Fluid distribution network monitoring system and method

Abstract

The disclosure relates to a fluid distribution network monitoring system. A computer model of a physical fluid distribution network comprises a plurality of nodes linked by edges in a tree structure representing a pipe network of the fluid distribution network. One or more of the plurality of nodes are metered consumption nodes representing physical metering devices of the physical fluid distribution network. A measurement input is arranged to receive consumption measurements originating from automatically read meter readings from the metering devices and assigning the received consumption measurements to corresponding the metered consumption nodes. Each consumption measurement comprises a consumption representation and a corresponding timestamp. A fault detector is arranged to process the consumption measurements on the basis of the computer model to establish an indication of a network fault in the physical fluid distribution network. A status output is arranged to provide the determined indication of a network fault. A monitoring method, and computer programs arranged to carry out the method are also disclosed.

Description

FLUID DISTRIBUTION NETWORK MONITORING SYSTEM AND METHODField of the invention

[0001] The present invention relates to monitoring of fluid distribution networks.Background of the invention

[0002] Fluid distribution networks, understood as pipe networks distributing fluidssuch as water from points of production or storage to points of consumption, typicallycomprise metering devices at the point of production or storage to document the totalamount of fluid supplied to the network, and metering devices at each point ofconsumption for monitoring consumption or billing the consumers according to their consumption.

[0003] Fluid distribution networks may distribute many different types of fluids suchas water, pressurized gas, and other forms of chemicals. A fluid distribution networkmay operate at various scales and in various settings such as by distributing drinkablewater to commercial and residential consumers in a major city or distributing coolantfor industrial purposes at a factory.

[0004] An example of a fluid distribution networks is a water distribution network,understood as pipe networks distributing potable water from waterworks or waterstorage facilities to points of consumption, such as private houses or industrial orcommercial establishments. Water distribution networks typically comprise watermeters at the waterworks or other supply points to document the total water suppliedto the network, and water meters at each point of consumption for billing the consumers according to their consumption.

[0005] Minor leakages in the pipe network between a point of production or storageand the consumption points are typically only detected by visual inspection. Majorleakages may be indicated by the metering devices running wild, but locating theleakage requires visual inspection, sometimes throughout a huge geographical area.

[0006] Metering devices used for consumer billing are typically required byregulation to be calibrated at regular intervals to ensure correct billing. Although themetering devices are typically capable of automated remote reading for billingpurposes, the calibration may, on the other hand, be quite cumbersome and expensiveas it typically requires visiting the physical metering devices and possibly dismountingthem from the network, calibrating them, and then possibly mounting them again. Ifmetering devices are found to have an unacceptable measurement error larger than e.g.a maximum permissible error, then the metering device must either be replaced, or theerror must be eliminated such as via some form of adjustment of the metering device.Alternatively, it has been known to implement redundant metering devices or meteringdevice parts for mutual verification, also sometimes referred to as permanentperformance monitoring. Summary of the invention

[0007] The inventors have identified the above-mentioned problems and challengesrelated to fluid distribution networks and have developed the invention andembodiments described below in relation to detection and possible localization offaults in the fluid distribution network, such as leakages, and / or detection and possiblelocalization of metering devices that do not measure within the allowed tolerances.

[0008] In an aspect, the invention relates to a fluid distribution network monitoringsystem comprising: a computer model of a physical fluid distribution network, thecomputer model comprising a plurality of nodes linked by edges in a tree structure representing a pipe network of the fluid distribution network; one or more of said plurality of nodes being metered consumption nodes representing physical meteringdevices of the physical fluid distribution network; a measurement input arranged toreceive consumption measurements originating from automatically read meter readings from said metering devices and assigning the received consumption measurements to corresponding said metered consumption nodes; each consumption measurement comprising a consumption representation and a correspondingtimestamp; a fault detector arranged to process said consumption measurements on thebasis of said computer model to establish an indication of a network fault in saidphysical fluid distribution network; and a status output arranged to provide saiddetermined indication of a network fault.

[0009] In an aspect, the invention relates to a fluid distribution network monitoringmethod comprising: storing a computer model of a physical fluid distribution network,the computer model comprising a plurality of nodes linked by edges in a tree structurerepresenting a pipe network of the fluid distribution network; one or more of said plurality of nodes being metered consumption nodes representing physical meteringdevices of the physical fluid distribution network; receiving consumptionmeasurements originating from automatically read meter readings from said metering devices and assigning the received consumption measurements to corresponding said metered consumption nodes; each consumption measurement comprising aconsumption representation and a corresponding timestamp; processing saidconsumption measurements on the basis of said computer model to establish anindication of a network fault in said physical fluid distribution network; and providingsaid determined indication of a network fault.

[0010] A fluid distribution network monitoring system or fluid distribution networkmonitoring method according to the invention may in various embodiments make it possible to detect network faults such as leakage and / or consumption measurement errors in a fluid distribution network by using existing sensor infrastructure. Theexisting sensor infrastructure may be in the form of metering devices which mayalready be installed in the fluid distribution network before the fluid distribution network monitoring system disclosed by the invention is implemented. Utilizing meter readings from installed metering devices offers several advantages such as reduced cost compared to other monitoring systems, which may require that new or additional sensors be installed to cover the entire fluid distribution network.

[0011] A fluid distribution network monitoring system or fluid distribution networkmonitoring method according to the invention may in various embodiments merely provide an indication of the presence of network faults such as leakages and / or consumption measurement errors, wherein the indication represents a likelihood that a network fault is present in the fluid distribution network, but does not specify thetype, location or magnitude of the network fault. Still, it may often be advantageous that such an indication of a network fault is provided to trigger an investigation, compared to not detecting faults at all, or only detecting major leaks or erroneous metering devices due to discovery of significant damage.

[0012] A fluid distribution network monitoring system or fluid distribution networkmonitoring method according to the invention may in various embodiments be able to detect the existence of leakages, including smaller leakages, with greater certainty than hitherto feasible. According to various embodiments of the invention, the system may further determine estimated leakage locations of detected leakages. Thereby more reliable indication of leakages may be achieved sooner, enabling earlier and informed dispatch of response teams or service technicians to impede and repair the leakage. Sooner detection and localization of major leakages may save huge amounts of crucial fluid such as water or avoid huge spills of potentially harmful fluids, protecting the environment and saving costs. Detection and localization of minor leakages that would otherwise not be detected for, sometimes, years, may also save huge amounts of fluid, and enable early repair to avoid developing into major leakages.

[0013] A fluid distribution network monitoring system according to the invention orfluid distribution network monitoring method may in various embodiments be able to detect the existence of erroneous metering devices not measuring within allowed tolerances, i.e. having unjustified measurement errors. According to various embodiments of the invention, the system may further determine estimated errorlocations of detected erroneous metering devices. Thereby a permanent errormonitoring may be achieved, possibly reducing the need for physical visit and calibration to only such metering devices that have been indicated by the system as probable error causes. Furthermore, a measurement error of a metering devicedetermined by the present invention represents meter behavior in an actual use in anactual installation, e.g. influenced by actual temperatures, pressure and consumptionpattern, contrary to a calibration performed, e.g., at manufacture.

[0014] Generally, advantages may be obtained by the present invention for any fluiddistribution network having one or more parent node(s) with metering devices, suchas a main meter at the waterworks, or a zone meter for each geographic or administrative zone that the fluid distribution network is divided into, as well asdownstream metering devices at the leaf nodes, i.e. consumers, such as houses,commercial buildings, factories etc. Improved or further advantages and technical effects may generally be achieved by one or more of increasing the frequency of meter readings, increasing the accuracy of time synchronization between meters, and increasing the number of metered pipe forks, i.e. joints or nodes that have a metering device mounted to meter all downstream consumption.

[0015] A fluid distribution network monitoring system or fluid distribution networkmonitoring method according to the invention may in various embodiments be able to exploit predictable fluid consumption patterns of e.g. consumers and commercial establishments such as factories or restaurants. Fluid consumption may follow a 24-hour pattern which can be used to establish a typical consumption pattern for the endconsumer. A typical consumption pattern may describe average demand over a 24- hour period. Consumption patterns may be employed to identify when a significant deviation from these typical consumption patterns occurs. Consumption patterns may also be used to predict or estimate when fluid consumption is zero. According to various embodiments of the invention, the fault detector of the fluid distribution network monitoring system may use this information to simplify the computer model of the fluid distribution network and thereby confine the fault search to a smaller part of the fluid distribution network.

[0016] A fluid distribution network monitoring system or fluid distribution networkmonitoring method according to the invention may exploit the trend of the increasing frequency of availability of fluid consumption measurements from e.g. water metering devices. In the past, a water consumption measurement may only have been provided from a water metering device on e.g. a monthly or yearly basis. The trend is that water metering devices provide water consumption measurements at increasing frequency such as daily or hourly measurements. The increased measurement frequency of water metering devices or other types of metering devices may be exploited by the fluiddistribution network monitoring system to provide a faster identification of unjustified measurement error and / or leakages.

[0017] A fluid distribution network is understood as a physical system that suppliesand distributes fluid to various points such as points of consumption or storage. A fluid distribution network typically comprises distinct areas such as the main network which transports large amounts of fluid from points of production or storage to the branching network of pipes which further distributes fluid to e.g. its point of consumption. The part of the fluid distribution network that distributes fluid to the end consumer may often be considered as a sub-network of the fluid distribution network. The sub-network may often be described as having a tree structure with branching pathsconstituted by pipes supplying fluid to e.g. its point of consumption. The fluid distribution network typically comprises different interconnected components such as tanks, reservoirs, valves, pumps, and pipes. The fluid distribution network may also include sensor infrastructure in the form of metering devices which measure the quantity of fluid flowing through e.g. the pipes, an inlet or from a storage point such as a tank. A fluid distribution network may have one fluid inlet such as e.g. from a water treatment plant, or it may have multiple fluid inlets from different sources. A sub-network of a fluid distribution network can be understood as being a fluid distribution network in and of itself. Thus, the sub-network of a larger fluid distribution network is also a fluid distribution network.

[0018] A pipe network of the fluid distribution network is understood to be thenetwork of physical pipes through which fluid is transported and distributedthroughout the fluid distribution network. The pipe network may include pipes of many different diameters and pipes made from many different materials such as galvanized steel, stainless steel, iron, copper, and polyvinyl chloride.

[0019] A metering device is understood as a physical instrument for measuring thequantity of fluid passing through a pipe or other outlet. A metering device is a consumption meter that provides comparable consumption representations. Consumption representations are comparable because they obey an appropriate conservation principle such as e.g. conservation of mass, and are therefore suitable fordirect comparison such as between consumption representations provided by different metering devices. A consumption meter may provide a consumption representation given in physical units of e.g. normal cubic meter Nm3, joule J, or a volume in cubic meter m3or flow in liters per hour if the fluid is substantially incompressible. An example of a substantially incompressible fluid is water. A metering device may be mounted at many different points in the fluid distribution network such as in the main pipe, at the fluid inlet, in meter wells strategically positioned in the pipe network such as where the network branches, or in the pipe network of a residential or commercial building or a factory. A metering device may measure the fluid flow according to different measurement principles such as positive displacement or velocity. Metering devices using different technologies to measure the fluid flow are typically labeled accordingly, such as e.g. electromagnetic flow meter, multi-jet flow meter, turbine flow meter, or ultrasonic flow meter. The technology used or physical quantity measured by the metering device does not limit the functionality of the fluid distribution monitoring system or fluid distribution network monitoring method according to the present invention. Further, a fluid distribution network can have a mix of metering devices of several different models and even several different metering technologies, which can also be handled by the fluid distribution monitoring system or fluid distribution network monitoring method according to the invention. A metering device according to the present invention has some means by which a meter reading may be communicated to the fluid distribution network monitoring system. Communication means of the metering device may be in the form of a cable connection or wireless communication means whereby the metering device may comprise a wireless communication module.

[0020] A meter reading is understood to be a measurement of a physical quantity ofvolume or flow of fluid through a pipe or other outlet performed by a metering device in a fluid distribution network.

[0021] The meter reading may initially be represented by a continuous physicalquantity such as e.g. displacement, velocity or voltage, but is typically converted into a digital format in the form of a consumption measurement. The consumptionmeasurement is represented as binary digits represented by a number of bytes suitable for storage and processing on digital computer systems. The physical quantity denoted by the consumption measurement is termed the consumption representation of said consumption measurement. A consumption measurement may comprise several consumption representations, such as volume flow rate, accumulated volume, volume difference, etc. The consumption measurement additionally comprises a timestamp, preferably indicating the time at which the meter reading was performed. The consumption measurement may comprise several timestamps, such as measurement time, transmission time, etc.

[0022] A network fault is understood to be a physical fault in the fluid distributionnetwork which impedes the network’s ability to perform its function as related to the distribution of fluid to e.g. residential and commercial consumers. According to the present invention, several types of network faults are considered. A first type ofnetwork fault is leakage which is understood to be a fault which causes fluid to be lostto leaks in e.g. the pipe network of the fluid distribution network. A leakage may be caused by many different factors such as corroded, cracked, or ruptured pipes; brokenor loose pipe connections; or cracks in reservoirs or tanks. Fluid lost through a leakageis essentially wasted since it can typically not be used for its intended purpose. A related network fault is accidental or unintended fluid consumption, that technically is not due to a leak, but rather due to a human error or controller error of forgetting to close a valve or the like, such as a garden hose, swimming pool supply or a valve at a factory.

