SysDetLok - a leakage detection and localization system for water distribution networks ⁎

10th IFAC Symposium on Fault Detection, 10th IFAC Symposium on Fault Detection, Supervision and Safetyon for Technical Processes 10th IFAC Symposium Detection, Supervision and Safety forFault Technical Processes Available online at www.sciencedirect.com Warsaw, August 29-31, 2018 Supervision and Safety for Technical Processes 10th IFACPoland, Symposium on Fault Detection, Warsaw, Poland, August 29-31, 2018 Warsaw, Poland, Augustfor 29-31, 2018 Processes Supervision and Safety Technical Warsaw, Poland, August 29-31, 2018

ScienceDirect

IFAC PapersOnLine 51-24 (2018) 521–528

SysDetLok – SysDetLok SysDetLok – – a leakage detection and localization system SysDetLok – a leakage detection and localization a leakage detection and localization system system  for water distribution networks a leakage detection and localization system for water distribution networks for water distribution networks  for water distribution networks ∗∗∗ ∗ W. Moczulski ∗∗ J. Karwot ∗∗ ∗∗ R. Wyczółkowski ∗∗∗ D. Wachla ∗ ∗∗ R. Wyczółkowski ∗∗∗ D. Wachla ∗ W. Moczulski ∗∗ J. Karwot ∗∗ ∗∗∗∗ ∗ ∗ ∗ W. Moczulski J. Karwot R. Wyczółkowski K. Ciupke Pająk ∗ P. Przystałka ∗ ∗ D. Wachla ∗ D. K. P. Przystałka ∗Ciupke ∗ ∗∗∗∗ ∗ ∗ D. Pająk ∗ D. Wachla ∗ W. Moczulski J. Karwot R. Wyczółkowski K. Ciupke P. ∗∗ Przystałka D. Pająk ∗ K. Ciupke ∗ P. Przystałka ∗ D. Pająk ∗ ∗ ∗ Institute of Fundamentals of Machinery Design, Silesian University ∗ Institute of Fundamentals of Machinery Design, Silesian University Institute of Fundamentals of Machinery Design, Poland Silesian(e-mail: University of 18a Str., of Technology, Technology, 18a Konarskiego Konarskiego Str., Gliwice, Gliwice, ∗ Institute of Fundamentals of Machinery Design, Poland Silesian(e-mail: University of Technology, 18a Konarskiego Str., Gliwice, Poland (e-mail: [email protected]) [email protected]) ∗∗ of Technology, 18a Konarskiego Str., Gliwice, Poland [email protected]) Limited Liability Company, 62 (e-mail: Pod Lasem ∗∗ ∗∗ The Water and Sewage Limited Liability Company, ∗∗ The Water and Sewage [email protected]) The Water and Sewage Limited Liability Company, 62 62 Pod Pod Lasem Lasem Str., Rybnik, Poland (e-mail: [email protected]) ∗∗ ∗∗∗ Str., Rybnik, Poland (e-mail: [email protected]) The Water and Sewage Limited Liability Company, 62 Pod Lasem Str., Rybnik, Poland (e-mail: [email protected]) Institute of Production Engineering, Silesian University ∗∗∗ ∗∗∗ Institute of Production Engineering, Silesian University of of ∗∗∗ Str., Rybnik, Poland (e-mail: [email protected]) Institute of Production Engineering, Silesian University of Technology, 26 Roosevelta Str., Zabrze, Poland (e-mail: ∗∗∗ Technology, 26 Roosevelta Str., Zabrze, Poland (e-mail: Institute of Production Engineering, Silesian University Technology, 26 Roosevelta Str., Zabrze, Poland (e-mail: of [email protected]) [email protected]) Technology, 26 Roosevelta Str., Zabrze, Poland (e-mail: [email protected]) [email protected])

