METHOD FOR LEAKAGE DETECTION

Abstract

The invention relates to a method for leakage detection on an object flown through by a medium, in particular a pipe or a pipeline. According to the invention, a pattern is identified in the determined values for the change of the flow rate and the pressure of the medium and for a temperature change, and a probability for the presence of a leak is determined based on the identified pattern and due to self-learning systems.

Claims

1. A method for leakage detection on an object carrying a flow of a medium, in particular a pipe or a pipeline, wherein a value for a change in the flow rate of the medium, for a pressure change in the medium and a temperature value change is ascertained at each of a plurality of measurement points on a measurement section of the object carrying a flow, and wherein the ascertained values are recorded and statistically evaluated by a data processing device, wherein in that a group of values formed from the ascertained values and a pattern is identified in the group of values by means of the data processing device and a likelihood of a presence of a leak in the measurement section of the object carrying a flow is determined by based on the identified pattern.

2. The method as claimed in claim 1, wherein a classification algorithm and/or a pattern analysis algorithm is applied to the identified pattern.

3. The method as claimed in claim 1, wherein the identified pattern is stored as a dataset in a database and/or is assigned to a dataset stored in a database.

4. The method as claimed in claim 1, wherein the identified pattern is compared with one or more stored patterns.

5. The method as claimed in claim 1, wherein a characteristic pattern serves as a criterion for the presence of a leak in the measurement section of the object carrying a flow.

6. The method as claimed in claim 1, wherein the data processing device applies a learning algorithm to the ascertained values and/or the group of values.

7. The method as claimed in claim 6, wherein the learning algorithm is trained using stored and/or simulated values before being applied to the ascertained values and/or the group of values, wherein the stored and/or simulated values are associated with an actual and/or a simulated presence of a leak on an object carrying a flow of a medium.

8. The method as claimed in claim 1, wherein the value for the change in the flow rate of the medium, the value for the pressure change in the medium and/or the temperature value change are each ascertained noninvasively.

9. The method as claimed in claim 1, wherein the change in the flow rate is measured by means of an acoustic, preferably ultrasound-based, method.

10. The method as claimed in claim 1, wherein data from an external source, in particular concerning the weather in the surroundings of the measurement section and/or of a measurement point, are processed by the data processing device, in particular linked with the group of values and/or added to the group of values.

11. The method as claimed in claim 1, wherein the ascertained values from different measurement points are transmitted to a central data processing device, preferably wirelessly.

12. A computer program product for determining a likelihood of a presence of a leak on an object carrying a flow of a medium, in particular a pipe or a pipeline, wherein instructions for recognizing a pattern in a group of values, wherein the group of values is formed by values, which are ascertained on a measurement section of the object, carrying a flow for a change in the flow rate of the medium, for a pressure change in the medium and/or for a temperature change.

Description

[0044] The invention is explained below in more detail on the basis of exemplary embodiments. All of the features described and/or shown in the drawings each form independent aspects of the invention, regardless of their combination in the exemplary embodiments or in the dependency references in the claims.

[0045] In the drawings

[0046] FIG. 1 shows a schematic representation of an illustrative application situation for the method according to the invention,

[0047] FIG. 2 shows a schematic representation of a further application situation for the method according to the invention and

[0048] FIG. 3 shows a schematic illustration of the data processing for the method according to the invention.

[0049] FIG. 1 shows a typical application situation for the method according to the invention. An object 1 carrying a flow, in the form of a pipeline or a pipeline section for conveying a product in the form of an in particular fluidic medium, is laid outdoors partly above ground and partly below ground.

[0050] The detail shown represents a measurement section 2 of the considerably longer object 1 carrying a flow. The measurement section 2 is monitored by the method for leakage detection according to the invention. This is accomplished by ascertaining a value for various physical parameters at each of two measurement points 3.

[0051] As a departure from the two measurement points 3 shown, a measurement section 2 may also have a larger associated number of measurement points 3. It is furthermore certainly preferred, but not absolutely necessary according to the invention, for the measurement points 3 for various physical quantities to be arranged at the same positions along the measurement section 2 of the object 1 carrying a flow.

[0052] In general, the object 1 carrying a flow may be understood to mean an object that is fundamentally intended to have a medium flow through it. In this respect, it is fundamentally also possible in the invention to ascertain values relating to a measurement section 2 that does not have the medium flow through it continuously. Determination and/or prediction of environmental parameters, such as a change in the ambient temperature, may be of interest in regard to a forthcoming transportation of the medium through the measurement section 2, for example.