[0023] Another type of network fault considered by the present invention isunjustified measurement error in the meter readings provided by the metering devices in the fluid distribution network. Unjustified measurement error is an error in the meterreading which is numerically larger than a predetermined acceptable error level ortolerance such as a maximum permissible error defined in relevant regulations. Unjustified measurement error of one or more metering devices may cause consumers to be overbilled or underbilled for their consumption thus motivating a timely correction of such errors. Examples of overbilling or underbilling are seen in e.g. waterdistribution networks where water metering devices may be prone to measurement errors.

[0024] A fluid distribution network monitoring system or fluid distribution networkmonitoring method is understood as a computer-implemented system or method,respectively, for monitoring faults in a fluid distribution network. The fluiddistribution network monitoring system or fluid distribution network monitoring method of the present invention involves a computer model which maps the physical structure of the fluid distribution network onto a data representation in a computer such as a tree data structure. The data representation describes the physical fluid distribution network in terms of nodes and edges which describe the connections between nodes. The fluid distribution network monitoring system or fluid distribution network monitoring method receives data from metering devices located in the physical fluid distribution network via a measurement input, and is arranged to determine an indication of a network fault using a fault detector. An indication of network fault is presented via a status output which may be displayed on e.g. a screen, transferred to a backend system or stored in computer memory. The fluid distribution network monitoring system or fluid distribution network monitoring method may run on computer hardware which typically comprises at least a processor, memory, and communication means. The computer hardware on which the system or method isimplemented may be directly linked with metering devices such as via cables orwireless communication means such as Wi-Fi, or may receive consumption measurements through a meter reading system or other metering backend system. Communication means facilitate the transmission of meter readings from metering devices in the fluid distribution network. The fluid distribution network monitoring system or fluid distribution network monitoring method may alternatively be implemented on computer hardware which is remote with respect to the fluid distribution network. Remote indicates remoteness in terms of distance such as thousands of kilometers and remoteness in terms of the communication means by which meter readings are presented to the fluid distribution network monitoring systemor fluid distribution network monitoring method. Remote computer hardware may forexample comprise a cloud server on which the fluid distribution network monitoringsystem or fluid distribution network monitoring method is implemented and to which meter readings are transferred via the internet.

[0025] A computer model of a physical fluid distribution network is understood tobe a digital representation and mapping of the structure of the pipe network of a physical fluid distribution network. The digital representation is comprised of a plurality of nodes linked by edges in a tree structure. The tree structure may be implemented in a computer in different ways such as some form of linked data structure. The computer model may simplify certain structural aspects of the fluid distribution network to emphasize the most important aspects of the network structure and to simplify the processing of consumption measurements and the determination of network faults. Mapping the structure of the pipe network and metering devices to the computer model, requires that the structure of the fluid distribution network is known beforehand, such that the tree structure of the computer model can be established. The structure of the fluid distribution network may e.g. be derived from technical drawings or files or from an existing software program which provides the necessary details for building the computer model. The digital representation of the structure of the fluid distribution network may be established via e.g. manual input of data, or the process may be automated such as by importing the relevant data into the computer model and subsequently generating a suitable digital representation.

[0026] A tree structure is understood to be an abstract data type defining ahierarchical relationship between nodes connected via edges. The tree structure maythus define a network, and in the present invention is used to map the physical fluid distribution network onto a digital representation in a computer model. The treestructure contains a root node. All nodes in the tree structure, except for the root nodeat the root of the tree, have a parent node. Every node is connected by an edge from exactly one other node in the direction from parent node to child node. Each node can have an arbitrary number of child nodes but only one parent node. In cases where the structure of the fluid distribution network motivates a mapping of child nodes with multiple parent nodes, these multiple parent nodes may be combined as a single parent node. Nodes with no child nodes are called leaves or external nodes. Nodes may beassigned a level in the tree structure according to the number of edges between the node and the root node. The root node has level zero. It is emphasized that a computer model according to the invention may represent the tree structure in any suitable way, such as a simple list, as relational database records, as linked classes, as a dedicated tree structure data type, as an array, etc. The notation of tree structure, nodes and edges are here used for explanatory purposes, and may programmatically be implemented in any suitable way from which such a tree structure representation can be derived. It is noted that a physical fluid distribution network may have some nodes connected in a way that does not strictly resemble a tree structure. In such a system, those nodes may be considered together as a single node in the tree structure of the computer model, or the non-tree links between nodes may be disregarded, for example when physically closed, or be mapped into the computer model and processed by the fault detector as auxiliary data, for example associated with one of the tree structure nodes.

[0027] According to the present invention, the nodes may represent various locationsin the physical fluid distribution network. A node may e.g. represent the location of a metering device in the fluid distribution network. In such cases, the node is referred to as a metered consumption node indicating that it represents a point in the fluid distribution network at which consumption measurements are provided via meterreadings performed by a metering device. A node in the tree structure may alsorepresent a point at which a branch exists in the pipe network of the fluid distribution network. A node may thus e.g. be used to map a larger pipe from which multiple smaller pipes branch off. Sometimes a metering device will be located at such a location, for example in a meter well or meter box, thereby also making that node a metered consumption node. A node may also represent a termination of the fluid distribution network such as at a residential or commercial consumer or at any other point of consumption. Such nodes may typically also be metered consumption nodes.Edges of the tree structure denote how the nodes are connected. In terms of the fluiddistribution network, the edges may be thought of as representing the ways in which fluid may flow between different parts of the fluid distribution network. In some cases, edges may simply model a pipe and in other cases they may model multiple pipes. Theedges need not represent the length or geographic course of the physical pipes, but should preferably represent which nodes are connected by pipes.

[0028] A measurement input of the present invention is understood to be a softwareinterface through which consumption measurements may be presented to the fluiddistribution network monitoring system or fluid distribution network monitoringmethod for processing and possibly storage. The measurement input may perform the assignment of consumption measurements to the nodes of the computer model which comprise metered consumption nodes.

[0029] A fault detector is understood to be a computer-implemented method oralgorithm which processes the consumption measurements presented at themeasurement input. The fault detector combines the computer model of the physicalfluid distribution network with the consumption measurements to establish an indication of a network fault in the physical fluid distribution network. The faultdetector may use simple mathematical relationships between the consumptionmeasurements as established by the computer model, or it may use more advanced statistical methods for relating different consumption measurements with the established computer model of the fluid distribution network.

[0030] An indication of a network fault is understood to be a determination of thefault detector of the likely presence of a network fault in the physical fluid distribution network. An indication of a network fault may according to the present invention provide information at different levels of detail. The indication of a network fault may only provide the information that a network fault has been detected. The indication of a network fault may also include approximate geographical or spatial information about where in the fluid distribution network the network fault is located. The indication of a network fault may also specify the physical location of the network fault at a level of detail equivalent to that presented in the computer model. The indication of a network fault may also include information related to the estimated amount or severity of the network fault such as e.g. an estimated amount of water lost to a water leakage or an estimated amount of unjustified measurement error for a certain metering device. The indication of a network fault may also includeinformation related to the estimated duration of the network fault such as e.g. how long a water leakage has been present in a water distribution network.

[0031] The fault detector may discriminate between different types of network faultsby using one or more of several different strategies when comparing consumptionmeasurements from different metered consumption nodes. A method for discriminating between leakage and unjustified measurement error may involve notingthe magnitude of an inconsistency between consumption measurements. A leakagemay cause the loss of a massive amount of fluid which may be easily identified by a large discrepancy between consumption representations. A network fault in the form of a leakage may also be characterized in that the associated discrepancy between consumption representations is independent of the changes in volume or flow measured by the relevant metering devices. A leakage may often be assumed to be constant which may aid in the identification of the type of network fault encountered in the fluid distribution network.

[0032] A status output of the present invention is understood to be an interfacethrough which the indication of a network fault may be transferred or presented. Indication of network fault may e.g. be displayed on a screen as a message, it may trigger an alert in the form of an auditory warning or alert, it may be transferred to a backend system for storage or logging, it may be transferred to a smartphone as an alert notification, etc.

[0033] In an embodiment, said indication of a network fault comprises an indicationof leakage, representing a leakage in said fluid distribution network, and / or an indication of unjustified measurement error, representing an unjustified measurement error of one or more of said metering devices in said fluid distribution network.

[0034] The indication of a network fault may advantageously include informationabout the type of network fault detected or localized in the fluid distribution networkby the fluid distribution network monitoring system or fluid distribution networkmonitoring method. Information about the type of network fault is important when making decisions about what actions should be taken to mitigate or rectify the networkfault. An indication of leakage may for example require immediate attention whereasan indication of unjustified measurement error may be tolerable under certain circumstances or not require immediate action. In some embodiments, the indication of a network fault may simply indicate that a network fault exists without specifying a location whatsoever. This may be the case in e.g. water distribution networks with only a few water metering devices. In this situation, it is critical that information about the type of network fault is available since the appropriate resources must be allocated to locate the network fault depending on its type.

[0035] In an embodiment, said indication of a network fault comprises an indicationof magnitude of said network fault, such as a volume, flow or a percentage.

[0036] The fluid distribution network monitoring system or fluid distributionnetwork monitoring method may advantageously provide a magnitude of the network fault, which is understood to be a quantifiable measure of the error caused by the network fault. The measure may be formulated as e.g. the volume of water lost to a water leakage from the point in time at which the leak was detected by the fault detector, or the volume flow rate of the leakage. It may also be formulated as the absolute error in volume per unit time indicated by consumption measurements provided by a metering device with an unjustified measurement error, or for example as an error percentage or factor. Magnitude of the network fault indicates the severity of the error and may be used to decide how quickly the network fault must be repaired or mitigated. It may further be used to select an appropriate solution such as possibly disregarding meter readings from a metering device if redundant metering devices are available or applying a correction to the consumption measurements to account for or mitigate the unjustified measurement error.

[0037] In an embodiment, said indication of a network fault comprises an indicationof a location of said network fault, such as a geographical location with respect to said pipe network or a logical location with respect to said computer model.

[0038] The indication of a network fault may advantageously indicate the location ofthe network fault in the tree structure of the computer model or the physical distribution network.

[0039] Locations in the tree structure may be specified in terms of the nodes and theedges connecting the nodes. The indication of a network fault may thus specify a location which is confined to e.g. a single node, a single edge, multiple nodes, or multiple edges. The indication of a network fault may for example designate a subtree of the entire tree structure as the location of the network fault. Since the mapping between the computer model and the physical fluid distribution network may involve some simplification, confining the network fault to a single node or a single edge may provide a precise physical location, or it may constitute a wider geographical area in which the network fault may be located. The type of network fault also has a bearing on how precisely a geographical area may be defined. If the network fault is an unjustified measurement error and the offending metering device can be identified, then the location of the network fault can be exactly specified, provided the location of the metering device is known. A location may for example be specified in the form of a street address. Conversely, if the network fault is a leakage, it may for example only be possible to designate a location that is accurate to within for example the length of a pipe between two metering devices.

[0040] Specifying an exact or approximate geographical location of network faultsis advantageous in terms of dispatching the relevant personnel and tools, such as service technicians and excavators, to the correct site of the network fault. This avoids a time-consuming search and possibly expensive digging or measurements being performed at the wrong place instead of near or at the actual location of the network fault in the fluid distribution network.

[0041] In an embodiment, said fault detector is arranged to determine aninconsistency related to said metered consumption nodes.

[0042] In an embodiment, said fault detector is arranged to determine aninconsistency between two or more of said metered consumption nodes.

[0043] An inconsistency between two or more metered consumption nodes isunderstood as a state where the received consumption measurements do not match the computer model of the fluid distribution network. This indicates that something is wrong, and further analysis may lead to the kind of error. For example if a parent node’s consumption does not match the sum of consumption of its child nodes, it may be found that it is because one of more of the child node consumptions are erroneous due to unjustified measurement errors, or because an unmeasured leak accounts for the inconsistent consumption.

[0044] In an embodiment, said fault detector is arranged to determine aninconsistency between one or more of said metered consumption nodes and historical data.

[0045] An inconsistency between one or more metered consumption nodes andhistorical data is understood as a state where the received consumption measurements do not match a pattern of earlier consumption measurements, such as simple averages or more structured data taking into account variations in daily, weekly, monthly,seasons, etc., consumption. Deviation from historical data may indicate that somethingis wrong, and further analysis may lead to the kind of error. For example if a metered consumption node suddenly shows high consumption values during nights where it previously had substantially no consumption, it could be due to a leakage or an erroneous consumption meter device, but it could also simply be due to a changed consumption pattern of the consumer.

[0046] In an embodiment, said indication of a network fault is established byconsidering the physical principle of conservation of mass and / or mass flow rate.