Abstract Abstract Abstract The paper is focused on the leakage detection and localization system for water distribution The paper is on the and system for water distribution Abstract The paper(WDNs). is focused focused onarchitecture the leakage leakage ofdetection detection andislocalization localization systemand for its water distribution networks The the system briefly presented most important networks (WDNs). The architecture of the system is briefly presented and its most important The paper is discussed focused onin the leakage detection and system for its water networks (WDNs). The architecture thesecond system islocalization briefly mostdistribution important modules are detail. In of the part of thepresented paper, a and case study concerning modules are discussed in detail. In the second part of the paper, a case study concerning networks (WDNs). architecture system isinbriefly most important modules are discussed in detail. In of thethesecond part of paper, a and caseits study applications of the The system to a real WDN located onethe ofpresented the southern cities inconcerning Poland is applications of the system to a located in one of the southern cities in Poland is modules discussed in detail. In WDN the has second paper, a in case study applications the version system to the a real real WDN located in of onethe ofsince the 2013 southern cities Poland is presented.are A of pilot of system beenpart running this city,inconcerning which still presented. A pilot version of the system has been running since 2013 in this city, which still applications the version system to the a real WDN located in as one ofsince thereasonable southern cities Poland is presented. A ofpilot of system has been running 2013 in this city,inwhich still shows a high level of performance in leak detection, well as efficiency for leak shows a high level version of performance in leak detection, as wellsince as reasonable efficiency for leak presented. A pilot of emphasizing the system been running 2013ofinthis this city, which still shows a high level performance in leakhas detection, as well as reasonable efficiency for leak location. The paperofends by the merits and limitations solution from the location. The paper by the merits limitations of solution the shows a high level ofends performance in leak detection, as well as reasonable for leak location. The paper ends by emphasizing emphasizing the merits and and limitations of this this efficiency solution from from the water and sewage company’s (WSC’s) perspective. water and sewage company’s (WSC’s) perspective. location. paper ends by emphasizing the merits and limitations of this solution from the water andThe sewage company’s (WSC’s) perspective. © 2018,and IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. water sewage company’s (WSC’s) perspective. Keywords: water distribution systems, leakage detection and localization, fault diagnosis Keywords: water distribution systems, leakage detection Keywords: water distribution systems, leakage detection and and localization, localization, fault fault diagnosis diagnosis systems, computational intelligence methods. systems, computational intelligence methods. Keywords: water distribution systems, leakage detection and localization, fault diagnosis systems, computational intelligence methods. systems, computational intelligence methods. 1. INTRODUCTION Recently, Recently, several several research research studies studies on on the the detection detection and and 1. INTRODUCTION INTRODUCTION 1. Recently, several research studies on thebeen detection and localization of leakages on WDNs have published. localization of leakages on WDNs have been published. 1. INTRODUCTION Recently, several research on thebeen detection and ofmany leakages on studies WDNs have published. Furthermore, detection and location methods have One of the most vital municipal services is providing water. localization many detection and location methods have One of of the the most most vital vital municipal municipal services services is is providing providing water. water. Furthermore, localization of leakages on WDNs have been published. Furthermore, many detection and location methods have found practical implementation (Puust et al. (2010); One Water supply systems must meet the demand resulting found practical implementation (Puust et al. (2010); Water supply systems must meet meet the isdemand demand resulting Furthermore, detection andof(Puust location methods have found implementation et al. (2010); One ofpublic, the most vital municipal services providing water. Geiger practical et al. many (2003)): analysis flow changes during Water supply systems must the resulting from commercial and industrial activities. When from public, commercial and industrial activities. When Geiger et (2003)): analysis of flow during practical implementation (Puust etetal. (2010); Geiger et al. al. (2003)): analysis of(Ferrante flow changes changes during Water supplysuch systems must meet the demand resulting night-time; step testing; transient al. (2007); from public, commercial and industrial activities. When found operational, systems are exposed to uncontrolled night-time; step(2003)): testing; analysis transientof(Ferrante et al. (2007); operational, such systemsand are exposed activities. to uncontrolled ety al. flow changes during night-time; step testing; transient (Ferrante et al. (2007); from public, commercial When Geiger V´ ıtkovsk` et al. (2007)) and frequency (Mpesha et operational, such systems areindustrial exposed to uncontrolled leakages of differing intensities and locations. Therefore, V´ ıtkovsk` y et al. (2007)) and frequency (Mpesha et al. al. leakages of differing intensities and locations. Therefore, night-time; step testing; transient (Ferrante et al. (2007); V´ ıtkovsk` y et al. (2007)) and frequency (Mpesha et al. operational, such systems are exposed to uncontrolled (2002)) analysis of pressure waves; correlation analysis of leakages of differing intensities and locations. Therefore, leak detection is an increasingly important issue in prac- (2002)) analysis of pressure waves; correlation analysis of leak detection is an increasingly important issue in pracV´ ıtkovsk` y et al. (2007)) and frequency (Mpesha et al. (2002)) analysis of pressure waves; correlation analysis of leakages of differing intensities and locations. Therefore, leakage noise; and inspection with a georadar. Unfortuleak detection is anbeincreasingly important issue in practice. Leakages can caused by natural processes of wear leakage noise; and inspection with a georadar. Unfortutice. Leakages can be caused by natural processes of wear analysis of pressure waves; of leakage noise;methods and inspection with correlation alimitations: georadar.analysis Unfortuleak iscorrosion anbeincreasingly important issue in nately, these have numerous the need tice. Leakages caused by natural processes of pracwear and detection tear, the can of inner and outer surfaces of (2002)) nately, these methods have numerous limitations: the need and tear, the corrosion of inner and outer surfaces of leakage noise; and inspection with a georadar. Unfortunately, these methods have numerous limitations: the need tice. Leakages can be caused by natural processes of wear to carry out experiments on the object; the requirement and the corrosion inner outerbysurfaces of pipes,tear, mechanical damageof to pipesand caused excessive carry out experiments the object; the requirement pipes,tear, mechanical damageof to to pipesand caused bysurfaces excessive nately, these haveon numerous limitations: the need to carry out methods experiments on the object; thehigh requirement and the corrosion inner outerby of to for the to significantly level pipes, mechanical damage pipes caused excessive loads, assembling errors, seasonal temperature changes, for the diagnoser diagnoser to have haveonaathe significantly high level of of loads, assembling errors, seasonal temperature changes, to carry out experiments object; the requirement for the diagnoser to haveautomation a significantly high leveland of pipes, mechanical damage tomaterial pipestemperature caused by excessive experience; the impossible of inspections; loads, assembling errors, seasonal changes, movements of the subsoil and defects in the pipes experience; the impossible automation of inspections; and movements of the subsoil and material defects in the pipes the diagnoser to haveautomation ameasuring significantly high leveland of experience; the impossible ofequipment. inspections; loads, errors, seasonal temperature changes, the need to apply specialized movements the subsoil and material defects in the pipes for (Puust assembling et al.of(2010)). Water loss depends on the technical the need to the apply specialized measuringofequipment. (Puust et al. (2010)). Water loss depends on the technical experience; impossible automation inspections; and the need to apply specialized measuring equipment. movements of the subsoil and material defects in the pipes (Puust et al. (2010)). Water on 3-7% the technical standards of the pipeline andloss candepends equate to of total Many works have been published on leak detection and standards of the the pipeline andloss candepends equate to to 3-7% of total total the need to apply measuring equipment. works have specialized been published on leak detection and (Puust et al. (2010)). oncountries the technical standards of pipeline equate 3-7% of water consumption in Water the and casecan of developed and Many works have beenmethods publishedforonWDNs leak detection and isolation (localization) (see Wu water consumption in the case of developed countries and Many isolation (localization) methods for WDNs (see e.g. e.g. and Wu standards of the pipeline can equate countries to countries 3-7% of total water consumption in the and case of developed and up to 50% of consumption in developing (Puust Many works have been published leak detection isolation (localization) methods foronWDNs (see e.g. Wu up to 50% of consumption in developing countries (Puust and Farley (2011); Puust et al. (2010); Kim et al. (2015); and Farley (2011); Puust et al. (2010); Kim et al. (2015); water consumption in the case of developed countries and up to (2010)). 50% of consumption in developing countries (Puust isolation et al. (localization) methods for WDNs (see e.g. Wu and Farley (2011); Puust et al. of (2010); Kim et al. (2015); Meseguer et al. (2015)). Some the methods were proet al. (2010)). et(2011); al. (2015)). Some of the methods were proup to (2010)). 50% of consumption in developing countries (Puust Meseguer et al. and Farley Puust et al. (2010); Kime.g. et al. (2015); Meseguer etdeveloped al. (2015)). Some of the (see methods were proposed and by the authors Moczulski Leakages not only waste water and energy, but can also andetdeveloped by Some the authors (see e.g. Moczulski et al. (2010)). Leakages not only waste water and energy, but can also posed al. (2015)). of the methods were proposed and developed by the(2013); authors (see e.g. Moczulski et al. (2016a); Wyczółkowski Ciupke (2016); PrzysLeakages notquality. only waste waterthere and energy, but can affect water Therefore, is a constant needalso to Meseguer et al. (2016a); Wyczółkowski (2013); Ciupke (2016); Przysaffect water quality. Therefore, there is a constant need to posed and developed by the authors (see e.g. Moczulski et al. (2016a); Wyczółkowski (2013); Ciupke (2016); PrzysLeakages not only waste water and energy, but can also tałka and Wyczółkowski (2011); Przystałka and Moczulski affect water quality. Therefore, there is a constant need to develop methodologies for detecting and localizing leak- tałka and Wyczółkowski (2011); Przystałka and Moczulski develop methodologies for detecting detecting isand and localizing leakal. (2016a); Wyczółkowski (2013); Ciupke (2016); Przystałka and Wyczółkowski (2011); Przystałka and Moczulski affect water quality. Therefore, a constant need to et (2012); Wachla et as well (Moczuldevelop methodologies for localizing leakages occurring in water supply there networks. (2012); Wachla et al. al. (2015)), (2015)), as Przystałka well as as patented patented (Moczulages occurring in water supply networks. and Wyczółkowski (2011); and Moczulski (2012); Wachla et al. (2015)), as well as patented (Moczuldevelop methodologies detecting and localizing leak- tałka ski et al. (2016b)) and deployed by industry. The essence ages occurring in water for supply networks. ski et al.Wachla (2016b)) and deployed by industry. The(Moczulessence  The research described in this paper has been partially carried (2012); et al. (2015)), as well as patented  ski et al. (2016b)) and deployed by industry. The essence ages in waterinsupply networks. of the idea is based on the discretization of the water Theoccurring research described this paper has been partially carried of the idea is based on the discretization of the water  outThe as part of the research in project conducted within the framework research described this paper has been partially carried ski the et al. (2016b)) and deployed by industry. The essence of idea is based on the discretization of the water supply system to predefined areas, then identifying an out as part of the research project conducted within the framework  supply system to predefined areas, then identifying an of The the Programme ofthis Innovation Economy, financed by the out as National part of the research in project conducted within the framework research described paper has been partially carried of the idea islocation based onwhere the discretization of occur. the water supply system to predefined areas, thenmight identifying an of the National Programme of Innovation Economy, financed by the approximate of a leakage approximate location of where a leakage might occur. European Fund. Thisproject publication was financed from statutory of the Programme of Innovation Economy, by the out as National partSocial of the research conducted withinfinanced the framework supply system to predefined thenmight identifying European Social Fund. This publication was financed from statutory approximate location of where areas, a leakage occur. an funds the Institute Fundamentals of Machinery Design at the European Social Fund.of This publication was financedfinanced from statutory of the of National Programme of Innovation Economy, by funds of the Institute of Fundamentals of Machinery Design at the approximate location of where a leakage might occur.

