It is demonstrated that second approach can achieve betterperformance than the first one. It was possible to reach almost a100% of rights classifications with only few nuisance alarms. Noticethat the 1.6% of the nuisance alarms means 0.8% of the total pat-terns to be classified. Expert system 3 architecture is presented inFig. 12. A comparative performance is presented later in this work.

Fig. 13. Expert system 4 and 5 architecture.

1064 C.A. Laurentys et al. / Applied Soft Computing 11 (2011) 1057–1066

Fig. 14. Expert system 6 output.

Fig. 16. Expert systems performance for base #1: (a) flow estimator, (b) Expe

Fig. 15. Expert system 6 architecture.

the direct measured flow and the other uses a flow after correctingthe effects of pressure and temperature. The residue generated isthe difference between sent and received totalizers. They are sen-sitivity to small leakages. Due that they are also sensitive to misscalibration between meters. Specially the totalizer based on thecorrected flow. Expert systems 4 and 5 architectures are presentedin Fig. 13. Comparative performance of them will be presented laterin this work.

9. Building Expert system 6

This Expert system is based on the fact that under constant flowthe pressure at the side of receiver base will change just as a func-

rt systems based on totalizers and (c) Expert systems based on average.

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C.A. Laurentys et al. / Applied S

ion of the tank level increment. That increment as a time functionan be interpreted as flow. The Expert system models use the pres-ure measurement as input and a neural multilayer perceptroneural network. A difficult with this Expert system is to find theumber of inputs we need to take to get a good estimation. That

nputs are the past values or regressors [17] that keep the informa-ion we are modeling. The pressure change is an huge issue becausehe changes are to small. In this application, if we wait too muchhe operation ends before we get any estimation. If we take few

amples the estimation will be poor. To build a real-time flow esti-ator it was necessary to accept the estimator sensitive to process

oise and leave to the operator the task to put it to operate as annput to the leakage detection system. Fig. 14 shows the outputf this Expert system. The estimated flow is smooth because the

Fig. 17. Expert systems performance for base #2: (a) flow estimator, (b) Expe

puting 11 (2011) 1057–1066 1065

increment of the tank level acts as a filter. Average error is 5% of thetotal flow. Expert system 6 architecture is presented in Fig. 15. Itscomparative performance will be presented later in this work.

10. Validation and real-time operation data

System validation was done in a real-time operation. It was donein two steps. First one is to put the system to operate and check it if

generates nuisance alarms. Second step involves simulating a leak-age and checking if the system is able to generate an alarm. Firststep lasted out two months. One adjust was detect as needed dur-ing this time. There is an error between the measurement of thesent and received flow meters. The mean of this error should be

rt systems based on totalizers and (c) Expert system based on average.

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qual zero once we have just a random process. However the errorean is different from zero. That requires from the system a param-

ter to compensate such difference. Another aspect of this errors that it changes during normal operations. That causes the sys-em to reduce its accuracy when supervising the operations. Afterdjusted system was tested simulating a leakage. Leakage is simu-ated as negative bias on received flow. That is assumed constantased on the fact that if we have a hole in a pipeline there will beflow through out a hole that is constant because the area and theifferential of pressure is constant [27]. Fig. 16 and Fig. 17 show theutput of the Expert systems.

The Expert system adjusted using the algorithm here proposeds shown in Fig. 17 presents performance better than that Expertystem adjusted in a usual way under changes in the measurementsrrors during normal operation. During the test it was unable torigger an alarm when adjusted with usual strategy. Its main advan-age is the fact this Expert system is more reliable when dealingith normal operations because it does not generate high number

f nuisance alarms while keeping low the probability of missingailure on detecting leakage.

1. Conclusion

Fault detection is growing in the industry due society demands.eeping facilities operating safety, reliable and economic is a hard

ask.The search of techniques that support the engineer team setting

roperly a supervisory system to monitor operations is quite a chal-enge. The methodology proposed in this article is a contribution tohis area.

Although applied for a specific problem, it can be properlyxtended to other fields with relative few changes.

The concept of ensembles or mixture of Expert systems are aich research field. The proposed and tested approach to classifyatterns in a stochastic process gives a better result than those inse and available nowadays in DCS and SCADA system by default.

The solution of increasing the dimension of the problem is oneption to be used as an aid in various industry fault detection prob-ems.

Since all measurements taken from the process are statistics, toerform any data treatment involves a confidence of the result. Theoncept of using an optimization technique to search the best adjustf the system is the answer for that. It can be applied whatever weeed to find a set of parameters as complex as the proposed here.

The flow estimated can be improved in this work using the con-ept of ensemble. Two or more neural networks with a differentumber of inputs can be used. This ensemble will give us estimatesith short period of time, good when the operation began, and with

onger period of time as the operation goes on. An aggregation algo-ithm should take care of the each estimator confidence selecting orstimating the final value based on the reliability of the estimation.

eferences

[1] W. Al-Rafai, R.J. Barnes, Underlying the performance of real-time software-based pipeline leak-detection systems, Pipes & Pipeline International(November–December, 1999) 44–51.

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Design of a pipeline leakage detection using expert system: A novel approachIntroductionPipeline process descriptionA short review on pipeline leakage detection systemEnsemble concept applied to the problem in an industrial installationBuilding Expert system 1Building Expert system 2Building Expert system 3Building Expert systems 4 and 5Building Expert system 6Validation and real-time operation dataConclusionReferences