Method for characterising leaks

Abstract

A method for characterizing a leak in a fluid network, making it possible to determine the type and/or the flow rate of a leak in a fluid network, in which the fluid network is equipped with a plurality of vibro-acoustic sensors configured to provide vibro-acoustic signals, and in which a statistical learning model receives as input at least one vibro-acoustic signal obtained directly or indirectly from at least one vibro-acoustic sensor and provides as output at least one leak characterization data among the leak type and the leak flow rate.

Claims

1-15. (canceled)

16. A method for training a statistical learning model intended for the characterization of a leak in a fluid network including a plurality of pipes, wherein the fluid network is equipped with a plurality of vibro-acoustic sensors configured to provide vibro-acoustic signals, the method comprising: associating, with the construction of a database, at least for a plurality of documented leaks, at least one leak characterization data actually determined among the leak type and the leak flow rate with at least one vibro-acoustic signal obtained directly or indirectly from at least one vibro-acoustic sensor, and training of the statistical learning model on the thus constructed database.

17. The training method according to claim 16, wherein the fluid network is equipped with at least one flow rate sensor providing sectorization data, and wherein the training method comprises, for at least one documented leak, a step of determining the leak flow rate using the sectorization data.

18. The training method according to claim 16, wherein the fluid network is provided with a digital mapping comprising at least the geometry of the fluid network and the location of said vibro-acoustic sensors.

19. The training method according to claim 18, comprising, for at least one documented leak, a step of simulating at least one virtual vibro-acoustic sensor having a virtual location recorded in the digital mapping of the fluid network and a simulated vibro-acoustic signal from the actually measured vibro-acoustic signals from the real vibro-acoustic sensors and the geometric data from the digital mapping of the fluid network.

20. The training method according to claim 18, comprising a step of locating the leak from the vibro-acoustic signals from the vibro-acoustic sensors and the geometric data from the digital mapping of the fluid network and, for at least one documented leak, a step of reconstructing the vibro-acoustic signal at the level of the leak from the vibro-acoustic signals from the vibro-acoustic sensors and the geometric data from the digital mapping of the fluid network.

21. The training method according to claim 16, wherein the database comprises, for at least one documented leak, structural data of the pipe at level of the leak.

22. The training method according to claim 16, comprising a standardization step resulting in converting the raw vibro-acoustic signal from at least one vibro-acoustic sensor into a standardized vibro-acoustic signal having a predetermined format.

23. The training method according to claim 16, wherein the statistical learning model is a neural network.

24. A method for characterizing a leak in a fluid network including a plurality of pipes, wherein the fluid network is equipped with a plurality of vibro-acoustic sensors configured to provide vibro-acoustic signals, the method comprising: receiving, by a statistical learning model, as input at least one vibro-acoustic signal obtained directly or indirectly from at least one vibro-acoustic sensor and providing, from the statistical learning model, as output at least one leak characterization data among the leak type and the leak flow rate, and wherein the statistical learning model has been trained using a training method according to claim 16.

25. The leak characterization method according to claim 24, wherein the fluid network is provided with a digital mapping comprising at least the geometry of the fluid network and the location of said vibro-acoustic sensors.

26. The characterization method according to claim 25, comprising a step of locating the leak from the vibro-acoustic signals from the vibro-acoustic sensors and the geometric data from the digital mapping of the fluid network and a step of reconstructing the vibro-acoustic signal at the level of the leak from the vibro-acoustic signals from the vibro-acoustic sensors and the geometric data from the digital mapping of the fluid network, wherein the statistical learning model receives as input at least the vibro-acoustic signal reconstructed at the level of the leak.

27. The characterization method according to claim 25, wherein the digital mapping of the fluid network comprises structural data of the fluid network.

28. A module for characterizing a leak in a fluid network, the fluid network being equipped with a plurality of vibro-acoustic sensors configured to provide vibro-acoustic signals, the module comprising: a statistical learning model, configured to receive as input at least one vibro-acoustic signal obtained directly or indirectly from at least one vibro-acoustic sensor and to provide as output at least one leak characterization data among the leak type and the leak flow rate, wherein the statistical learning model has been trained using a training method according to claim 16.

29. A fluid network, comprising: a plurality of vibro-acoustic sensors configured to provide vibro-acoustic signals, and a characterization module according to claim 28.

30. A computer program comprising instructions for executing the steps of the training method of claim 16 when the program is executed by a computer.

31. A computer program comprising instructions for executing the steps of the characterization method of claim 24 when the program is executed by a computer.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0088] The appended drawings are schematic and primarily intended to illustrate the principles of the disclosure.

[0089] In these drawings, from one figure to another, identical elements (or parts of elements) are identified by the same reference signs. Furthermore, elements (or parts of elements) belonging to different exemplary embodiments but having a similar function are identified in the figures through numerical references incremented by 100, 200, etc.

[0090] FIG. 1 is an overall diagram of a fluid network equipped with a leak characterization module.

[0091] FIG. 2 is an overview diagram of a leak characterization module.

[0092] FIG. 3 illustrates the detection of a leak within the fluid network of FIG. 1.

[0093] FIG. 4 illustrates the acquisition of the vibro-acoustic signal at the level of the leak.

[0094] FIG. 5 illustrates a first example of a neural network training.

[0095] FIG. 6 illustrates a first example of a leak characterization using this neural network.

[0096] FIG. 7 illustrates the simulation of virtual vibro-acoustic sensors.

[0097] FIG. 8 illustrates a second example of a neural network training.