[0047] Applying the principle of conservation of mass and / or mass flow rate to thefluid distribution network provides a method for relating the consumption measurements at different nodes. For substantially incompressible fluids, the mass conservation principle may be equally true for conservation of volume and / or volumetric flow rate. Thereby the principle can be applied to determine inconsistencies related to consumption measurements based on any of mass or volume, e.g. weighingmetering devices or volumetric flow rate metering devices. Due to the conservation principle, the flow into and out of a pipe must be equal in the absence of any branchingof the pipe network or loss of fluid. For example for an incompressible fluid, thevolume of fluid into and out of a pipe must be the same. For example, for a compressible gas the volume may be measured according to normal cubic meters which normalize the measured gas volume with respect to temperature and pressure such that gas consumption measurements become comparable and the flow measured in normal cubic meters into and out of a pipe must be the same. The tree structure of the computer model formalizes this by specifying that the measured fluid flow or volume into a node must be equal to the measured fluid flow or volume out of a node. Any violation of this principle may indicate a network fault. Thus, equations relatingthe flows or volumes into and out of nodes may be established using this principle, andany deviation from the principle can be detected at points at which the principle does not hold, and at which a network fault may consequently be identified.

[0048] In an embodiment, said metering devices of said physical fluid distributionnetwork are consumption meters.

[0049] Metering devices of the fluid distribution network may advantageously beconsumption meters which provide consumption representations that obey a principle of conservation such as e.g. conservation of mass. Consumption representations provided by different consumption meters must therefore be comparable in situations where the appropriate principle of conservation applies. The physical quantity provided in the consumption representation may be obtained via an appropriate form of normalization such as e.g. normalizing a measured gas volume to account for volume differences caused by variations in temperature and pressure. A consumption meter measuring a compressible gas may e.g. provide a consumption representation in the physical unit of normal cubic meters Nm3which provides a comparable volume normalized with respect to a standardized temperature and pressure value.Consumption meters may for example comprise water meters, heat meters, gas metersand fuel meters.

[0050] In an embodiment, said indication of a network fault is established byarranging said consumption measurements in a system of linear equations and providing a solution to said system of linear equations.

[0051] A system of linear equations may be established by considering a subtree ofthe tree structure defined in the computer model. At the level of the subtree, a number of nodes connected by edges may be given. If these nodes are metered consumption nodes, then a set of consumption measurements may be provided at a given time. Subsequently, an additional set of consumption measurements may be provided such that the two sets of measurements constitute a system of two linear equations. The system of linear equations may be extended with additional equations by considering additional measurements with consecutive timestamps. A solution to the system of linear equations may be provided by applying a suitable solution strategy or algorithmsuch as Gauss-Jordan elimination or any number of different numerical methods forobtaining an approximate solution such as ordinary least squares.

[0052] In an embodiment, said indication of a network fault is established by makinguse of zero-consumption measurements to simplify the computer model of the fluiddistribution network.

[0053] A zero-consumption measurement is understood to be a consumptionmeasurement which indicates that no amount of or a negligible amount of fluid isflowing through the particular part of the fluid distribution network which is meteredby the metering device providing the consumption measurement. A negligible amount of fluid is understood to be an amount of fluid which is smaller than a predetermined flow or volume of fluid that is considered so close to zero as to be negligible. A zero- consumption measurement may be used to simplify the equations obtained by relating the flows or volumes of fluid into and out of a node. This is done by eliminating the term associated with the metered consumption node to which the zero-consumption measurement is associated. Assumptions may in some cases need to be made about the presence or absence of an unjustified measurement error associated with the metering device which provided the zero-consumption measurement. For example, it may be possible that an unjustified measurement error is only present when asubstantial fluid flow or volume is measured, such that the absence of unjustified measurement error may be assumed when processing zero-consumption measurements.

[0054] In an embodiment, said fault detector is arranged to take into account apredetermined maximum permissible error MPE for said consumption representations.

[0055] According to various embodiments, not all inconsistencies or discrepanciesdetermined by the fault detector are necessarily representative of unacceptable error orleakage. In practice, metering devices cannot be adjusted to zero error, so they are typically calibrated before installation, and if necessary, adjusted to achieve a measurement error within a predetermined, acceptable range, also referred to as maximum permissible error, for example set by relevant regulation. As the metering devices and therefore the consumption measurements processed by the fault detectorthus inherently possess a variation of acceptable or justified errors, this may be takeninto account by the fault detector to only establish an indication of a network fault when a determined inconsistency or error is outside the maximum permissible error, or provide it indications of network faults together with a representation of an uncertainty derived from the maximum permissible errors of the involved metering devices.

[0056] In an embodiment, said indication of a network fault is established bydetermining an inconsistency between consumption measurements of a parent node and consumption measurements of one or more of its child nodes.

[0057] Indication of a network fault may be established by analyzing a part of thetree structure which includes only a parent node and one or more of its children.Equations or inequalities which relate the consumption measurements of the parent node to the consumption measurements of one or more child nodes may be established using e.g. the assumption of conservation of mass and / or mass flow rate. The indication of a network fault may on the basis of a solution to said equations or inequalities provide an indication of a location of the network fault which is confined to include only the parent node, the one or more child nodes, and the edges linkingsaid nodes. Confining the location of a network fault to include only a parent node and one or more of its child nodes may provide advantages related to assessing consequences for other parts of the fluid distribution network.

[0058] Considering only a parent node and its child nodes may provide advantagesin terms of calibrating metering devices in the fluid distribution network. In some embodiments, it may be the case that the parent node has been calibrated such thatmeasurement errors associated with the child nodes may be found by the fluiddistribution network monitoring system. The measurement errors will typically be associated with metered consumption nodes in the tree structure which represent a physical metering device in the fluid distribution network. Thus, traceability of the calibration of metering devices may be obtained for metering devices which have been installed in a fluid distribution network.

[0059] In an embodiment, an indication of leakage of said network fault is modeledby one or more of said nodes in said tree structure of said computer model.

[0060] The computer model may advantageously model the presence of a leak in thefluid distribution network by adding an additional node at the location in the tree structure corresponding to the physical leak location in the fluid distribution network. Since leakage corresponds to an unwanted flow of fluid out of the fluid distribution network, an additional node may be introduced which represents the opening through which the fluid is lost i.e. equivalent to modeling the addition of a pipe in the pipe network of the fluid distribution network through which fluid is lost at the location of the leak.

[0061] In an embodiment, the computer model comprises assumed leak nodes asadditional child nodes for all parent nodes in the tree structure, and then disregards assumed leak nodes that get calculated to substantially zero consumption. In an embodiment an assumed leak node is added to the computer model when unjustified measurements errors do not substantially explain inconsistency in the received consumption measurements.

[0062] In an embodiment, said one or more nodes in said tree structure modeling saidindication of leakage are introduced as variables in a system of linear equations with said consumption measurements and a solution to said system of linear equations is provided.

[0063] Nodes modeling an indication of leakage may advantageously be representedas variables in a system of linear equations obtained by arranging consumptionmeasurements as described previously. The variable may e.g. represent the magnitudeof the leakage such as the amount of water lost per unit of time or the total volume of water lost in a water distribution network. The system of linear equations may then be solved such that a solution which indicates the magnitude of leakage is obtained. The variables representing leakage may be introduced into a system of linear equations which has other variables representing e.g. unjustified measurement errors. In such anembodiment, a solution to the system of linear equations may simultaneously providea magnitude of unjustified measurement errors and a magnitude of leakage.

[0064] In an embodiment, said unjustified measurement error originating from oneor more said metered consumption nodes is known.

[0065] Various circumstances may lead to an advantageous situation where theunjustified measurement error of certain metered consumption nodes is known before any processing of the consumption measurements by the computer model. The situation may arise when a metering device has been inspected or tested by a technician to ensure that it functions correctly with no unjustified measurement error. The computer model can exploit information about known unjustified measurement errors for certain metered consumption nodes to simplify the processing required to obtain an indication of unjustified measurement error and leakage at other nodes in the model of the physical fluid distribution network.

[0066] In an embodiment, said unjustified measurement error originating from oneor more said metered consumption nodes is known to be zero.

[0067] The unjustified measurement error of some metered consumption nodes mayadvantageously be known by the computer model to be zero before any processing ofconsumption measurements takes place. This situation may e.g. be obtained when ametering device has recently been calibrated or otherwise checked to make sure that itprovides an appropriately accurate consumption measurement with no unjustifiedmeasurement error. A metered consumption node which is known to provide aconsumption measurement with a measurement error that is smaller than the unjustified measurement error can be used by the computer model to simplify the processing of consumption measurements, such that indications of unjustifiedmeasurement error or leakage at other nodes may be more easily obtained. This isespecially advantageous when the metered consumption node with no unjustified measurement error is the parent node of multiple child nodes, since this leads to fewer processing steps being required to obtain an indication of leakage and unjustified measurement error.

[0068] In an embodiment, said indication of a network fault may be obtained by saidcomputer model identifying consumption measurements which deviate by a predetermined amount from an established statistical measure of consumption measurements.

[0069] The computer model may advantageously process consumptionmeasurements to obtain different statistical measures of typical consumption patterns and employ these measures to identify when a deviation from these typical consumption patterns occurs. A typical consumption pattern may e.g. be that nightly water consumption for residential consumers is typically relatively low or zero. The average water consumption at night may for example be estimated, and a predetermined deviation from this average may be used in the computer model as an indication of a network fault. Statistical measures establishing a pattern of fluid consumption may be measured across different timespans such as hours, days, weeks, months or years. Corrections may be applied to statistical measures such as to account for differing fluid consumption patterns on weekdays, weekends, and public holidays.

[0070] In an embodiment, said consumption representation of said consumptionmeasurement is a flow representation.

[0071] A flow representation may be used to accurately disregard instantaneous zero-consumption measurements to simplify the network fault analysis. However, flowrepresentations require adequate time synchronization between the timestamps ofconsumption measurements from different metering devices. If the time synchronization of meter readings performed by different metering devices is not precise enough to reflect the dynamics of changing fluid flows, then consumption measurements from different metering devices cannot be readily compared for analysis without applying some form of correction to account for the time difference.

[0072] In an embodiment, said consumption representation of said consumptionmeasurement is a volume representation.

[0073] A volume representation provides less precise network fault detection ascompared to flow representations. However, the requirements for time synchronization for consumption measurements from different metering devices are not as strict due to the slower dynamics of volume measurements as compared to flow measurements.

[0074] In an embodiment, said consumption representation of said consumptionmeasurement has a predetermined measurement uncertainty.

[0075] The measurement uncertainty of a consumption representation may becontinuously estimated via a statistical measure such as a standard deviation by thefluid distribution network monitoring system or fluid distribution network monitoringmethod. The measurement uncertainty may also be estimated as a random variable andthus continuously be subtracted from the consumption representation. The estimation of measurement uncertainty may not necessitate an indication of a network fault if the measurement uncertainty does not exceed a predetermined measurement uncertainty such that it does not qualify as an unjustified measurement error.

[0076] In an embodiment, said consumption measurements of said meteredconsumption nodes provided by said meter readings from said one or more metering devices are synchronized in time to obtain compatible timestamps.

[0077] Ensuring an adequate level of time synchronization between consumptionmeasurements from different metering devices is a prerequisite for accuratelydisregarding certain meters from network fault analysis based on zero-consumptionmeasurements. Time synchronization accuracy is relatively more important when consumption representations are provided as flow representations than volume representations due to the difference in frequency content. Time synchronization is relevant between two or more metering devices for processing their consumption measurements together to determining inconsistencies between them, and / or between recent and previous consumption measurements from the same metering device, for processing its consumption measurements in relation to historical data to detect irregular consumption patterns.

[0078] In an embodiment, said metering devices at least occasionally perform meterreadings at substantially equal measurement times.

[0079] Metering devices may advantageously provide meter readings for example atfixed hours, or at fixed intervals. A plurality of metering devices in the fluid distribution network may advantageously provide consumption measurements according to the same measurement time schedule.

[0080] In an embodiment, the frequency at which metering devices perform meterreadings and / or provide consumption measurements is adjustable.

[0081] Metering devices may have an adjustable sampling frequency such that thetime interval between meter readings can be adjusted according to need. If for example the flow measured by a metering device can be assumed constant for a certain time interval, the sampling rate may be adjusted to match this assumption. Same considerations apply to situations where the flow may be assumed to be highly dynamic such that a sampling frequency is desirable.

[0082] In an embodiment, said metering devices comprise adjustable clocks.

[0083] Adjustable clocks are a prerequisite for being able to synchronize the clocksof different metering devices and thereby obtain a desired precision of timesynchronization across the metering devices of the fluid distribution network. Clock synchronization may be supplied locally to individual metering devices through for example an optical, NFC, or USB connection or via buttons. For metering devices with bidirectional communication capabilities, the clock synchronization may be performed via a synchronization signal from a time server or a master metering device.

[0084] In an embodiment, said fluid distribution network monitoring system or fluiddistribution network monitoring method is arranged to synchronize clocks of the metering devices.

[0085] The fluid distribution network monitoring system or fluid distributionnetwork monitoring method may advantageously be the supplier of a clock synchronization signal which is communicated to metering devices to establish the precision of time synchronization necessary for comparing consumption measurements from different metering devices.

[0086] In an embodiment, said fluid distribution network monitoring system or fluiddistribution network monitoring method is arranged to adjust timestamps in consumption measurements to compensate for unsynchronized clocks of metering devices.