Silesian of Technology. funds of University the Institute of Fundamentals Design at the European Social Fund. This publication of wasMachinery financed from statutory Silesian University of Technology. Silesian of Technology. funds of University the Institute of Fundamentals of Machinery Design at the Silesian University of Technology. 2405-8963 © 2018 2018, IFAC IFAC (International Federation of Automatic Control) Copyright © 521 Hosting by Elsevier Ltd. All rights reserved. Copyright © under 2018 IFAC 521 Control. Peer review responsibility of International Federation of Automatic Copyright © 2018 IFAC 521 10.1016/j.ifacol.2018.09.626 Copyright © 2018 IFAC 521

IFAC SAFEPROCESS 2018 522 Warsaw, Poland, August 29-31, 2018

W. Moczulski et al. / IFAC PapersOnLine 51-24 (2018) 521–528

The presented paper describes a real leakage detection and localization system, developed by the authors. The system is implemented in a WSC in Poland, which supplies water to approximately 150,000 inhabitants, as well as companies and businesses, and is based on the proposed above mentioned methodology concerning the discretization of the water supply system to predeĄned areas. 2. ARCHITECTURE OF A LEAKAGE DIAGNOSIS SYSTEM The SysDetLok system is the result of work carried out by a group of researchers interested in the various methods involved in the operation of this system. The creators of the individual methods were also responsible for building and testing the respective part of the system that implements each method. In addition, because the system was created as a prototype, while the short time spent on its implementation was partly offset by research work on methods and software development work, it was decided to adopt the modular and open architecture of the SysDetLok system. The system consists of independently functioning modules communicating with each other via messages that are placed on and read from the system’s blackboard, which is implemented in PostgreSQL (Fig. 1). The synchronization module is responsible for the whole operation of the program and cyclical contact with the measurement database, as well as generating a message about the appearance of the new measurement in the database and the registration of individual modules and mediating the transfer of data from/to the blackboard. Thanks to this assumption, it was possible to easily exchange individual modules for new versions, along with making changes in the message system and the structure of the blackboard without any changes to the individual modules. Descriptions of the other modules are provided in the next part of this paper.