[0053] In principle, for all of the relevant physical parameters, it is preferred for the applicable values to be ascertained noninvasively where possible, i.e without the flowing medium being influenced by components introduced into the object 1 carrying a flow or the flow being disrupted in another way.

[0054] A value for the change in the flow rate of the medium is ascertained. This is performed in particular by a flowmeter 4. In the example shown in the present case, the preferred configuration of the flowmeter 4 is shown as a so-called clamp-on flowmeter, which is applied externally to the object 1 carrying a flow. The flow rate of the medium, or the change in said flow rate, may therefore be ascertained noninvasively. It is self-evident that any other type of flow measurement in principle may be useful for ascertaining values. The flow rate may be understood as referenced to the mass and/or the volume.

[0055] In the example shown, the flowmeter 4 is based on an acoustic principle for measuring the change in the flow rate of the medium. This involves acoustic signals, in particular in the ultrasonic range, being introduced into the medium through the wall of the object 1 carrying a flow, and their propagation speed being measured in order to draw a conclusion about the flow properties of the medium. Preferably, the acoustic signal is injected and/or the propagated signal is read contactlessly, i.e. without mechanical coupling of a transducer of the flowmeter 4 to the wall of the object 1 carrying a flow.

[0056] In addition, a value for the pressure change in the medium is ascertained at each measurement point 3. The pressure change is measured in particular by way of an appropriate pressure sensor 5.

[0057] Furthermore, a temperature change is ascertained in particular by means of a temperature sensor 6. This is in particular a value for the ambient temperature, or the change therein, at the location of the measurement point 3. Alternatively or additionally, a value may also be recorded away from the measurement point 3, for example between two measurement points 3 of a measurement section 2. In this context, such a value may be ascertained for the air temperature, the ground temperature, the temperature of the object 1 carrying a flow or of the flowing medium itself. In particular the influence of the ambient temperature on the medium in the object 1 carrying a flow along the stretch may therefore be taken into consideration.

[0058] The values, in particular ascertained by measurement, for the cited and, if necessary, further physical parameters are transmitted to a data processing device 7 in order to be subsequently evaluated further. The transmission is preferably effected wirelessly. It is self-evident that, alternatively or additionally, a wired transmission may also take place.

[0059] The data processing device 7 may, as indicated in the representation in FIG. 1, be a central data processing device 7 positioned at a location that is remote from the measurement section 2. The data processing device 7 may be in the form of a single computer, but also in the form of a network of multiple interacting computers. In addition, there may also be provision for a configuration of the data processing device 7 as a complex system having multiple computing units that operate in parallel and/or are hierarchically linked.

[0060] The data transmission from the measurement points 3 to the data processing device 7 may be effected in particular according to popular transmission standards, such as Bluetooth or WiFi, and/or via a mobile radio network. In addition, there is also the possibility of satellite-based communication between the measurement point 3, or the devices for ascertaining values provided at the measurement points 3, and the data processing device 7.

[0061] Furthermore, communication may take place between applicable communication devices at the measurement points 3. By way of example, this permits provision to be made for a powerful transmission installation, just at one measurement point 3 or at least at a few measurement points 3, in order to transmit the ascertained values to the data processing device 7. The at the individual measurement points 3 of the measurement section 2 are initially transmitted over comparatively short distances to a central measurement point 3 of this kind and from there are transferred to the data processing device 7. An appropriate design may also be realized by a separate relay station 10, which is not associated with a specific measurement point 3 but rather is situated in the surroundings of the relevant measurement section 2 and hence in range of the communication devices of all of the relevant measurement points 3.

[0062] One particular configuration of the method involves at least substantially exclusively data relating to the flow rate of the medium, or the change in said flow rate. These data are preferably delivered by flowmeters 4 and/or ascertained in a modeling.

[0063] Particularly preferably, a network of measurement points 3, or flowmeters 4, is furthermore used that extends at least over a portion of the object 1 carrying a flow, or of the measurement section 2. In this case, the individual measurement points 3, or flowmeters 4, preferably communicate with one another and/or with a data processing device 7 wirelessly, optionally using an interposed relay station 10. Alternatively or additionally, just as in other configurations of the method, there may also be recourse to standard mobile radio technologies and/or provision for satellite-based communication.