[0087] If the meters cannot be synchronized, e.g. having non-adjustable clocks, itmay be possible to compensate if knowledge of the individual meters’ clocks can be obtained, e.g. by requesting and comparing time information from the individual meters, or by knowledge of the programmed transmission times compared to the actually experienced transmission times, or by transmissions including both measurement times and transmission times, and adjusting measurement times according to the difference between transmission time and reception time. Using suchknowledge at the receiver end to compensate timestamps and / or consumption values,the consumption measurement timestamps from different meters may become comparable.

[0088] In an embodiment, said metering devices provide said meter readings whichare synchronized in time to within an interval between 0.05 % and 2.5 % of nominal time between said meter readings.

[0089] The precision required by the time synchronization of different meteringdevices may advantageously be quantified by an interval such as between 0.05% and 2.5% of nominal time between meter readings.

[0090] In an embodiment, said pipe network of said fluid distribution network hasmultiple fluid inlet pipes.

[0091] The fluid distribution network monitoring system or fluid distributionnetwork monitoring method may advantageously be adapted to monitor a fluiddistribution network with multiple fluid inlets. The computer model of the fluid distribution network must be adapted to reflect multiple inlets such as e.g. by adding additional child nodes that represent the additional fluid inlets.

[0092] In an embodiment, said fluid distribution network is a water distributionnetwork, and said fluid distribution network monitoring system or fluid distribution network monitoring method is a water distribution network monitoring system or water distribution network monitoring method, respectively.

[0093] The fluid distribution network monitoring system or fluid distributionnetwork monitoring method of the present invention may be particularly advantageous for water distribution networks.

[0094] In an embodiment, said fluid distribution network distributes potable water.

[0095] In an embodiment, said metering devices are wirelessly connected to saidfluid distribution network monitoring system.

[0096] Metering devices with a wireless communication module confer severalbenefits such as reduced installation cost and flexibility in terms of install location. Metering devices may transmit consumption measurements and other data using automated meter reading AMR systems, for example using Wireless M-BUS orproprietary metering protocols, or using standard wireless technologies such as Wi-Fi, mobile cellular network 4G or 5G, LoRaWAN, Bluetooth or ZigBee. The network monitoring system or fluid distribution network monitoring method may receive the consumption measurements directly from the metering devices, through concentrators, or from a meter reading system backend. The fluid distribution network monitoring system or fluid distribution network monitoring method may in turn inquire or control the operation of the metering devices using the same wireless communication technology.

[0097] In an embodiment, said tree structure is implemented as a hierarchical treedata structure in said computer model representing said physical fluid distribution network.

[0098] The tree structure may advantageously be implemented as a hierarchical datastructure in the computer model of the fluid distribution network monitoring system or fluid distribution network monitoring method. The topmost element of the datastructure represents the root node and nodes of the tree may be accessed via indexing.The data structure may represent the relationship between nodes by using e.g. pointers to represent edges and relations between parent and child nodes. The data structure may also be searched efficiently such as when certain parts of the tree structure must be analyzed due to a detected inconsistency between two or more nodes.

[0099] In an embodiment, said tree structure is implemented as a table of values insaid computer model of said fluid distribution network.

[0100] The tree structure may have a simple implementation whereby the structureis implicitly inferred from a table of values in e.g. a file stored on the computer hardware on which the fluid distribution network monitoring system runs. The valuesmay be accessed and updated accordingly such as when consumption measurementsare provided by metering devices. Data included in the table may include information that specifies node identifiers in addition to the relationship between parent nodes and child nodes. The table may also be used to record the indication of network faults orquantities associated with network faults such as measurement errors associated with specific nodes or the magnitude of a leakage.

[0101] In an embodiment, said tree structure is implemented as a database in saidcomputer model of said fluid distribution network.

[0102] A database may contain data entries which specify the tree structure of thecomputer model. The database may contain entries which specify the relationship between parent nodes and child nodes in addition to data entries for consumption measurements. The database may also contain data entries related to the indication of network faults such as measurement errors and magnitude of leakages.

[0103] In an aspect, the invention relates to a computer program comprisinginstructions which, when the program is executed by a computer, cause the computer to carry out the fluid distribution network monitoring method of any of the above.

[0104] In an aspect, the invention relates to a computer program comprisinginstructions to cause the fluid distribution network monitoring system of any of theabove to execute the fluid distribution network monitoring method of any of the above.

[0105] In an aspect, the invention relates to a computer-readable data carrier havingstored thereon the computer program of any of the above.

[0106] In an aspect, the invention relates to a data carrier signal carrying thecomputer program of any of the above.The drawings

[0107] Various embodiments of the invention will in the following be described withreference to the drawings wherefig. 1 illustrates a fluid distribution network monitoring system implemented tomonitor a fluid distribution network,figs. 2a – 2c illustrate a part of a fluid distribution network modeled by a computermodel in the form of a tree structure,fig. 3 illustrates a mapping from the computer model of the fluid distribution networkmonitoring system to a part of the fluid distribution network wherein a network faultis present, andfigs. 4a and 4b illustrate the tree structure of the computer model.Detailed description

[0108] Fig. 1 illustrates an embodiment of a fluid distribution network monitoringsystem 100 implemented to monitor a fluid distribution network 210.

[0109] The fluid distribution network 210 illustrated on fig. 1 may represent severaldifferent types of fluid distribution networks 210. In a preferred embodiment, the fluiddistribution network 210 represents a water distribution network by which e.g. potablewater is distributed to various residential and commercial consumers. The fluiddistribution network 210 may also illustrate other types of networks which maydistribute other types of fluids to end consumers and serve other purposes. The fluiddistribution network 210 illustrated on fig. 1 may for example be a district heating orcooling system by which hot or cold water is distributed to consumers for the purposeof heating or cooling.

[0110] The fluid distribution network 210 is shown to have one main pipe 221through which fluid is distributed to four main branch pipes 222 of the pipe network220. The fluid flow 223 through the pipe network 220 is illustrated with arrows thatindicate the direction of fluid flow 223 through different parts of the pipe network 220.

[0111] Although only four main branch pipes 222 are shown, the main pipe 221 maydistribute fluid to additional branches of the pipe network 220 which are not illustratedon fig. 1. Two out of the four main branch pipes 222 are illustrated with dashed linesto indicate that the full extent of their further branching and termination is not shownon fig.1.

[0112] The smallest diameter pipes of the pipe network 220 are shown to terminateat consumers 280 to which the fluid distribution network 210 provides fluid. Varioustypes of consumers 280 are illustrated on fig. 1, namely residential consumers such ashouses and apartment buildings, and commercial consumers such as factories. Thetypes of consumers 280 illustrated on fig. 1 only constitute examples and do not preclude other types of consumers 280 from being supplied by the fluid distribution network 210.

[0113] At various points in the pipe network 220 of the fluid distribution network210, metering devices 240 are mounted to facilitate meter readings 241 (not shown)which are measurements of the fluid flow or fluid volume through that part of the pipenetwork 220. Metering devices 240 are shown to be mounted at various levels of thepipe network 220 such as at the main pipe 221, at main branch pipes 222 and at pipesdirectly connected to consumers 280. Metering devices 240 are shown to be distributedacross the fluid distribution network 210 to facilitate the discovery and localization ofnetwork faults 261.

[0114] A specific instance of a network fault 261 is illustrated on fig. 1 in the formof a leakage 262 caused by a rupture in the pipe network 220 at the main pipe 221. Anarrow indicates the direction of the flow of fluid 223 out of the pipe network 220 atthe location of the leakage 262.

[0115] Additional network faults 261 may exist in the form of unjustifiedmeasurement errors 263 (not shown) at any of the metering devices 240 in the fluiddistribution network 210. An unjustified measurement error 263 of a meter reading241, indicates that the metering device 240 measures the fluid flow or fluid volumewith an error that has a magnitude which is larger than a predetermined acceptablemeasurement error.

[0116] Fig. 1 indicates the physical location of the fluid distribution networkmonitoring system 100 as a box placed in the vicinity of the fluid distribution network 210. The box illustrates that the fluid distribution network monitoring system 100 isimplemented on computer hardware which is placed at a physical location within somedistance of the fluid distribution network 210. In other embodiments, the physicallocation of the fluid distribution network monitoring system 100 may be a great distance from the fluid distribution network i.e. the fluid distribution networkmonitoring system 100 may run on a remote server which may be located on a differentcontinent from the fluid distribution network 210, or it may be run on a cloudcomputing system.

[0117] A view of the functional structure of the fluid distribution network monitoringsystem 100 is illustrated on the bottom half of fig. 1. The computer model 110 of thefluid distribution network monitoring system has been created by mapping the physicalstructure of the pipe network 220 and the locations of the metering devices 240 onto atree structure 120. The tree structure 120 is comprised of nodes 130 which are linkedby edges 121 to reflect the structure of the pipe network 220.

[0118] The nodes 130 represent the locations of metering devices 240 in the fluiddistribution network 210. As an example, the mapping from a physical metering device240 to a node 130 of the computer model is indicated with an arrow for the meteringdevice 240 placed at the inlet of the main pipe 221. This metering device 240 is mappedto the root node 131 of the tree structure 120 since it is placed at the inlet of the mainpipe 221, i.e. the location at which fluid flows into the fluid distribution network. Theother nodes 130 in the computer model 110 are obtained according to the samemapping procedure whereby metering devices 240 are mapped to corresponding nodes130.

[0119] The root node 131 of the computer model 110 is shown to have four edges121 which model the four main branch pipes 222 that branch off from the main pipe221. Two of the edges 121 are illustrated using dashed lines to indicate that thestructure of that part of the fluid distribution network 210 is, for simplicity, notconsidered in this example. In a realization of the embodiment, the model shouldpreferably consider the entire physical network, or a part thereof relevant to amonitoring party. The edges 121 indicated with solid lines connect the root node 131with its two child nodes 133 which model the metering devices 240 placed at the twomain branches considered in the computer model 110. The root node 131 is thus alsoa parent node 132 with two child nodes 133. The remaining parts of the tree structure120 map out the remaining metering devices 240 and the remaining pipes of the pipenetwork 220. Since all fifteen nodes 130 in the tree structure 120 of the computermodel 110 represent a metering device 240, all fifteen nodes 130 of the tree structure120 may also be designated as metered consumption nodes 140.

[0120] Fig. 1 further illustrates an embodiment in which meter readings 241 (notshown) provided by metering devices 240 are wirelessly transmitted as consumptionmeasurements 141 to the fluid distribution network monitoring system 100. Wirelesstransmission may be via a conventional automated meter reading AMR system usingproprietary or standardized communication protocols such as Wireless M-BUS, andoptionally using intermediate concentrators, or any other form of e.g. serial or paralleldata transfer using technologies such as Wi-Fi, mobile cellular network such as 4G or5G, LoRaWAN, Bluetooth or ZigBee. In other embodiments, the transmission may bevia cables that connect each of the metering devices 240 to the fluid distributionnetwork monitoring system 100, possibly through meter reading systems, or viaproximity reading or drive-by reading, but such embodiments are not illustrated on fig.1.

[0121] Consumption measurements 141 are received at the measurement input 150of the fluid distribution network monitoring system 100. One or more consumptionmeasurements 141 may be received at the measurement input 150 before any processing is performed by the fluid distribution network monitoring system 100.

[0122] Each consumption measurement 141 received at the measurement input 150comprises a consumption representation 142 and a timestamp 143. The consumptionrepresentation 142 indicates e.g. a volume or flow measurement of fluid performed bythe metering device 240 which performed the meter reading 241 associated with theconsumption measurement 141. The volume or flow measurement of fluid may e.g. beone or more of an instantaneous fluid flow measurement, the accumulated total volumeof fluid with respect to a fixed time reference such as install date of the metering device240, or the accumulated total volume of fluid measured since the precedingconsumption measurement 141. In an embodiment, each consumption measurement141 may comprise several consumption representations 142, such as both a fluid flowand a fluid volume, or any other combination of consumption representations 142related to the consumption of fluid. The timestamp 143 preferably indicates the timeat which the meter reading 241 was performed, such that each consumptionrepresentation 142 is paired with an indication of the time at which the consumptionmeasurement 141 was performed. In certain embodiments, the timestamp 143indicates the time at which the meter reading 241 was transmitted, or estimated to bemeasured. In an embodiment, each consumption measurement 141 may comprise several timestamps 143, such as both measurement time and transmission time, or anyother combination of timestamps 143 related to the consumption.

[0123] The fault detector 160 of the fluid distribution network monitoring system100 processes the consumption measurements 141 received at the measurement input150 on the basis of the computer model 110 of the fluid distribution network 210. Thefault detector 160 may on the basis of received consumption measurements 141 andthe computer model 110 of the fluid distribution network determine an indication of anetwork fault 161 which may involve one or more of the fifteen different meteredconsumption nodes 140 represented in the tree structure 120. An indication of anetwork fault 161 may e.g. be identified for any one or more nodes 130 of the treestructure 120 and may involve the root node 130, which in this embodiment representsthe metering device 240 installed at the main pipe 221. An indication of a networkfault 161 may also be identified at one or more of the nodes 130 which represent themetering devices 240 installed at one or more of the, in this example, eleven consumers280 illustrated on fig.1.