Figure 1. Modules of the SysDetLok system not). In addition, if a leak is detected, information about its approximated size is also recorded. This information is taken over by the leakage location module, which, from among the predefined zones by which the water supply network has previously been divided, indicates the likely leakage zones. The way of dividing the network into predefined zones is described by Wyczółkowski (2013). Leakage location is the basis for the operation of the M WIZ module, which prepares data for visualization through external SCADA and GIS systems. As there was a need to erase the system-generated alarms by the network operator, and SysDetLok had no communication interface, it was necessary to develop an additional interface to perform this operation. This was prepared as a webpage by which the operator cleared the alarm, while the corresponding module (M DEL WIZ) cleared the data for visualization in the GIS system and generated a signal, which the alarm ceased in the SCADA system.

In order not to burden the network operator with the use of additional tools, the built-in system did not have its own interface with which to indicate leakage locations; the alarms and visualization of the results were used by the company’s SCADA and GIS systems. Upon detection by the leak system, the corresponding information (warning or alarm) was generated and added to the SCADA alarm list. This resulted in the signalling of an event on the screen of this system. An additional benefit of this architecture is the solution to the data security problem. Authentication of system users, providing different levels of access to data is solved by the mechanisms implemented in the systems already in use in the company and preserves the already implemented level of rights for groups of users already defined. The operation algorithm for the SysDetLok system is presented in Fig. 2. The program works in a closed loop. At any given time, the synchronization module checks for new data to be processed and generates a corresponding message. The appearance of new data triggers two leak detection modules, working independently of each other. Their results are collected by the M KLAS DET module, which fuses the information and generates one final message about the degree of certainty regarding leak detection and the diagnostic signal (i.e., whether a leak is detected or 522

Figure 2. Diagram of the operation algorithm of the SysDetLok system 2.1 M DET IOM - leakage detection based on in/out models An information redundancy technique, corresponding to a model-based fault detection scheme, is proposed for

IFAC SAFEPROCESS 2018 Warsaw, Poland, August 29-31, 2018

W. Moczulski et al. / IFAC PapersOnLine 51-24 (2018) 521–528

the first leakage detection module. In this module, neural network-based non-linear autoregressive exogenous models are employed to map the relation between water flow rates, whereas classic parametric models are utilized to estimate uncertainties of neural models of water flows. Both types of models are then used for generating residuals and computing adaptive thresholds required for early and reliable leakage detection. Input-output neural models are created to represent dynamic behaviours of water flows at different locations in the WDN. It is assumed that flowmeters are installed at particular nodes of the network by means of the optimal placement method (Przystałka and Moczulski (2012)). A non-linear part of the models is obtained using a multilayer neural network with one hidden layer of neurons, for which the hyperbolic tangent activation functions are used. Neural models are trained using the LevenbergMarquardt algorithm and the simulation data related to a faultless state of the system. On the other hand, linear autoregressive models are applied for the approximation of adaptive thresholds. These models are developed using the least-squares technique. In this module, the value of the residuum is compared with adaptive threshold values representing the acceptable levels of leakage in the WDN. If the residuum is greater than the threshold, a diagnostic signal equal to 1 is returned by the module (in the other case, the diagnostic signal is equal to 0). It should be stressed here that a belief factor is computed for the diagnostic signal. In terms of the outcome, the diagnostic signal and corresponding belief factor are sent to the blackboard of the SysDetLok system. 2.2 M DET AR SVM - leakage detection based on SVM models The M DET AR SVM module was developed as the second tool in the SysDetLok system for supporting leakage detection tasks. Its operation is based on analytical redundancy, where the observed process variables and the analytical models of these variables are used for fault detection by means of analysis of the residuum signal. In the proposed solution, the support vector machine (SVM) method (see Vapnik (1995) and Sch¨ olkopf and Smola (2001)) was used to define adequate analytical models. Furthermore, it was assumed that: (a) the monitored WDN has only one inflow (in this case, any leakage in the WDN causes an increase in water flow at the WDN inflow where the level of water flow is equal to the level of the leakage); (b) the models are trained to represent the water flow at the main inflow to the WDN under normal conditions; and (c) the models have an autoregressive structure. The leakage detection procedure realized by the M DET AR SVM module consists of a number of steps. Firstly, an adequate data set from the database of the SysDetLok system is gathered. The features of this data set (e.g., a process variable specification, size of data) are defined by the structure of the models and by the size of the window for calculating the simple moving average of the residuum signal. At the second stage, the residuum signal is computed and the simple moving average is evaluated. Next, the estimated value of the residuum moving average is compared with a threshold value representing the acceptable level of leakage in the WDN. If the residuum moving average is greater than the threshold, the probability of 523

523

difference between the average of the residuum signal is equal to 0 (assumed for normal conditions of the WDN) and the actual average is computed by means of the tStudent statistic (Rice (2006)). Finally, the obtained diagnosis is sent to the blackboard of the SysDetLok system. 2.3 M KLAS DET - fusion of leakage detection results Having more than one classifier means there is a problem in combining their results. The combining process is also called classifier fusion. Fusing classifiers’ decisions can improve the performance of a leakage detection system. Many combination schemes have been proposed in the literature according to the type of information provided by each classifier, as well as their training and adaptation abilities. Some fusion methods operate on the classifiers, while others operate on their outputs (Ruta and Gabrys (2000)). We were able to distinguish three types of classifier (Bezdek et al. (2006)): • crisp classifier, • fuzzy classifier, • probabilistic classifier.