[0064] As will be explained in even more detail below, the transmitted data are evaluated as part of the method according to the invention by means of the data processing device 7 and examined for the presence of a pattern that indicates the presence of a leak 8 in the examined measurement section 2. If such a leak 8 is detected, or if a sufficient likelihood of a presence of a leak 8 is ascertained, appropriate measures may be taken in a short time to provide a remedy.

[0065] In the representation in FIG. 1, such a leak 8 is indicated in the section of the object 1 carrying a flow that runs below ground. The transported medium, which may be crude oil, for example, is getting into the soil 9 in an uncontrolled manner at the position of the leak 8 and may contaminate the groundwater there, for example. Besides the economic significance of a loss of the transported medium, such a leak 8 may entail serious ecological consequences. Extensive damage to the environment occurs not just in the case of catastrophic leaks 8 in which a large amount of the transported medium escapes in a short time. Rather, small leaks 8 that cause only a slow escape of the medium over time may also already be a great ecological hazard.

[0066] FIG. 2 shows a further application situation for the method according to the invention by way of illustration. The object 1 carrying a flow is formed by a comparatively complex pipe network there. The detail shown is intended to represent an extensively ramified network of pipelines, in some cases of great length, purely symbolically. Apart from ramified networks of supply lines, some of which span great distances between different regions of the earth, a larger industrial installation, for example a refinery, may also comprise a comparatively complex pipe network. Various measurement sections 2 may be defined in such a highly ramified object 1 carrying a flow. A measurement section 2 is not necessarily defined only by the section of the object 1 carrying a flow between two measurement points 3, but may also comprise further areas, in which there is in particular provision for more than two measurement points 3. The definition of a measurement section 2 is ultimately dependent on from which measurement points 3 received values, or for which measurement points 3 ascertained values, are used for the evaluation by the data processing device 7.

[0067] If the ramification complexity of the object 1 carrying a flow is accordingly high, said object may then be monitored directly by applicable sensors only with difficulty. Similarly to in the case of a pipeline having a very great length, complete monitoring of the system ultimately founders on the costs that would arise for an appropriate number of sensors. In addition, the partial volumes, which are in each case fluidically connected to one another, in the various branches of the object 1 carrying a flow result in interactions and buffer effects when the transported medium propagates in the pipe network. This also hampers the evaluation of a mass flow balance.

[0068] The method according to the invention has an advantageous effect here by detecting interference events, such as the occurrence of a leak 8, in a specific measurement section 2 by identifying patterns in the ascertained values.

[0069] The influence of different temperatures on the behavior of the transported medium arises not only as it passes through various climate zones or on account of different weather conditions along a pipeline. In the example of an industrial installation too, it is usually the case that pipelines run along structures at different temperatures. For this reason, the temperature of the medium usually changes as it flows through the pipeline, or the pipeline network. The associated expansion or contraction of the medium significantly disrupts the ascertainment of a mass flow balance and hampers the detection of an actual loss of mass, for example on account of a leak 8 or on account of illegal tapping on the transport path.

[0070] In this regard, the method according to the invention in particular allows for the fact that various influencing factors usually affect the transported medium, in particular the prevailing pressure and/or flow rate conditions, on different timescales. Changes in the climatic or weather-related influences generally affect the medium in pipelines, in particular those running below ground, with a time delay, this being accompanied by a certain inertia in the reaction of the system. By contrast, desired tappings of the medium, for example by end consumers, especially lead to short-term and especially locally occurring fluctuations, which likewise need to be taken into consideration in an appropriate manner.

[0071] In particular desired, but unschedulable, tappings of the medium in a measurement section 2, for example by end consumers, may be modeled by way of appropriate local consumption measurements and included in the method according to the invention. To this end, there may be provision for suitable positioning of one or more measurement points 3, in particular comprising a flowmeter 4, in the vicinity of the known tapping point.

[0072] The aim of the method according to the invention is to distinguish patterns in the ascertained values that occur on the basis of temperature and volume fluctuations in the medium on account of external and internal influences from patterns that are related to actual loss of the medium from the object 1 carrying a flow on the transport path. The natural influences on the medium are varied and accordingly may be taken into consideration completely in popular statistical methods only with difficulty.

[0073] Fluctuations that occur are primarily related to a change in the temperature of the transported medium over time and in space—in particular along the object 1 carrying a flow. Although this is highly dependent on the ambient temperature, it is influenced by numerous other factors. Air and ground temperature are dependent on the insolation to different degrees and affect the temperature of the medium accordingly. By contrast, rain and cloud have a short-term cooling effect. In addition, in particular in the case of pipelines that run below ground, the biomass at the surface may have an effect on the temperature of the medium in the line, for example in the form of an insulating effect or by shielding the ground from sunlight. This factor is also subject to sometimes short-term changes, for example as a result of cultivation and harvesting on areas used agriculturally.