[0124] Based on the processing of consumption measurements 141 from one or moremetered consumption nodes 140, the fault detector 160 may establish an indication of a network fault 161. An indication of a network fault 161 may simply indicate that anetwork fault 261 may be present in the fluid distribution network 210 and not disclosewhere the network fault 261 is located. An approximate or precise location in the treestructure 120 of the computer model 110 may also be supplied, and this in turn maybe used to identify e.g. a specific pipe or number of pipes in the pipe network 220 ofthe fluid distribution network 210 where the network fault 261, such as in the form ofa leakage 262, may be located.

[0125] Fig. 1 illustrates an example of a network fault 261 in the form of a ruptureof the main pipe 221 of the fluid distribution network 210. This network fault 261 mayresult in an indication of a network fault 161 being indicated by the fault detector 160between consumption measurements 141 associated with the root node 131 andconsumption measurements 141 associated with the nodes 130 representing themetering devices 240 of the main branch pipes 222 of the fluid distribution network210. The indication of a network fault 161 may in this embodiment simply be anindication that a network fault 261 has been detected somewhere in the fluiddistribution network 210, or it may be localized to e.g. several nodes 130 and edges121 of the tree structure 120.

[0126] Fig. 1 illustrates how the indication of a network fault 161 is provided to astatus output 170 for e.g. storage, logging, display, or transfer to another system suchas a backend system. The status output 170 thus provides an interface through whichthe information contained in the indication of a network fault 161 may be transferredto other systems, presented to stakeholders, or provide an alert such that action may beundertaken to fix or mitigate the network fault 261.

[0127] Figs. 2a – 2c each illustrate an identical fluid distribution network 210 subjectto different conditions which may affect the corresponding computer model 110 of thefluid distribution network monitoring system 100 and the processing of consumptionmeasurements 141. Examples of these embodiments are simplified for easierillustration and comparison between different scenarios, and the principles will alsoapply to larger and more complex real-world networks. The fluid distribution network210 illustrated on figs. 2a – 2c is a smaller part of the larger fluid distribution network210 illustrated on fig. 1, namely one of the four main branches which split off fromthe main pipe 221 illustrated on fig. 1. Fig. 2a illustrates a situation where all fiveconsumers 280 consume fluid such that fluid is flowing through the parts of the pipenetwork 220 that lead to the five consumers 280 connected to the fluid distributionnetwork 210. Fig. 2b illustrates a situation where three consumers 280 consume fluidand two consumers 280 do not consume any fluid. Fig. 2c illustrates a situation wherethree consumers 280 consume fluid, two consumers 280 do not consume fluid, and thefluid distribution network 210 has a network fault 261 in the form of a leakage 262caused by a rupture in the pipe network 220. The fluid distribution network 210illustrated on figs. 2a – 2c may in a preferred embodiment be a water distributionnetwork for distributing e.g. potable water or water for heating or cooling. Figs. 2a –2c may also illustrate fluid distribution networks 210 distributing other types of fluidsuch as liquefied natural gas LNG, liquefied petroleum gas LPG or liquid hydrogenbeing transported to e.g. points of storage or consumption.

[0128] Fig. 2a illustrates a fluid distribution network 210 and the corresponding treestructure 120 of the computer model 110 of a fluid distribution network monitoringsystem 100 (not shown). The fluid distribution network 210 has one main branch pipe222 and five smaller pipes that supply fluid to five consumers 280, all of which arepresently consuming fluid. The directions of the fluid flows in the pipe network 220are indicated by arrows 223. A computer model 110 corresponding to the fluiddistribution network 210 is illustrated with a tree structure 120 obtained from mappingthe pipe network 220 and metering devices 240 onto nodes and edges to obtain amodel. Each of the metering devices 240 are mapped onto a node 130, such that all sixnodes 130 of the tree structure 120 are metered consumption nodes 140. The resultingtree structure has one node 130 which is both root node 131 and parent node 132 offive child nodes 133.

[0129] Identifiers indicated on fig. 2a denote the different nodes 130, such that theroot node 131 is designated A0 and its five child nodes are designated A1, B1, C1, D1,and E1 respectively. For convenience, the node identifiers are also indicated on thecorresponding metering devices 240. Let ^^^, ^^^, ^^^, ^^^, ^^^ and ^^^ denoteconsumption representations 142 with approximately simultaneous timestamps 143denoted ^^, as measured by the six metering devices 240 of the fluid distributionnetwork. The consumption representations ^^^, ^^^, ^^^, ^^^, ^^^ and ^^^ maye.g. all represent fluid flow or fluid volume difference measurements. A fluid volumedifference measurement may e.g. be obtained by the metering device 240 subtractingtwo accumulated volume measurements from each and other thereby obtaining thevolume difference.

[0130] If consumption representations 142 do not all represent the same physicalquantity such as fluid flow or fluid volume, then an appropriate conversion must bemade such that all consumption representations 142 represent the same physicalquantity before consumption measurements 141, provided by different meteredconsumption nodes 140, are compared directly. Direct comparison betweenconsumption representations 142 may be in the form of e.g. mutual subtraction,addition or being provided as coefficients in equations. In some embodiments, severalconsumption representations 142 of different quantity types, e.g. both an instantaneous fluid flow and an accumulated fluid volume, may normally or upon request beprovided in each consumption measurement 141, so that the appropriate quantitycomparable to the other metered consumption nodes 140 can be selected.

[0131] The six consumption representations 142 having approximately simultaneoustimestamps 143 denoted ^^ means that a certain amount of variation betweentimestamps 143 is deemed allowable, such as in a predetermined interval from ^^ − Δ^to ^^ + Δ^ where Δ^ represents a predetermined negligible time difference. Apredetermined negligible variation Δ^ between timestamps 143 is thus deemed to notaffect the comparison of consumption representations 142 such that all consumptionmeasurements 141 may be assigned the approximately simultaneous timestamp of ^^.An assigned timestamp 143 of ^^ therefore reflects that the actual timestamp may besomewhere in an interval between ^^ − Δ^ and ^^ + Δ^. Variation between thetimestamps 143 of consumption measurements 141 associated with nodes 130 may in other embodiments be incorporated as an additional measurement uncertainty or errorwhich can be applied to estimated measurement errors to account for the lack of timesynchronization between consumption measurements 141.

[0132] The predetermined negligible time difference Δ^ may depend e.g. on thefrequency at which consumption measurements 141 are provided and the dynamics ofthe measured fluid flow or fluid volume difference. In an example, consumptionmeasurements 141 may be provided once per hour as volume differencerepresentations, and an acceptable negligible time difference Δ^ may be given as e.g.one minute. For fluid flow representations, the required predetermined negligible timedifference Δ^ may be much smaller such as e.g. a few seconds.

[0133] Assuming no measurement errors and applying the principle of conservationof mass or mass flow rate, the following relationship between the consumptionrepresentation ^^^ of the root node A0 and the consumption representations ^^^,^^^, ^^^, ^^^, and ^^^ of the child nodes A1, B1, C1, D1, and E1 can be describedvia an equation given by ^^^ = ^^^ + ^^^ + ^^^ + ^^^ + ^^^, i.e. theconsumption representation 142 of the metering device 240 associated with root nodeA0 must be equal to the sum of consumption representations 142 of the meteringdevice 240 associated with child nodes A1, B1, C1, D1, and E1. Such an equation maybe used together with the tree structure 120 to provide an indication of a network fault161 at e.g. one or more nodes 130.

[0134] A situation in which the principle of conservation of mass or mass flow rateis violated may be indicated by e.g. ^^^ ≠ ^^^ + ^^^ + ^^^ + ^^^ + ^^^ whichmay be caused by leakage 262 and / or measurement error. The fluid distributionnetwork monitoring system 100 (not shown) may in this instance provide an indication of a network fault 161 in the form of e.g. a warning that a network fault 261 (not shown) may be present in the fluid distribution network 210.

[0135] With reference to fig. 2a, it may be the case that the computer model 110maintains statistical measures of consumption measurements 141 associated with oneor more of the nodes 130 in the tree structure 120. A statistical measure may forexample be established by computing the average fluid volume or flow ^^^ at specifictime intervals across an entire day for node A1. The averages may e.g. be recorded ateach hour over a twenty-four hour period for a number of days, weeks or months toestablish a typical consumption pattern for the consumer 280. Deviation from theestablished average ^^^ by a predetermined absolute or relative amount of flow orvolume of fluid as recorded by subsequent consumption measurements 141, mayindicate that the metering device 240 associated with node A1 is causing a network fault 261 (not shown). Such statistical measures and / or consumption patterns may be used to identify network faults 261 which may be confined to an individual meteringdevice 240. In some cases, it may therefore not be necessary to compare consumptionmeasurements 141 from different metered consumption nodes in order to obtain an indication of a network fault 161.

[0136] Fig. 2b illustrates a fluid distribution network 210 equivalent to that of fig.2a. The tree structure 120 of the computer model 110 of a fluid distribution network monitoring system 100 (not shown) is also illustrated. The difference with respect tofig. 2a is that only three out of the five consumers 280 connected to the fluiddistribution network 210 consume fluid at the time of measurement. The remainingtwo consumers 280 do not consume any fluid, which is indicated by the absence ofarrows 223 on the pipes leading to the two consumers 280 in question.

[0137] The resulting tree structure 120 is identical to the one depicted on fig. 2a, withthe same nodes 130 and edges 121 indicating the structure of the fluid distributionnetwork 210. The same identifiers for nodes are used, such that A0 denotes the rootnode 131 and A1, B1, C1, D1, and E1 denote the child nodes 133. Let ^^^, ^^^, ^^^,^^^, ^^^, and ^^^ denote six consumption representations 142 with assignedtimestamps ^^as measured by the six metering devices 240 of the fluid distributionnetwork. The consumption representations 142 may represent either fluid flow or fluidvolume difference measurements.

[0138] Assuming no measurement errors and applying the principle of conservationof mass or mass flow rate, the following relationship between the consumptionrepresentation ^^^ of the root node 131 and the consumption representations ^^^,^^^, ^^^, ^^^ and ^^^ of the child nodes 133 can be described via an equation givenby ^^^ = ^^^ + ^^^ + ^^^i.e. the consumption representation 142of the metering device 240 associated with root node A0 must be equal to the sum ofconsumption representations 142 of the metering devices 240 associated with childnodes A1, B1, C1, D1, and E1, as with the example of fig. 2a above.

[0139] Because there is no fluid flow through two of the metering devices 240 in thisexample of fig. 2b, two of the corresponding consumption representationsand^^^ are zero-consumption measurements 141. Thus, the equation relating theconsumption representation ^^^ of the root node 131 and the consumptionrepresentations ^^^, ^^^, ^^^, ^^^and ^^^of the child nodes can be simplified as^^^ = ^^^ + ^^^ + ^^^. Zero-consumption measurements 141 thus allow for theelimination of terms from the equations which describe the relationship betweendifferent consumption representations 142. This in turn makes the identification of e.g.unjustified measurement errors 263 easier, since the unjustified measurement error263, due to the elimination of terms associated with certain metering devices 240, maynow be confined to fewer metering devices 240 and therefore be more easily identified.

[0140] The preceding mathematical examples regarding figs. 2a and 2b assume thatconsumption representations 142 are exact measurements of the fluid volumes or fluidflows in the pipe network 220, i.e. that there are no measurement errors. A morerealistic approach where measurement errors are present, can e.g. be modeled byintroducing an error factor which is multiplied by the consumption representation 142.The measurement error is thus assumed to be multiplicative in such a model, i.e. themeasurement error corresponds to a percentage of the fluid volume or fluid flow asrepresented by the consumption representation 142.

[0141] With respect to fig. 2b, four non-zero consumption representations ^^^, ^^^,^^^, and ^^^ associated with nodes A0, A1, C1, and D1 respectively were previouslydefined with an assigned timestamp 143 of ^^. If the metering device 240 at the mainbranch pipe 222 provides an exact meter reading 241 (not shown), the error factors ofthe remaining non-zero consumption representations 142 are given as ^^^, ^^^ and ^^^.Introducing the error factors to the model results in an equation ^^^ = ^^^ ⋅ ^^^ +^^^ ⋅ ^^^ + ^^^ ⋅ ^^^ relating the consumption representation 142 of the meteringdevice 240 associated with root node A0 to the sum of consumption representations142 of the metering devices 240 associated with child nodes A1, C1, and D1.

[0142] The measurement error can be computed from the error factor as thereciprocal of the error factor minus 1, such that e.g. an error factor ^^^ = 0.99corresponds to a measurement error associated with the consumption representation− 1 ≈ 0.01, i.e. the measurement error is approx. 1% of the measuredconsumption representation 142 in this example.