According to these types, different fusion methods are applied. In our case, two classifiers are used for the detection problem. The outputs are described with the degree of certainty. After an investigation process, it transpired that, in some cases, classification based on in/out models works better, while, in others, classification based on SVM models is more sensitive. Finally, then, the fusion of results is implemented in a simple way, which can be described as follows: if any of the classifiers detects a leakage, the alarm signal is indicated. The certainty degree of the signal is calculated as the maximum value of both calculated certainty degrees. 2.4 M LOK - leakage location based on hydraulic models and soft computing The following assumptions were made for the construction of the leak location module: • the leakage will be localized to the predefined area • the division of the entire network into areas will be made by analysing the sensitivity of individual sensors to the appearance of leakage at a particular location These assumptions were verified experimentally. During the experiment, at chosen locations, leakage at the specified intensity of 5m2 /h was simulated using a fire hydrant. The expected changes in flow, calculated with the district metering area (DMA) hydraulics model, were compared with changes detected in the measured flow signal. Fig. 3 shows DMA 5 with a partitioned view and leakage simulation locations. Table 1 shows the values of expected flow changes, while Fig. 4 shows the measured flow rate obtained from the st1 flowmeter during the experiment (red line), with flow prediction (blue line) and moments marked when changes were expected. As the experiment confirmed the assumptions, a leakage localization module was prepared.

IFAC SAFEPROCESS 2018 524 Warsaw, Poland, August 29-31, 2018

W. Moczulski et al. / IFAC PapersOnLine 51-24 (2018) 521–528

produces a similarity between the real condition and the virtual leakage in the following way:   n  1 pj =  [(P rij − P mij )2 ], n i=1 

pj =

1 , 1 + pj

(1)

(2)

where: p is theaverage distance of the data model and the actual data, p is the value of the similarity between the model and actual flows, P r is the flow rate measured by the actual flowmeter, P m is the flow rate predicted by the virtual flowmeter, i is an index of the flowmeter, j is an index of the area, and n is the total number of flowmeters. Areas with the higher value of similarity (2) are potentially considered to be the ones where the leakage could occur. The proposed method assumes that the leak is simulated at one node in the area. Thus, the number of emitters is equal to the number of control areas. If the areas are well isolated (they are highly distinguishable, even for small leaks), this assumption is correct because all places in the area are equally representative, which, in the same way, reveals the appearance of leaks anywhere in the area where the given flowmeter is installed. This method allows for very fast computations. This approach makes possible the location of individual leaks in the water supply network.

Figure 3. A DMA layout with measured points Table 1. Flow rate changes [m3 /h] expected during the experiment No.

predef. area no.

st1

st2

st6

st7

st11

st12

1 2 3 4 5

44 21 37 50 9

0 1.5 1.5 0.5 0

0 -1 -1 -1 0

2.5 0 0 0 0

0 0 0 2.5 0

0 -1.5 -1.5 1 0

0 0 5 0 0

. Figure 4. A sample flow rate measured during the experiment The first method is based on the flow data obtained from simulating the hydraulic model for virtual leaks localized in particular areas of the district (Tomasik (2012)). For each leakage, virtual flow values are calculated corresponding to the nodes where flowmeters are physically located in the network. The locations of virtual measurement nodes are also determined, based on the method proposed by Przystałka and Moczulski (2012). The size of a leakage is determined with the use of the hydraulic model. This is done by comparing the virtual flow values calculated for the input of the zone (it is calculated using the model for normal conditions), with the nominal values measured by the real flowmeter, which is installed on the supply pipe of the zone. In the next step, a comparison of the values of the virtual flow with the values of the real flow measurements 524

The second solution developed for leakage location and implemented in the M LOK module was described by Wachla et al. (2015). In this solution, the process of leakage location is divided into a set of simple tasks of leakage detection, where every single leakage detection task is conducted for a specified subarea of the considered WDN by classifying different signals between actual water flows at selected nodes of the WDN and water flows estimated using adequate analytical models. For this purpose, the procedure of leakage detection is composed of two steps. The first step involves the generation of residual signals using a SVM for the approximation of water flow, while the second involves the leakage location using neuro-fuzzy classifiers with residual signals as inputs. In particular, each classifier for leakage detection is associated with only one subarea of the WDN where the partition of the WDN into subareas is defined a priori by domain experts. The structure of the classifiers for leakage location is hierarchical and consists of two elements: the adaptive network-based fuzzy inference system ANFIS (MATLAB (2009)) and a simple binary classifier for comparing the output of ANFIS with a threshold value. The neurofuzzy system application, whose purpose is to evaluate the residuum signals according to the level of disturbances occurring in the WDN, is used to generate signals that are associated with two states: an occurrence of leakage in the associated subarea of the WDN (value 1) and a faultless state in this subarea (value 0). The output signal of the neuro-fuzzy system is a signal whose values vary continuously; therefore, in the structure of the proposed classifiers for leakage location, an additional binary classifier is applied in order to obtain an output with the adequate decision value.

IFAC SAFEPROCESS 2018 Warsaw, Poland, August 29-31, 2018

W. Moczulski et al. / IFAC PapersOnLine 51-24 (2018) 521–528

525

2.5 M WIZ - data preprocessing for visualization purposes When the system generates information about the detection and identification of the area where the leakage may be located, the system passes the identifiers of the relevant network segments to the three GIS tables, which were created previously and correspond to the successive fault locations (with the qualitative belief factor: very likely, likely, unlikely location). This was possible because each predefined differentiated spill area in the system database was assigned identifiers representing the network segments. The system operator, who is notified about the leak, must go to the GIS system and trigger a view. The screens of both systems showing example leakages are shown in Fig. 5. After resolving the problem, the operator must clear the alarm, which is correlated with the automatic clearing of the corresponding tables in the GIS system.