[0074] If the object 1 carrying a flow is of sufficiently great extent or accordingly complex ramification, such as a pipeline or a pipeline network, thermodynamic changes in the flow properties of the transported medium generally also invariably occur on account of internal effects. The reasons for this are for example the fluctuation or change in the flow resistance on account of the shape of the line. In particular if the transported medium is composed of various substances, a change in the composition may additionally occur. This may also affect the flow behavior of the medium.

[0075] Large pipelines or pipe networks may furthermore have a considerable natural volume that is initially filled during so-called “line packing”, i.e charging the line with the medium and building up operating pressure, before the medium comes out again, or is tapped, at a particular point. A sufficiently large internal volume of the object 1 carrying a flow additionally leads to buffer effects, even during operation, that allow volume-related changes in the medium to be registered only indirectly. Without further consideration of internal and/or external parameters, it is thus hardly possible to draw meaningful conclusions from a comparative measurement of the flow rate, or the change therein, at the input and the output of a measurement section 2 of the object 1 carrying a flow.

[0076] Extensive tests have shown, surprisingly, that different types of patterns may form in the ascertained values. Some natural fluctuations may not be completely eliminated by means of popular statistical methods, even after the environmental parameters have been included, but lead to patterns in the data. These are distinguished from those patterns that may be observed in the event of an actual loss of mass, for example owing to a leak 8, a line break or an illegal tapping of medium on the transport path.

[0077] This is the starting point for the invention in that these two types of patterns are identified and distinguished from one another. As already mentioned, the method involves the data processing device 7 being used, during or after the evaluation of the ascertained values for the change in the flow rate, in the pressure and in the temperature and, if necessary, in further physical quantities, to look for a pattern in these values.

[0078] The representation shown in FIG. 3 illustrates the basic sequence for the evaluation of ascertained values by the data processing device 7 for leakage detection. A group of values 11 is initially formed from the ascertained values and is analyzed by the data processing device 7 for the presence of a pattern. The group of values 11 may comprise all of the values ascertained at the measurement points 3 of a measurement section 2 or may be a subset thereof.

[0079] If a pattern is identified in the group of values 11, the data processing device 7 may take this pattern as a basis for determining the likelihood of the presence of a leak 8 in the relevant measurement section 2 of the object 1 carrying a flow. Such a pattern in the data of the group of values 11 is identified in particular by way of an appropriate algorithm of a detection routine, similarly to in the case of digital image recognition.

[0080] The data processing device 7 is preferably designed to execute a classification algorithm and applies such an algorithm to the group of values 11. An identified pattern is therefore classified in respect of its type, nature and/or qualities.

[0081] As an alternative or in addition to such a classification algorithm, a pattern analysis algorithm may also be applied to the group of values 11 by the data processing device 7. Such a pattern analysis algorithm may interpret the significance of the identified pattern. This allows a statement to be made regarding what real event is represented by the pattern that occurs in the ascertained values.

[0082] In one preferred configuration, the data processing device 7 accesses a database in which an identified pattern may be stored as dataset 12. The same applies to the ascertained values, or the group of values 11. In particular, the identified pattern, the group of values 11 and/or a specific really occurring event, for example the presence of a leak 8, may be linked with one another and stored in the database as datasets 12 or as a joint dataset 12.

[0083] A comparison of the pattern in the analyzed group of values 11 with one or more patterns stored in datasets 12 of the database allows the identified pattern to be quickly assigned to a group of events in the simplest case. Such a comparison in the manner of a fingerprint is possible in particular if the data processing device 7 has access to datasets 12 that are classified in respect of the stored patterns and/or the associated events and the identified pattern may be uniquely assigned to one of these classes on the basis of its characteristics.

[0084] Alternatively or additionally, a characteristic, or idealized, pattern may also be used as a criterion that is taken as a basis for determining the likelihood of a presence of a leak 8 in the measurement section 2 under consideration by way of a comparison with the pattern identified in the group of values 11. A characteristic pattern of this kind may be based on measured values from one or more measurements relating to an event that has really occurred or else may be based on simulated values.

[0085] If there is a sufficient degree of match between the identified pattern and the characteristic pattern, i.e if a defined threshold value is exceeded, the criterion for the presence of a leak 8 may be rated as met, so that appropriate measures may be taken.