[0143] Since the error factor of each consumption representation 142 is initially anunknown, a number of equations at least equal to the number of error factors shouldideally be obtained to solve for the error factors and obtain the measurement errors.Multiple linear equations may be obtained by observing multiple sets of consumptionmeasurements 141 taken at different times. One set of consumption measurements 141may for example be defined by consumption representations ^^^, ^^^, ^^^ and ^^^with assigned timestamp ^^; equivalently indicated using the notation ^^^(^^),with the timestamp 143 in parenthesis indicating thetimestamp 143 associated with the given consumption representation 142. Given twoadditional sets of consumption measurements with assigned timestamps ^^ and ^^respectively, a system of three linear equations can be established as^^^(^^) = ^^^ ⋅ ^^^(^^) + ^^^ ⋅ ^^^(^^) + ^^^ ⋅ ^^^(^^)where the measurement errors and therefore the error factors are assumed to beconstant with respect to time. Note that with reference to fig. 2b, it is assumed in theabove example that ^^^(^^), ^^^(^^), ^^^(^^), ^^^(^^), ^^^(^^) and ^^^(^^) areall consumption representations 142 belonging to zero-consumption measurements141.

[0144] A set of consumption representations 142 representing either fluid flow orfluid volume difference according to the system of three linear equations may in anexample be given by^^^(^^) = 300, ^^^(^^) = 102, ^^^(^^) = 50, ^^^(^^) = 148.5^^^(^^) = 600,204, ^^^(^^) = 300, ^^^(^^) = 99^^^(^^) = 200, ^^^(^^) = 51, ^^^(^^) = 100, ^^^(^^) = 49.5from which the corresponding system of three linear equations can be established as:300 = ^^^ ⋅ 102148.5600 = ^^^ ⋅ 204300200 = ^^^ ⋅ 51 + ^^^ ⋅ 100 + ^^^ ⋅ 49.5The above system of three linear equations may be solved with respect to the error factors ^^^, ^^^and ^^^using e.g. Gauss-Jordan elimination to obtain a solution of^^^ = 0.9804, ^^^ = 1 and ^^^ = 1.0101. The corresponding measurement errors are^ therefore ^^^ − 1 ≈ 2%,^ ^^^ − 1 = 0% and^ ^^^ − 1 ≈ −1%, associated with the nodesA1, C1, and D1 respectively.

[0145] For the present example, the maximum permissible error may have beenindicated as interval between -1% and 1%. Upon computing the measurement errorsof nodes A1, C1 and D1, the fault detector 160 (not shown) must therefore provide anindication of a network fault 161 (not shown) to indicate that the metering device 240associated with node A1 has caused a network fault 261 (not shown) in the form of anunjustified measurement error 263 (not shown) of 2%.

[0146] Further, as the metering device 240 is a physical metering device, e.g. a watermeter, of a physical fluid distribution network, associated metadata or so-called masterdata, typically has a physical geographical location, such as a street address or GPScoordinates, and / or contact information for a relevant user or manager, or such may bederivable due to the mapping between the computer model and the physical fluiddistribution network. Thereby the indication of a network fault may be providedtogether with geographical location information or contact information, or data, e.g. ameter serial number, from which such can be derived.

[0147] In the preceding example, the consumption representations 142 of the parentnode A0 were assumed to be without measurement error. A measurement error of the parent node A0 may be considered by e.g. adding a predetermined measurement uncertainty to the measurement errors obtained by solving the system of linear equations. Measurement errors may thus be indicated in an interval which includes the measurement uncertainty attributed to the parent node.

[0148] With reference to fig. 2b, it may be the case in another example that a set ofconsumption representations 142 representing either fluid flow or fluid volume difference may be given by a set of three linear equations as 303 = ^^^ ⋅ 50 + ^^^ ⋅ 50 + ^^^ ⋅ 200606 = ^ ⋅ 200 300101from which the corresponding measurement errors may be found as 1% for each of thenodes A1, C1, and D1. The measurement errors being equal across consumptionrepresentations 142 associated with different metered consumption nodes, mayindicate that a wrong assumption has been made. In this example, a reasonable conclusion would be that the parent node A0 does in fact not have a measurement errorof zero, which is indicated by all the computed measurement errors being equal.

[0149] The measurement errors and consequently the error factors may vary as afunction of time, such that the error cannot be assumed to be constant for different setsof consumption measurements 141 with substantially different timestamps 143. Thesystem of linear equations may also be overdetermined with more equations thanunknowns, such that it may not be possible to find a unique solution when using e.g.Gauss-Jordan elimination to solve for the error factors. The method of ordinary leastsquares may for example be used to estimate the measurement errors when these arenot constant but perturbed by e.g. a small random variation as a function of time.

[0150] With reference to fig. 2b, ^ sets of consumption measurements 141 withconsumption representations ^^^(^^), ^^^(^^), ^^^(^^), ^^^(^^), …, ^^^(^^),^^^and assigned timestamps ^^, …, ^^ may be given forminga system of ^ linear equations of the form^^^(^^) = ^^^(^^) ⋅ ^^^(^^) + ^^^(^^) ⋅ ^^^(^^) + ^^^(^^) ⋅ ^^^(^^)⋮ ⋮ ⋮ ⋮^^^(^^) = ^^^(^^) ⋅ ^^^(^^) + ^^^(^^) ⋅ ^^^(^^) + ^^^(^^) ⋅ ^^^(^^)which may be solved for an estimate of the error factors ^^^, ^^^ and ^^^ using themethod of ordinary least squares. The method of ordinary least squares mayadvantageously be implemented by defining a ^ × 3 coefficient matrixand a 3 × 1 variable vectorand a ^ × 1 constant vector^ = [^^^(^^) ⋯ ^^^(^^)]^ from which the ordinary least squares solution may be obtained as ^= (^^^)^^^^^where ^^ is the transpose of coefficient matrix ^ and (^^^)^^ is the matrix inverse ofthe matrix product ^^^.

[0151] With reference to fig. 2b, it may be the case that the measurement errorcorresponding to ^^^has a mean value of 2%, the measurement error corresponding to^^^ has a mean value of -2%, and the measurement error corresponding to ^^^ has amean value of 2%. Each of the measurement errors may additionally be perturbed bya randomly varying error sampled from a uniform distribution in the interval from -0.5% to 0.5%. Ten sets of consumption measurements 141 representing either fluidflow or fluid volume difference and perturbed by measurement errors as describedabove may be given and represented in a 10 × 3 coefficient matrix as170 99.5 63.3é110 110ù ê12720.ú ê9 169 288290 60 8ú ê8.5252 246 26.3ú=ê ú ê169 4.74 55.8ú ê224 54.3 309ú ê118 408 50.1ú ê206 148 122ú ë173 157 38.8ûwith a 10 × 1 constant vector containing the consumption representations 142 of theparent node 330 é 344ù ê 474ú ê ú ê 432ú=ê 521ú ê 226ú ê 577ú ê 580ú ê 472ú ë368ûfrom which an estimate of the mean of the error factors ^ = [^^̂^ ^^̂^ ^^̂^]^maybe obtained using the method of ordinary least squares as^ = (^^^)^^^^^= [0.9787 1.019 0.9791]^from which an estimated mean of the measurement errors may be found as^ ^̂^^ − 1 =^ 2.18%, ^^̂^ − 1 = −1.87%,^ ^^̂^ − 1 = 2.14%. The estimated mean of themeasurement errors obtained using e.g. the method of ordinary least squares is valuable since it may take variations of the measurement error due to e.g. changes influid temperature or fluid flow into account. The measurement error may also beaffected by the installation conditions of the metering device in the fluid distributionnetwork, e.g. flow disturbance due to piping changes such as bends. The estimatedmean of the measurement error may thus be more representative than an estimate ofthe measurement error obtained during e.g. production of the metering device, as a measurement error obtained by the present invention represents meter behavior in an actual use in an actual installation.

[0152] Methods for estimating a varying measurement error may also be employedto track the measurement error over time such that a trend or drift of the measurement error can be identified. The method of ordinary least squares may e.g. be implementedon consecutive sets of consumption measurements 141 such as e.g. ten sets ofconsumption measurements at a time. This allows the fluid distribution network monitoring system 100 to provide updates of the estimated measurement error and possibly provide an indication of a network fault 161 if the measurement errorbecomes larger than a predetermined maximum permissible error.

[0153] Fig. 2c illustrates a fluid distribution network 210 equivalent in structure tothat of figs. 2a and 2b with the difference that the fluid distribution network 210 has anetwork fault 261 in the form of a leakage 262 caused by a rupture in the main branchpipe 222. Mapping of the fluid distribution network 210 onto a tree structure 120 ofthe computer model 110 is done equivalently to figs. 2a and 2b. The same node 130identifiers are indicated as on figs. 2a and 2b such that A0 denotes the root node 131and A1, B1, C1, D1, and E1 denote the child nodes 133. The leakage 262 has beenmodeled by adding an additional node L1 to the tree structure 120 of the computermodel 110.

[0154] As on fig. 2b, three of the consumers 280 consume fluid, and two consumers280 do not consume fluid. Let again ^^^, ^^^, ^^^, ^^^, ^^^ and ^^^ denoteconsumption representations 142 with assigned timestamps ^^ as measured by the sixmetering devices 240 of the fluid distribution network. Because there is no fluid flowthrough two of the metering devices 240, two of the corresponding consumption representations ^^^and ^^^are zero-consumption measurements 141.

[0155] Given assumptions of no measurement error and the conservation of massflow rate, it is clear that ^^^ ≠ ^^^ + ^^^ + ^^^ because of the leakage 262 seen atthe main pipe of the fluid distribution network 210. Rather, it is clear from fig. 2c that^^^ > ^^^ + ^^^ + ^^^ because the fluid lost to the leakage 262 is also included inthe consumption representation ^^^.

[0156] The inequality ^^^ > ^^^ + ^^^ + ^^^ additionally confines the leakage262 to a location in the pipe network 220 between the metering device 240 representedby root node A0 and the metering devices 240 represented by child nodes A1, B1, C1,D1, and E1. To compensate for the loss of fluid, an additional variable ^^^ is introducedto model the volume or flow of fluid lost through the leakage 262. An equationdescribing the relation between consumption representations ^^^, ^^^, ^^^, ^^^andthe leakage representation ^^^ is now given by ^^^ = ^^^ + ^^^ + ^^^ + ^^^.

[0157] Solving with respect to ^^^ yields ^^^ = ^^^ − (^^^ + ^^^ + ^^^), i.e. thevolume or flow of fluid lost to the leakage 262 can be obtained as the differencebetween the consumption representation 142 of the root node A0 and the sum ofconsumption representations 142 of the child nodes A1, C1, and D1. The presence ofa leakage 262 in the fluid distribution network 210 may thus be identified by notinginconsistencies in the expected mathematical relationships governing the fluidconsumption measurements 141 of multiple metering devices 240. Using such amethod, an approximate or precise location of a leakage 262 in addition to an estimateof the fluid volume or fluid flow lost to a leakage 262 may be identified.

[0158] With reference to fig. 2c, it may sometimes be unclear whether a leakage ispresent or not. In such an example, the presence of measurement errors may first beassumed and the relevant measurement errors may then be computed. If themeasurement errors are found to deviate uncharacteristically from a predeterminedinterval of reasonable and expected error values, it may be an indication that a leakageis present. A leakage may also be present when estimated measurement errors arefound to be substantially identical and negative, such as when the variation betweendifferent measurement errors is smaller than a predetermined value.

[0159] In the preceding example, the location and magnitude of the leakage 262could be established by simply subtracting relevant consumption representations 142from each other. A situation may arise in which the fluid distribution network 210 issubject to multiple types of network faults 261 at the same time. Fig. 2c may representa situation where in addition to a leak 262, the metering devices 240 represented by nodes A1 and D1 exhibit a measurement error in their respective consumption representations 142. The problem can be solved by observing multiple sets of consumption measurements 141.

[0160] If consumption representations 142 associated with node A0 are known to bewithout measurement error, four sets of consumption measurements 141 may establisha system of four linear equations from which the measurement errors and magnitude of leakage may be identified. The four sets of consumption measurements 141 mayhave assigned timestamps 143 given by ^^, ^^, ^^ and ^^ such that the system of fourlinear equations corresponding to the fluid distribution network 210 and tree structure120 of fig. 2c is obtained as^^^(^^) = ^^^ ⋅ ^^^(^^) + ^^^ ⋅ ^^^(^^) + ^^^ ⋅ ^^^(^^) + ^^^where the error factors ^^^, ^^^, ^^^ and magnitude of leakage ^^ are assumed to beconstant with respect to time. Note that with reference to fig. 2c, it is implicitlyassumed in the present example that ^^^(^^), ^^^(^^), ^^^(^^),^^^(^^), ^^^(^^) and ^^^(^^) are all zero-consumption measurements 141.

[0161] A set of consumption representations 142 according to the above system offour linear equations may in an example be given by^^^(^^) = 350, ^^^(^^) = 102, ^^^(^^) = 50, ^^^(^^) = 148.5^^^(^^) = 650,204, ^^^(^^) = 300, ^^^(^^) = 99^^^(^^) = 250, ^^^(^^) = 51, ^^^(^^) = 100, ^^^(^^) = 49.5^^^(^^) = 150, ^^^(^^) = 25.5, ^^^(^^) = 50, ^^^(^^) = 24.75from which the system of four linear equations can be established as 350 = ^^^ ⋅ 102148.5650 = ^^^ ⋅ 20430025010049.5150 = ^^^ ⋅ 25.524.75The above system of four linear equations may be solved with respect to the error factors ^^^, ^^^, ^^^and the leakage magnitude ^^^using e.g. Gauss-Jordanelimination to obtain a solution of ^^^ = 0.9804, ^^^ = 1, ^^^ = 1.0101 and ^^^ =^ 50. The corresponding measurement errors are therefore ^^^ − 1 ≈ 2%,^ ^^^ − 1 = 0%^ and ^^^ − 1 ≈ −1%, and the magnitude of the leakage has been found as ^^^ = 50.The present example illustrates that even if multiple types of network faults 261 arepresent in a fluid distribution network 210, the fluid distribution network monitoring system 100 may still be able to localize and quantify these errors.