Figure 6. Layout of DMAs with measured points a school. For these customers, their specific water consumption patterns were determined. In special cases, when the consumption was particularly large and irregular, the measurement was carried out in the online mode. 3.2 Examples of leakage detection

Figure 5. The screens of the SCADA and GIS systems with leakage signalization 3. CASE STUDIES The SysDetLok system has been developed to work with big DMAs, that is, parts of an entire water distribution system, separated by measuring devices, for which the amount of water can be measured. 3.1 Description of diagnosed object The system has been implemented and tested for different DMAs, which supply water to various districts of the city. Basic information about these DMAs is summarized in Table 2. The layout of the DMAs is shown in Fig. 6. In this figure, the measurement points are marked as well. Table 2. Basic information on diagnosed DMAs DMA ID No. of residents No. of consumers No. of flow meters

2 ∼20,000 ∼3,000 18+3

5 ∼5,000 ∼1,300 11+1

14 ∼5,000 ∼900 11+1

In addition, in the middle of Zone 2, there is a pump station equipped with water tanks, which raises the level of water in order to supply higher-level buildings comprising multi-family housing. The estate also has an additional power supply, which is powered by water on two sides. The filling of tanks at the pump station is carried out at night, which greatly increases the flow of water throughout the zone. The remaining zones, which have a one-sided power supply, mainly comprise single-family houses. In each of the zones, there are industrial and non-typical customers, who use more water, such as a concrete plant, a bakery and 525

The model-based fault detection procedure of the leakage detection module (M DET IOM) was created as follows. For DMAs described above, input-output neural models were created to represent dynamic behaviours of water flows at different locations. As mentioned in the previous section, the flowmeters were installed at particular nodes of the network by means of the optimal placement method. Neural network-based, non-linear autoregressive exogenous models with one hidden layer (including neurons with the hyperbolic tangent activation function) were created. The mean absolute percentage error (MAPE) and the normalized root-mean-square error (NRMSE) were used to estimate the accuracy of each neural model. Moreover, the Bayesian information criterion (BIC) was used for the selection of neural model structures. It should be noted that the MAPE was less than 5% for each neural model. On the other hand, linear autoregressive models were successfully applied for the approximation of adaptive thresholds. These models were developed using the leastsquares technique (the mean-square error was acceptable for each model). The final results of this stage are presented in Table 3. Diagnostic signals were generated using residual signals, while adaptive thresholds were obtained by means of neural and linear autoregressive models. The parameters of the decision block (which is needed for the evaluation of residuals) were calculated using the threesigma rule. Next, the performance of the leak detection scheme was evaluated by applying false and true detection rates. Validation tests were carried out for 25 trials with a random leakage occurring at one point of the DMA. The average false detection rate was less than 1%, while the average true detection rate was greater than 97%. The implementation of the module was realized with the use of the prepared models and by taking into account the outcomes of the verification tests. The performance results of the M DET IOM module are presented in Figs. 7-9. The figure legend is as follows: the blue dashed line is used to visualize the flow measurements, the red line denotes the diagnostic signals and the solid yellow line is used to show the belief factor. As one can observe, a validation stage was

IFAC SAFEPROCESS 2018 526 Warsaw, Poland, August 29-31, 2018

W. Moczulski et al. / IFAC PapersOnLine 51-24 (2018) 521–528

Table 3. The accuracy evaluation of in/out models and error models

Figure 9. Leak detection results for DMA 14

carried out for almost three months. During this period of time, several leakages occurred. These leakages were detected by the module and hence the diagnostic signals, as well as values of the belief factor, which were sent to the blackboard of the SysDetLok system.

software (Rossman (2000)). This model was used to simulate operation of the DMA under faultless conditions and with instances of leakage uniformly distributed over the whole DMA. In particular, it was assumed that the number of leakage emissions at every single subarea would be equal to four. The first four emissions were the source of the training data, while the fourth emission was the source of the testing data. Due to the dynamic properties of the DMA and the requirements of the approximators of the water flow, one assumed that a simulation of the DMA operation would be four weeks with a time interval ∆t = 15 minutes. The response of the network was observed at nodes related to the locations of flowmeters installed at the real DMA. Furthermore, the DMA was partitioned into 23 subareas based on expert knowledge. The next step was to identify the approximators of the water flow at selected nodes of the DMA. In particular, a regression formulation of the SVM was applied (Sch¨ olkopf and Smola (2001)). The SVM models were trained for faultless conditions of the DMA using adequate simulation data. The linear structure of the identified SVM approximators with the following output and inputs was assumed: Qid (k) = f (Qid (k − ∆t), Qid (k − 664∆t)) , (3)

Figure 7. Leak detection results for DMA 2

where: Qid represents the water flow rate at a measurement point identified by node id in the hydraulic model of the DMA, ∆t is a sampling interval (15 min), n∆t is the time delay and k is the time point id. Twelve SVM approximators of the water flow at 12 specified nodes of the DMA were identified.