[0086] A configuration of the method according to the invention in which the data processing device 7 applies a learning algorithm to the ascertained values, or to the group of values 11, in order to identify a pattern is particularly preferred. Alternatively or additionally, an algorithm with learning capability may also be used to serve as a classification algorithm and/or as a pattern analysis algorithm. Compared to the previously described identification and evaluation of a pattern in the group of values 11 on the basis of essentially firmly prescribed criteria, an algorithm with learning capability has the advantage that it becomes more powerful and more reliable over time as a result of appropriate training with suitable data. There is therefore a decrease in susceptibility to error in regard to the incorrect interpretation of a pattern as an indicator of a leak 8 (false positive) and in regard to the nondetection of an existing leak 8 on the basis of the ascertained values (false negative).

[0087] Such a learning algorithm is preferably trained by way of datasets 12 that relate to real events, in particular the presence of a leak 8, or were measured when the relevant event occurred. Such data ultimately model reality in the best way possible, so that the trained learning algorithm is ultimately tailored to the specific patterns that may arise in the ascertained values in individual cases under real conditions.

[0088] Alternatively or additionally, the learning algorithm may also be trained using simulated values, or model data. This allows the algorithm to have components added that relate to idealized conditions.

[0089] For optimum detection performance in regard to the identification, classification and/or interpretation of patterns in a group of values 11, training the algorithm with a combination of real and simulated, or ideal, data may sometimes be particularly expedient.

[0090] In a more preferred configuration, the data processing device 7 may use an in particular iterative method for modeling values. This involves using in particular a method for forward modeling in order to ascertain values that may be expected under certain work and/or ambient conditions.

[0091] The values ascertained by way of such a modeling method may be employed in different ways for the method according to the invention. By way of example, the parallel application of such a modeling method allows independent verification of the measured values and/or of a pattern that has emerged in the values.

[0092] Data obtained by way of the forward modeling are furthermore also suitable for training a learning algorithm.

[0093] Preferably, a comparison of the evaluation of real data with the modeling of a specific trend for the system allows possible artefacts of the pattern identification to be determined and in particular corrected. In this way, it is preferably possible to compensate for shortcomings of the learning algorithm that emerge in this context and may be conditional, inter alia, on less-than-optimum prioritization during the training of the algorithm. Repeated use of this approach therefore continually improves the reliability of the pattern identification.

[0094] In addition, it is possible for the pattern-recognition-based method according to the invention to be serially linked with a corresponding method for modeling data on the basis of measured values. This allows for example a future trend to be modeled on the basis of known or measured starting parameters and the risk of an imminent structural failure of the object 1 carrying a flow to be assessed in the results thus obtained by identifying patterns that occur.

[0095] In addition to taking into consideration datasets 12 of a database in the manner explained above, it is also possible to include data from external sources, in particular generally available data, in different ways. These data are in particular added to the group of values 11 and/or linked with the group of values 11 in order to be taken into consideration for the evaluation. However, external data of this kind may also be used for a modeling and/or for training a learning algorithm. By way of example, the data may relate to the weather, the geological composition of the ground, the in particular agricultural use of areas or the like.

[0096] The evaluation of the ascertained values, or of the group of values 11 formed therefrom, involves identifying a pattern in the group of values 11 and, if necessary, interpreting the pattern or otherwise associating it with a specific event or an event likelihood. Preferably, the data processing device 7 then generates an appropriate output 13 conveying the result of the preceding analysis, or of the method used, for a user.

[0097] The output 13 may be provided in different ways, preferably visually, audibly and/or in text form. In particular, the output 13, as shown in FIG. 3, may comprise a warning about the presence of a leak 8. Furthermore, a status report may be generated, for example.

[0098] With regard to an automatically operating system, it is alternatively or additionally also possible for remedial measures relating to the output 13 to be immediately taken, for example for an alert to be delivered to maintenance and/or service personnel.

[0099] It is self-evident in this case that it is fundamentally also possible to combine different outputs 13 or reactions to the result of the analysis by the method.

LIST OF REFERENCE SIGNS:

[0100] 1 object carrying a flow

[0101] 2 measurement section

[0102] 3 measurement point

[0103] 4 flowmeter

[0104] 5 pressure sensor

[0105] 6 temperature sensor

[0106] 7 data processing device

[0107] 8 leak

[0108] 9 soil

[0109] 10 relay station

[0110] 11 group of values

[0111] 12 dataset

[0112] 13 output