[0162] The preceding examples regarding figs. 2a – 2c wherein sets of consumptionmeasurements 141 are used to establish a system of linear equations assumed the presence of zero-consumption measurements 141 at some of the nodes 130 in the tree structure 120. However, zero-consumption measurements are not required for a systemof linear equations to be established. With reference to fig. 2a, it may be the case thatfive sets of consumption measurements 141 with timestamps ^^, … , ^^ are given suchthat a system of five linear equations can be established as 450 = ^^^ ⋅ 102148.551.5750 = ^^^ ⋅ 204 300 103 + ^^^ ⋅ 49250 = ^^^ ⋅ 51 + ^^^ ⋅ 100 + ^^^ ⋅ 49.5 + ^^^ ⋅ 25.75 + ^^^ ⋅ 24.5475 = ^^^ ⋅ 204 + ^^^ ⋅ 100 + ^^^ ⋅ 99 + ^^^ ⋅ 25.75 + ^^^ ⋅ 49325 = ^^^ ⋅ 51 + ^^^ ⋅ 50 + ^^^ ⋅ 99 + ^^^ ⋅ 103 + ^^^ ⋅ 24.5from which the measurement errors can be found as 2%, 0%, -1%, 3%, and -2% for nodes A1, B1, C1, D1, and E1 respectively.

[0163] Figs. 2a – 2c have been used to illustrate different examples of how the fluiddistribution network monitoring system 100 may provide a numerical estimate or valueon the basis of specific consumption measurements 141 and the computer model 110.In some embodiments, it may not be possible to rely on these numerical estimatesalone, such as when the estimates are considered too uncertain according to apredetermined metric. An uncertain estimate of e.g. measurement errors or magnitudeof leakage may be used to take further action such as by validating the presence of anetwork fault 261 through visual inspection. Numerical estimates provided by the fluiddistribution network monitoring system 100 may thus be used to support and guidetrained personnel in their further identification and mitigation of network faults 261.

[0164] Fig. 3 illustrates an embodiment of the invention wherein a mapping is madefrom the digital computer model 110 of the fluid distribution network monitoringsystem 100 (not shown) to the physical fluid distribution network 210 to indicate thegeographical location of a network fault 261 in the form of leakage 262. The part ofthe tree structure 120 to be mapped is indicated as a hatched area. The hatched area onthe tree structure 120 corresponds to two edges 121, the first edge connecting node E2with node A3, and the second edge connecting node E2 with node B3.

[0165] The fluid distribution network 210 illustrated on fig. 3 comprises one mainpipe 221 and two main branch pipes 222 of the pipe network. Multiple consumers 280are connected to the fluid distribution network 210.

[0166] The fluid distribution network 210 illustrated on fig.3 may distribute differenttypes of fluids for different purposes such as e.g. potable water for consumption orcoolant used for cooling at a factory. The fluid flow 223 through the pipe network isindicated with arrows. In a preferred embodiment, the fluid distribution network 210of fig. 3 illustrates a water distribution network by which water is distributed to e.g.residential and commercial consumers 280.

[0167] According to the embodiment illustrated on fig. 3, the fault detector 160 (notshown) of the fluid distribution network monitoring system 100 has provided anindication of a network fault 161 which is confined to the two edges 121 as indicated. The two edges connecting node E2 with nodes A3 and B3 correspond to a part of thepipe network 220 of the physical fluid distribution network 210. The part of the pipenetwork 220 corresponding to the two edges is indicated as a hatched area of the pipenetwork 220 confined to the pipes between the metering device 240 represented bynode E2 and the two metering devices 240 represented by nodes A3 and B3. Themapping from a part of the tree structure 120 to a part of the pipe network 220 of thefluid distribution network 210 has been indicated using an arrow originating at thehatched area of the tree structure 120 and terminating at the hatched area of the pipenetwork 220.

[0168] This illustrates that the indication of a network fault 161 may in thisembodiment include an indication of leakage 162 which may disclose an approximatelocation of said leakage confined to the part of the pipe network 220 indicated with hatching on fig.3.

[0169] The location of the leakage 262 with respect to the physical location of thefluid distribution network 210 may be provided in various ways by the indication of anetwork fault 161. It may be that the indication of a network fault 161 includes locationcoordinates which delimit an area or identify a geographical location in which theleakage 262 may be located. Location coordinates may e.g. be defined according to astandard such as the Global Positioning System or be a list of possibly relevant streetnames or addresses, or the coordinates may be defined according to a local referencesuch as a coordinate frame defined with respect to the pipe network 220.

[0170] The location of the network fault 261 may also be established with referenceto the pipe network 220 itself, such as by indicating the specific pipes in the pipenetwork 220 or the parts of specific pipes which may be subject to e.g. a leakage 262.Such an indication of the location of a network fault 261 may rely on pipes in the pipenetwork 220 being identified in some way such as by a pipe identification number, andthe indication of a network fault 161 may in turn provide the relevant pipeidentification numbers via the status output 170 (not shown).

[0171] The indication of a network fault 161 may in addition to location coordinatesprovide a statistical estimate of the likelihood of the network fault 261 being confinedto the indicated location(s) such as by providing a covariance matrix indicating theuncertainties associated with the estimated location of a network fault 261.

[0172] Fig. 3 illustrates that an excavator 290 has been deployed in the vicinity ofthe part of the pipe network 220 that has been identified as being subject to a networkfault 261 in the form of leakage 262. The excavator 290 may constitute part of an effortto localize and subsequently repair the leakage 261 which has been indicated by thefluid distribution network monitoring system 100. The excavator 290 may in theembodiment illustrated on fig. 3 be used to dig into the ground to expose parts of thepipe network 220 which may be located beneath the ground. The excavator 290 may,by digging in the area designated by the indication of a network fault 161, expose theleakage 262 such that it can be identified by visual inspection. Trained personnel may begin the process of repairing the leakage 262 through means such as replacing thepipes of the relevant part of the pipe network 220 or repairing the existing pipe network220.

[0173] Fig. 3 illustrates an embodiment in which the search for a leakage 262 maybe confined to only include the location(s) as specified by the indication of a networkfault 161. The actual location of the leakage 262 may thus be identified much faster than by searching e.g. randomly such as when a leakage 262 in the fluid distributionnetwork 210 is suspected but no estimate of the location is known. Without anapproximate leakage 262 location as provided by the fluid distribution network monitoring system, trained personnel would at first have to perform leakage localization in a greater part of the fluid distribution network, such as by usingmicrophones for detection, which may further delay efforts to repair the leakagesignificantly. Depending on the physical network layout, its complexity and distancesbetween metering devices in the fluid distribution network, the approximate leakagelocation provided by the invention may be sufficient to lead more or less directly to asuspected leakage, but even when only giving an approximate location of, for example,a certain residential area, it may significantly reduce the subsequent required effort,such as significantly reducing the area in which to apply microphone-based leakage localization.

[0174] Figs. 4a and 4b illustrate the tree structure 120 of the computer model 110which models the fluid distribution network 210 illustrated on fig. 3. All fifteen nodes130 of the tree structure 120 are metered consumption nodes representing the meteringdevices 240 of the fluid distribution network 210 illustrated on fig. 3. All nodes 130of the tree structure have been designated with a unique identifier as indicated on figs.4a and 4b. The root node 131 of the tree structure has been given the identifier A0 andmodels the metering device 240 (not shown) mounted on the main pipe 221 (notshown) of pipe network 220 (not shown) illustrated on fig. 3.

[0175] Fig. 4a illustrates the hierarchical arrangement of the tree structure 120. Theroot node A0 has no parent node and forms the top-most node in the hierarchy of thetree structure 120. The root node A0 may also be said to constitute level zero of thetree structure 120. Root node A0 is the parent node 132 to child nodes A1 and B1which in turn constitute level one of the tree structure 120. Parent node A1 has childnodes A2, B2, C2, D2, E2 and parent node B1 has child nodes F2, G2, H2, I2, J2. Theten nodes A2, B2, C2, D2, E2, F2, G2, H2, I2, and J2 constitute level two in thehierarchy of the tree structure 120. Node E2 is a parent node with child nodes A3 andB3. Child nodes A3 and B3 constitute the fourth and final level of the tree structure120. The tree structure 120 illustrated on fig. 4a thus has four levels, each of which areconstituted by a subset of nodes 130. The edges 121 indicate how nodes 130 of different levels are connected.

[0176] With reference to fig. 4a, it may be the case that for each meteredconsumption node 140, a consumption measurement 141 (not shown) is given. Theconsumption measurements 141 may be synchronized in time to within an allowabletolerance which allows for consumption representations 142 (not shown) associatedwith different metered consumption nodes 140 to be compared. Processing of saidconsumption measurements 141 on the basis of the computer model 110 maypotentially establish an indication of a network fault 161 (not shown). The comparisonof consumption representations 142 from multiple metered consumption nodes 140may be done using different strategies for effective traversal of the hierarchy of thetree structure 120. The tree structure 120 traversal strategy may depend on factors suchas how the computer model 110 is implemented, the complexity of the tree structure120, the average number of child nodes 133 per parent node 132, and the type ofnetwork fault 261 (not shown) being identified.

[0177] Tree traversal may define a strategy by which each node 130 in the treestructure 120 can be accessed and the consumption measurement(s) 141 and / or otherdata associated with the specific node 130 can be written or read. Reading data refersto the process of accessing the data associated with a given node 130 such as by reading data entries stored in memory of a digital computer system on which the fluiddistribution network monitoring system 100 (not shown) has been implemented.Writing data may involve tree structure 120 traversal steps by which relevant data suchas consumption measurements 141 are assigned to the appropriate meteredconsumption nodes 140. Tree traversal is thus a part of the necessary processing ofconsumption measurements 141 performed by the fluid distribution networkmonitoring system 100.

[0178] With reference to fig. 4a, a tree structure 120 traversal strategy which may beemployed by the computer model 110 is a level-order traversal. A tree structure 120traversal strategy by level-order may be implemented by accessing nodes 130 startingfrom the root node 131 and accessing nodes 130 in the order of level from left to right.With reference to fig. 4a, the root node A0 comprises level zero of the tree structure120, and the tree structure 120 traversal strategy of level-order thus begins at node A0.The next level of the tree structure 120 to be accessed is level one, starting from theleft-most node A1 and moving on to node B1. The second level nodes A2, B2, C2, D2,E2, F2, G2, H2, I2, and J2 are subsequently accessed from left to right. Traversal oflevel four concludes the level-order tree structure 120 traversal strategy by visitingnodes A3 and B3 in order.

[0179] Upon accessing each node 130 during tree structure 120 traversal, thecomputer model 110 may perform necessary processing e.g. by assigning data such asconsumption measurement(s) 141 to the node 130 or reading data such as consumptionmeasurement(s) 141 from the node. Upon assigning to or reading data from a node130, additional processing steps may be undertaken by the computer model 110.Additional processing steps may involve the comparison of multiple consumptionmeasurements 141 from one or more metered consumption nodes 140, such as byestablishing a mathematical relationship between said consumption measurements 141 from different metered consumption nodes 140 or multiple consumption measurements 141 from a single metered consumption node 140. Mathematicalrelationships between consumption measurements 141 may e.g. be equations,inequalities, systems of equations, or statistical measures established to identifypotential one or more indication(s) of a network fault 161.

[0180] With reference to fig. 4a, it may be the case that consumption representations142 assigned to nodes A0, A1 and B1 do not comply with the principle of conservationof mass or mass flow rate. This scenario may indicate a network fault 261 (not shown)confined to levels one and / or two of the tree structure 120, or possibly a network fault261 involving levels three and / or four. The level-order tree structure 120 traversalstrategy may be used by the computer model 110 to identify such a violation of theprinciple of conservation of mass or mass flow rate by first accessing node A0 toretrieve consumption measurement(s) 141 with associated consumptionrepresentation(s) 142 and timestamp(s) 143. Nodes A1 and B1 of level one aresubsequently accessed in order, and the consumption measurements 141 of each nodeare retrieved.

[0181] Upon retrieval of consumption measurement(s) 141 from B1, a processingstep may entail the comparison of consumption representations 142 from nodes A0,A1 and B1 which may identify that the consumption measurements 141 do not obeythe principle of conservation of mass or mass flow rate. To further establish anindication of a network fault 161, it may be necessary to further traverse the treestructure 120 by employing the level-order tree structure 120 traversal strategy orpossibly another tree structure 120 traversal strategy.