Figure 8. Leak detection results for DMA 5 3.3 Examples of leakage location The application of the soft-computing approach for leakage location was presented using DMA 14. Both the identification stage of the SVM approximators of the water flow through the selected nodes of the DMA and the process of training and testing the neuro-fuzzy classifiers for leakage location were described in detail. The data for training and testing water flow approximators and leakage classifiers were obtained using a hydraulic model of the considered DMA, which was developed using EPANET 526

Based on the identified SVM models and simulated data, including instances of leakage in each subarea of the DMA, the residual signals were calculated. They were used to form learning examples for training and testing neurofuzzy classifiers. In particular, the learning examples were created as follows. From each residuum, signal frames with 96 samples (one day) were extracted. Next, the extracted frames were connected together to form input signals for the neuro-fuzzy classifiers. Given the imbalance between the frames with regard to the considered leakages and the other frames, the first were repeated several times. A detailed description of the preparation of such learning examples was provided by Wachla et al. (2015). In the last stage, the process of training and testing classifiers for leakage location was initiated by establishing the initial structure and parameter values of the neuro-fuzzy models. This was realized using the subtractive clustering method (Chiu (1994)). In particular, a squash factor

IFAC SAFEPROCESS 2018 Warsaw, Poland, August 29-31, 2018

W. Moczulski et al. / IFAC PapersOnLine 51-24 (2018) 521–528

supplies water to approximately 150,000 inhabitants, as well as companies and businesses. One of the prominent parts of this work has been the SysDetLok system, which takes advantage of data acquired from GIS databases, the measurement database and the maintenance and servicing database. The hardware part of the system has been implemented in three separated zones of the water delivery system. Each zone has a different character, with one covered by individual houses and another dominated by multi-family buildings with many floors, which require additional technical systems to guarantee the necessary water pressure on the highest floors. Research and development work carried out within the last decade has allowed the WSC to draw conclusions from the models and simulations developed in the framework of related research and implementation projects.

Table 4. The confusion matrix of classifiers for leakage location in subareas of the WDN no.14 (part 1) Cl. ID

A1

A2

A3

A4

C1 1.0 0.0 0.99 0.01 C2 0.0 1.0 0.0 0.0 C3 1.0 0.0 1.0 0.22 C4 0.13 0.0 0.38 0.99 C5 0.0 0.0 0.0 0.0 C6 0.67 0.69 0.83 0.83 C7 0.0 0.0 0.0 0.0 C8 0.0 0.0 0.0 0.0 C9 0.0 0.0 0.0 0.0 C10 0.0 0.0 0.0 0.0 C11 0.0 0.0 0.0 0.0

Subarea ID A5 A6 A7 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0

0.88 0.0 0.93 0.15 0.0 0.97 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0

A8

A9 A10

0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0

Table 5. The confusion matrix of classifiers for leakage location in subareas of the WDN no.14 (part 2) Cl. ID

Subarea ID A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23

C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23

0.81 0.93 0.16 0.0 0.0 0.3 0.0 0.13 0.26 0.15 0.0 0.96 0.0

0.91 1.0 0.2 0.0 0.0 0.4 0.0 0.11 0.21 0.02 0.0 1.0 0.0

0.94 0.0 0.0 0.0 0.0 0.0 0.25 0.8 0.96 0.0 0.0 0.0 0.0 0.0 0.16 0.09 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0,99 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.02 0.0 0.0 0.94 0.98 1.0 0.67 0.0 0.0 0.0 0.0 0.64 1.0 0.59 0.0 0.0 0.01 0.04 0.03 1.0 1.0 1.0 0.83 0.0 0.0 0.03 0.03 0.0 0.0 0.0 0.99 0.88 0.0 0.0 0.0 0.0 0.0 0.0 0.13 1.0 0.0 0.0 0.0 0.0 0.02 0.0 0.0 0.0 0.08 0.0 0.0 0.0 0.0 0.0 0.95 0.0 0.0 0.0 0.0 0.17 0.96 0.13 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0

527

0.89 0.0 0.99 0.0 0.13 0.0 0.0 0.0 0.0 0.0 0.48 0.97 0.0 1.0 0.15 1.0 0.39 0.0 0.01 0.0 0.0 0.03 1.0 0.0 0.0 0.99

equal to 1.25, an accept ratio equal to 0.5, a reject ratio equal to 0.15 and Gaussian membership functions on each input were used. Finally, the structure of the neuro-fuzzy inference system was generated. It consisted of three rules, three membership functions on every single input and three linear membership functions on the outputs. Subsequently, the ANFIS (Jang (1993)) algorithm was used to train the neuro-fuzzy models. This algorithm (MATLAB (2009)) transforms a fuzzy model into a neural network and calculates the network’s weights using the backpropagation method and the gradient descent (Jang (1993)) method. Twenty-three classifiers were obtained, with their leakage detection ability in every single subarea of the WDN presented in the form of a confusion matrix (Tab. 4 and Tab. 5). The values in the confusion matrix vary from 0 to 1 and represent the leakage detection probability. The confusion matrix clearly shows that every classifier properly detects leakage occurring in the related subarea (the main diagonal of the confusion matrix). On the other hand, some are also more sensitive to leak occurrence in other subareas than their nominal one. For example, it is not possible to identify the right subareas A1, A3 and A6 with regard to leakage by means of classifier C1. For this reason, other classifiers must be considered. 4. DISCUSSION AND CONCLUSION In this paper, the complex methodology of designing and tuning a system for leakage detection and location in water supply systems has been presented. This methodology has been implemented in a WSC in Poland, which 527