[0182] With reference to fig. 4a, a different tree structure 120 traversal strategy mayaccess nodes according to a depth-first strategy. Because the root node A0 has twochild nodes A1 and B1, each of these two nodes form a subtree. According to thedepth-first tree structure 120 traversal strategy, the nodes 130 of each of the twosubtrees are accessed in turn. The first subtree consists of nodes A1, A2, B2, C2, D2,E2, A3, and B3. The second subtree consists of nodes B1, F2, G2, H2, I2, and J2. Thedepth-order tree structure 120 traversal strategy will thus access nodes 130 in an order such that all nodes of each of the two subtrees are visited in order. Completion of the first subtree thus involves accessing nodes A1, A2, B2, C2, D2, E2, A3, and B3 inorder followed by accessing nodes B1, F2, G2, H2, I2, and J2. The order in which eachof the two subtrees are accessed may be swapped, as may the order in which the rootnode 131 is accessed.

[0183] With reference to fig. 4a, it may be the case that consumption representations141 assigned to nodes E2, A3 and B3 do not comply with the principle of conservation of mass or mass flow rate. This scenario may indicate a network fault 261 (not shown)confined to levels three and / or four of the tree structure 120. The depth-first treestructure 120 traversal strategy may be used by the computer model 110 to identify such a violation of the principle of conservation of mass or mass flow rate by firstaccessing the subtree consisting of nodes A1, A2, B2, C2, D2, E2, A3, and B3 in order.

[0184] Fig. 4b illustrates the tree structure 120 highlighting four subtrees 122consisting of a parent node 132 and corresponding child nodes 133. A first subtree 122includes parent and root node A0 with child nodes A1 and B1. A second subtree 122includes parent node A1 and child nodes A2, B2, C2, D2, and E2. A third subtree 122 includes parent node B1 and child nodes F2, G2, H2, I2, and J2. A fourth subtree 122includes parent node E2 and child nodes A3 and B3. Each of the four subtrees 122 aredemarcated using a dotted line.

[0185] The four subtrees 122 illustrated on fig. 4b each consist of a parent node 132and its child nodes 133 and indicate a typically useful grouping of nodes into subtrees122 which may facilitate an effective identification of network faults 261 (not shown).A tree structure 120 traversal strategy may effectively group nodes 130 of the tree structure 120 such that each parent node 132 and its child nodes 133 are considered as one subtree 122 and such that measurements associated with the nodes of the subtreeare compared separately from the rest of the tree structure 120. The principle ofconservation of mass or mass flow rate may for example be employed to establish amathematical relationship for comparison between consumption measurements 141associated with a parent node 132 and consumption measurements 141 associated withall of its child nodes 133. A processing step may advantageously be implemented bythe computer model 110 once the tree structure 120 traversal strategy has accessed allthe nodes of the subtree 122 consisting of a parent node 132 and all of its child nodes 133.

[0186] The preceding descriptions of figs. 1 – 4b include elaborations on differenttypes of uncertainties which may affect the indication of a network fault 160 asprovided by the fluid distribution network monitoring system 100. The consumption representation 142 of a consumption measurement 141 may for example be perturbed by a measurement error which constitutes a form of uncertainty. In certain situations, the measurement error may be estimated, but in other situations it may be necessary for the fluid distribution network monitoring system 100 to apply assumptions aboutthe magnitude of the measurement error. The measurement error may for example beassumed to be within an interval defined by the maximum permissible error. Thisrepresents an additional uncertainty and may affect the ability of the fault detector 160to provide e.g. an accurate estimate of the magnitude of a network fault 261.

[0187] Time synchronization may add additional uncertainty to the indication of anetwork fault 161. Depending on the accuracy of time synchronization achieved,consumption representations 142 from different metered consumption nodes 140 maybe assumed to be perturbed by a measurement uncertainty that may be proportional tothe amount of time skew between the timestamps 143 of consumption measurements141 provided by different metering devices 240.

[0188] Other types of uncertainties may arise from assumptions which the fluiddistribution network monitoring system may make about the absence of measurementerror for certain nodes. For example, for a subtree of the tree structure 120 consistingof a parent node 132 and its child nodes 133, the assumption may be made that theparent node is not perturbed by any form of measurement error in order to simplifycorresponding mathematical relationships such as equations. This assumption addsadditional uncertainty when providing an indication of a network fault 161, since theassumption cannot always be verified or may simply be assumed for convenience in order to simplify calculations.

[0189] Assumptions related to the presence or absence of leakage may also addadditional uncertainty, such as when nodes which model leakage are added to the treestructure 120 of the computer model 100 to account for possible leakages 262 in the fluid distribution network 210.

[0190] Different types of uncertainties may be combined to obtain a resultinguncertainty according to some form of mathematical or statistical rule. The fault detector 160 may incorporate this resulting uncertainty when estimating e.g. the magnitude or location of a network fault 261.

[0191] List of reference signs:100 fluid distribution network monitoring system110 computer model of a physical fluid distribution network120 tree structure121 edges of the tree structure122 subtree130 node131 root node132 parent node133 child node140 metered consumption node141 consumption measurement142 consumption representation143 timestamp150 measurement input160 fault detector161 indication of a network fault162 indication of leakage163 indication of unjustified measurement error170 status output210 fluid distribution network220 pipe network221 main pipe of the pipe network222 main branch pipe of the pipe network223 fluid flow through the pipe network240 metering device241 meter reading261 network fault262 leakage263 unjustified measurement error280 consumer290 excavator

Claims

Claims1. Fluid distribution network monitoring system (100) comprising:a computer model (110) of a physical fluid distribution network (210), thecomputer model (110) comprising a plurality of nodes (130) linked by edges (121) ina tree structure (120) representing a pipe network (220) of the fluid distributionnetwork (210); one or more of said plurality of nodes (130) being meteredconsumption nodes (140) representing physical metering devices (240) of the physicalfluid distribution network (210);a measurement input (150) arranged to receive consumption measurements(141) originating from automatically read meter readings (241) from said meteringdevices (240) and assigning the received consumption measurements (141) tocorresponding said metered consumption nodes (140); each consumptionmeasurement (141) comprising a consumption representation (142) and acorresponding timestamp (143);a fault detector (160) arranged to process said consumption measurements (141)on the basis of said computer model (110) to establish an indication (161) of a networkfault (261) in said physical fluid distribution network (210); anda status output (170) arranged to provide said determined indication (161) of anetwork fault (261).

2. Fluid distribution network monitoring method comprising: storing a computer model (110) of a physical fluid distribution network (210),the computer model (110) comprising a plurality of nodes (130) linked by edges (121)in a tree structure (120) representing a pipe network (220) of the fluid distributionnetwork (210); one or more of said plurality of nodes (130) being meteredconsumption nodes (140) representing physical metering devices (240) of the physicalfluid distribution network (210); receiving consumption measurements (141) originating from automatically read meter readings (241) from said metering devices (240) and assigning the received consumption measurements (141) to corresponding said metered consumption nodes(140); each consumption measurement (141) comprising a consumption representation (142) and a corresponding timestamp (143); processing said consumption measurements (141) on the basis of said computer model (110) to establish an indication (161) of a network fault (261) in said physical fluid distribution network (210); and providing said determined indication (161) of a network fault (261).

3. The system of claim 1 or the method of claim 2, wherein said indication (161) of anetwork fault (261) comprises an indication of leakage (162), representing a leakage(262) in said fluid distribution network (210), and / or an indication of unjustifiedmeasurement error (163), representing an unjustified measurement error (263) of oneor more of said metering devices (240) in said fluid distribution network (210).

4. The system or method of any of the preceding claims, wherein said indication (161) of a network fault (261) comprises an indication of magnitude of said network fault (261), such as a volume, flow or a percentage.

5. The system or method of any of the preceding claims, wherein said indication (161) of a network fault (261) comprises an indication of a location of said network fault (261), such as a geographical location with respect to said pipe network (220) or alogical location with respect to said computer model (110).

6. The system or method of any of the preceding claims, wherein said fault detector (160) is arranged to determine an inconsistency related to said metered consumption nodes (140).

7. The system or method of any of the preceding claims, wherein said fault detector (160) is arranged to determine an inconsistency between two or more of said metered consumption nodes (140).

8. The system or method of any of the preceding claims, wherein said fault detector(160) is arranged to determine an inconsistency between one or more of said meteredconsumption nodes (140) and historical data.

9. The system or method of any of the preceding claims, wherein said indication (161)of a network fault (261) is established by considering the physical principle ofconservation of mass and / or mass flow rate.

10. The system or method of any of the preceding claims, wherein said meteringdevices (240) of said physical fluid distribution network (210) are consumption meters.

11. The system or method of any of the preceding claims, wherein said indication of anetwork fault (161) is established by arranging said consumption measurements (141) in a system of linear equations and providing a solution to said system of linear equations.

12. The system or method of any of the preceding claims, wherein said indication of anetwork fault (161) is established by making use of zero-consumption measurements(141) to simplify the computer model (110) of the fluid distribution network (210).

13. The system or method of any of the preceding claims, wherein said fault detector (160) is arranged to take into account a predetermined maximum permissible errorMPE for said consumption representations (142).

14. The system or method of any of the preceding claims, wherein said indication of a network fault (161) is established by determining an inconsistency betweenconsumption measurements (141) of a parent node (132) and consumptionmeasurements (141) of one or more of its child nodes (133).

15. The system or method of any of the preceding claims, wherein an indication ofleakage (162) of said network fault (161) is modeled by one or more of said nodes(130) in said tree structure (120) of said computer model (110).

16. The system or method of any of the preceding claims, wherein said one or morenodes (130) in said tree structure (120) modeling said indication of leakage (162) areintroduced as variables in a system of linear equations with said consumptionmeasurements (141) and a solution to said system of linear equations is provided.

17. The system or method of any of the preceding claims, wherein said unjustifiedmeasurement error (263) originating from one or more said metered consumption nodes (140) is known.

18. The system or method of any of the preceding claims, wherein said unjustified measurement error (263) originating from one or more said metered consumptionnodes (140) is known to be zero.

19. The system or method of any of the preceding claims, wherein said indication of a network fault (161) may be obtained by said computer model (110) identifying consumption measurements (141) which deviate by a predetermined amount from an established statistical measure of consumption measurements (141).

20. The system or method of any of the preceding claims, wherein said consumption representation (142) of said consumption measurement (141) is a flow representation.

21. The system or method of any of the preceding claims, wherein said consumption representation (142) of said consumption measurement (141) is a volume representation.

22. The system or method of any of the preceding claims, wherein said consumptionrepresentation (142) of said consumption measurement (141) has a predeterminedmeasurement uncertainty.

23. The system or method of any of the preceding claims, wherein said consumptionmeasurements (141) of said metered consumption nodes (140) provided by said meterreadings (241) from said one or more metering devices (240) are synchronized in timeto obtain compatible timestamps (143).

24. The system or method of any of the preceding claims, wherein said meteringdevices (240) at least occasionally perform meter readings (241) at substantially equalmeasurement times.

25. The system or method of any of the preceding claims, wherein the frequency at which metering devices (240) perform meter readings (241) and / or provide consumption measurements (141) is adjustable.

26. The system or method of any of the preceding claims, wherein said metering devices (240) comprise adjustable clocks.

27. The system or method of any of the preceding claims, wherein said fluiddistribution network monitoring system (100) or fluid distribution network monitoringmethod is arranged to synchronize clocks of the metering devices (240).

28. The system or method of any of the preceding claims, wherein said fluiddistribution network monitoring system (100) or fluid distribution network monitoringmethod is arranged to adjust timestamps in consumption measurements (141) tocompensate for unsynchronized clocks of metering devices (240).

29. The system or method of any of the preceding claims, wherein said meteringdevices (240) provide said meter readings (241) which are synchronized in time towithin an interval between 0.05 % and 2.5 % of nominal time between said meterreadings (241).

30. The system or method of any of the preceding claims, wherein said pipe network(220) of said fluid distribution network (210) has multiple fluid inlet pipes.

31. The system or method of any of the preceding claims, wherein said fluid distribution network (210) is a water distribution network, and said fluid distribution network monitoring system (100) or fluid distribution network monitoring method isa water distribution network monitoring system or water distribution networkmonitoring method, respectively.

32. The system or method of any of the preceding claims, wherein said fluiddistribution network (210) distributes potable water.

33. The system or method of any of the preceding claims, wherein said meteringdevices (240) are wirelessly connected to said fluid distribution network monitoringsystem (100).

34. The system or method of any of the preceding claims, wherein said tree structure(120) is implemented as a hierarchical tree data structure in said computer model (110)representing said physical fluid distribution network (210).

35. The system or method of any of the preceding claims, wherein said tree structure(120) is implemented as a table of values in said computer model (110) of said fluiddistribution network (210).

36. The system or method of any of the preceding claims, wherein said tree structure(120) is implemented as a database in said computer model (110) of said fluiddistribution network (210).

37. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the fluid distribution networkmonitoring method of any of the claims 2-36.

38. A computer program comprising instructions to cause the fluid distributionnetwork monitoring system of claim 1 or any of claims 3-36 to execute the fluiddistribution network monitoring method of any of the claims 2-36.

39. A computer-readable data carrier having stored thereon the computer program ofclaim 37 or 38.

40. A data carrier signal carrying the computer program of claim 37 or 38.

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