The investments carried out in last five years have also allowed the WSC to modernize the water supply network over a range of about 100 km and develop systems to manage the water supply process for residents of the city. In addition to the standard replacement investments, the WSC has invested in a variety of measuring devices, sensors, servers and a teleinformatic infrastructure, which facilitate data acquisition and collection, as well as their further interpretation and subsequent decision-making with the highest probability of success. This activity has reduced the number of failures from 490 in 2012 to 190 in 2016 and reduced water losses from 8.52% in 2012 to 4.10% in 2016. The water supply system is constantly being rebuilt and the currently deployed intelligent integrated water management system, in a configuration elaborated at the end of the projects, needs to be reconfigured while awaiting a new calibration formula that includes all network rebuilds and additionally installed measurement and data transmission equipment. The analysis of collected historical and topical data will allow the system to perform self-learning and self-adjustment in order to take constantly changing flow directions into account, along with the ongoing process of pressure optimization. The undisputed advantage of implementing this system, together with the earlier development, and testing the water supply network models in selected locations is the change in the organizational culture of the company, which continues to run continuously, allowing the relevant data gathering services to develop alongside it. To sum up, the amount of data collected through the exploitation of this system has allowed the company to draw optimal conclusions and make decisions that anticipate the emergence of any critical situation, which it is strongly advised to avoid in order not to cause serious disruption to the water supply process in the area of operation of the enterprise. The experience we have gained has enabled us to continually develop the system and keep it operational at a technical level in order to meet the needs of the enterprise, for the benefit of the local community, in a reliable way by implementing the sustainability formula and, in particular, the smooth operation of the water supply system, which is currently expected from companies of this kind. Further work will be focused on reducing implementation costs and developing algorithms for self-learning and selftuning models that are used for the detection of leakages.

IFAC SAFEPROCESS 2018 528 Warsaw, Poland, August 29-31, 2018

W. Moczulski et al. / IFAC PapersOnLine 51-24 (2018) 521–528

Furthermore, more accurate methods for locating leaks will be sought. To improve data acquisition, new ideas related to the Internet of Things will be implemented, especially in the hardware part of the system. Finally, respective methods of big data analysis and processing will be developed and implemented. REFERENCES Bezdek, J.C., Keller, J., Krisnapuram, R., and Pal, N. (2006). Fuzzy models and algorithms for pattern recognition and image processing, volume 4. Springer Science & Business Media. Chiu, S. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems, 2(3). Ciupke, K. (2016). Leak detection using regression trees. In International Congress on Technical Diagnostic, 311– 321. Springer. Ferrante, M., Brunone, B., and Meniconi, S. (2007). Wavelets for the analysis of transient pressure signals for leak detection. Journal of hydraulic engineering, 133(11), 1274–1282. Geiger, G., Werner, T., Matko, D., et al. (2003). Leak detection and locating-a survey. In PSIG Annual Meeting. Pipeline Simulation Interest Group. Jang, J. (1993). Anfis: Adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. Kim, Y., Lee, S.J., Park, T., Lee, G., Suh, J.C., and Lee, J.M. (2015). Robust leakage detection and interval estimation of location in water distribution network. IFAC-PapersOnLine, 48(8), 1264 – 1269. doi: https://doi.org/10.1016/j.ifacol.2015.09.142. 9th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2015. MATLAB (2009). Fuzzy Logic Toolbox User’s Guide. The MathWorks, Inc. Meseguer, J., Mirats-Tur, J.M., Cembrano, G., and Puig, V. (2015). Model-based monitoring techniques for leakage localization in distribution water networks. Procedia Engineering, 119(Supplement C), 1399 – 1408. doi: https://doi.org/10.1016/j.proeng.2015.08.1000. Computing and Control for the Water Industry (CCWI2015) Sharing the best practice in water management. Moczulski, W., Wyczółkowski, R., Ciupke, K., Przystałka, P., Tomasik, P., and Wachla, D. (2016a). A methodology of leakage detection and location in water distribution networks - the case study. In 2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol), 331–336. doi:10.1109/SYSTOL.2016.7739772. Moczulski, W., Karwot, J., Wyczółkowski, R., Ciupke, K., Przystałka, P., Tomasik, P., and Wachla, D. (2016b). Detection and location of faults in closed water supply systems using devices measuring water flow in the network. Silesian University of Technology, Water and Sewage Limited Liability Company, patent pl no. 224 049 edition. Mpesha, W., Hanif Chaudhry, M., and Gassman, S.L. (2002). Leak detection in pipes by frequency response method using a step excitation. Journal of hydraulic research, 40(1), 55–62. Przystałka, P. and Moczulski, W. (2012). Optimal placement of sensors and actuators for leakage detection and 528

localization. IFAC Proceedings Volumes, 45(20), 666– 671. Przystałka, P. and Wyczółkowski, R. (2011). Detection of small leaks in water supply systems using the uncertainty modelling method (in polish). Measurements Automation Control, 57, 1307–1310. Puust, R., Kapelan, Z., Savic, D., and Koppel, T. (2010). A review of methods for leakage management in pipe networks. Urban Water Journal, 7(1), 25–45. Rice, J.A. (2006). Mathematical Statistics and Data Analysis. Duxbury Press. Rossman, L.A. (2000). EPANET 2 Users Manual. Ruta, D. and Gabrys, B. (2000). An overview of classifier fusion methods. Computing and Information systems, 7(1), 1–10. Sch¨olkopf, B. and Smola, A.J. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, USA. Tomasik, P. (2012). Hydraulic model-based method for leak location in urban water distribution networks (in polish). Diagnostyka, (1), 45–50. Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer-Verlag, New York. V´ıtkovsk` y, J.P., Lambert, M.F., Simpson, A.R., and Liggett, J.A. (2007). Experimental observation and analysis of inverse transients for pipeline leak detection. Journal of Water Resources Planning and Management, 133(6), 519–530. Wachla, D., Przystałka, P., and Moczulski, W. (2015). A method of leakage location in water distribution networks using artificial neuro-fuzzy system. IFAC-PapersOnLine, 48(21), 1216 – 1223. doi: https://doi.org/10.1016/j.ifacol.2015.09.692. 9th IFAC Symposium on Fault Detection, Supervision andSafety for Technical Processes SAFEPROCESS 2015. Wu, Z.Y. and Farley, M. (2011). Water Loss Reduction. Bentley Systems. Wyczółkowski, R. (2013). A methodology of a detection and localization of the water supply networks failures with using approximate models (in Polish). 493. Publishing House of Silesian University of Technology, Gliwice.