[0098] FIG. 9 illustrates a second example of a leak characterization using this neural network.

DESCRIPTION OF THE EMBODIMENTS

[0099] In order to make the disclosure more concrete, examples of training methods, characterization methods and characterization modules are described in detail below, with reference to the appended drawings. It is recalled that the disclosure is not limited to these examples.

[0100] FIG. 1 represents a fluid network diagram 1, in this case a drinking water distribution network. This fluid network 1 has a plurality of pipes 2 on which a plurality of vibro-acoustic sensors 3, here acoustic sensors of the accelerometer type, are mounted. The fluid network also has a leak characterization module 10 which can be hosted within a computer of the management of the fluid network 1 or within a remote server. The fluid network 1 further includes sectorization sensors, and particularly flowmeters and pressure gauges.

[0101] FIG. 2 illustrates the main elements of this leak characterization module 10. It thus includes a digital mapping 11 of the fluid network 1, a leak database 12, a neural network 13 (forming a statistical learning model), a calculation unit 14 and a memory 15; it also includes all the electronic elements that allow operating such an electronic module: power supply, user interfaces, etc.

[0102] The digital map 11 includes the geometry of the fluid network 1, that is to say the position, the orientation and the length of all the pipes 2, as well as the position of the whole equipment of the network, that is to say the valves, the junction collars, the connections, the valve boxes etc. The digital map 11 further includes the location of all the vibro-acoustic sensors 3 but also of the sectorization sensors.

[0103] The digital map 11 also includes the most comprehensive structural data for the entire network 1, and in particular, as much as possible, the material of each pipe, its nominal diameter, its thickness, its depth or the surrounding ground material.

[0104] The database 12 for its part compiles as much data as possible concerning the leaks identified and characterized in the past within the fluid network 1. Its construction will be described in more detail below.

[0105] In the present example, the neural network 13 is a convolutional network of the regressor type including two convolutional layers of time filters and two non-convolutional layers. The layers of the neural network 13 contain time filters of a size between 25 and 100 ms. The convolutional layers are organized such that the number of filters increases with each layer. The single layers are organized such that the number of neurons decreases until the final estimation. To avoid the overlearning, the abandonment technique is used between 30 and 70%. In addition, to obtain an estimation of uncertainty, a Bayesian network is used.

[0106] The calculation unit 14 can in particular take the form of a processor: it is in particular programmed to be capable of solving digital problems of propagation of a sound wave along the fluid network 1, based on the geometric and structural data from the digital mapping 11.

[0107] The memory 15 can take any form of data storage. It includes in particular the theoretical equations of the propagation of the sound waves along a pipe. It also includes a library of transfer functions, established theoretically or empirically using laboratory or on-site tests, making it possible to simulate the deformation undergone by a vibro-acoustic signal during its passage through a particular equipment of the fluid network 1, particularly a valve, a bend, a connection or a collar. This library also includes transfer functions for converting the raw signal from a vibro-acoustic sensor of a given type and model into a common reference format of the sound wave type, for example in the form of a WAVE type sound file.

[0108] In such a fluid network 1, any leak generates a characteristic noise which propagates along the pipes 2 and which can therefore be detected and recorded by vibro-acoustic sensors 3 such as microphones, geophones, hydrophones or accelerometers. Thus, as represented in FIG. 3, in the presence of a leak 20, the vibro-acoustic sensors 3 of the network 1 each record a signal 21 revealing the presence of the leak 20. However, due to their different positions within the fluid network 1, each sensor 3 records a slightly different signal 21: particularly, the level of the signal is all the more attenuated as the sensor 3 is away from the leak 20; moreover, the shape of the signal can also be altered during the propagation, in particular when passing through certain equipment of the network 1.

[0109] Studying this attenuation and/or these alterations then allows more or less accurately locating the leak 20. Moreover, in the present example, the vibro-acoustic sensors 3 are correlative, that is to say they all share a common clock: in this way, it is possible to measure the delay between the different signals 21, which allows, the speed of sound propagation along the pipes 2 being known for a given material recorded in the digital mapping 11, determining the distance separating each sensor 3 from the leak 20. Such correlating sensors then allow locating the leak 20 more easily by cross-checking the data from several sensors 3.

[0110] Once the leak 20 is located, it is possible to go on site to excavate it and repair it. On this occasion, it is also possible to determine its type Tf, that is to say to determine whether it is a leak caused by a crack, a tapping or a defective seal, for example at the level of a collar or a connection.

[0111] Once the leak 20 is repaired, the calculation unit 14 is capable of automatically determining the flow rate Qf that this leak 20 presented by comparison of the sectorization data before and after the repair.

[0112] In addition, as represented in FIG. 4, it is also possible on this occasion to record the vibro-acoustic signal 22 generated by the leak 20 precisely at the level of the leak.

[0113] Alternatively or additionally, it is also possible to reconstruct the vibro-acoustic signal during the leak 22 from the signals 21 of the vibro-acoustic sensors 3. Such a reconstruction is carried out by the calculation unit 14 by digital simulation of the acoustic propagation from the geometric and structural data contained in the digital mapping 11 as well as the propagation equations and transfer functions recorded in the memory 15.

[0114] The training of the neural network 13 is then represented in FIG. 5. For each leak 20 identified and characterized by an operator, all the data relating to this leak 20 are recorded in the database 12: particularly, the characterization, including the leak type Tf and the leak flow rate Qf, is recorded in association with the vibro-acoustic signal during the leak 22.

[0115] Structural data of the pipe 2 having the leak 20 are also recorded in the database 12: these structural data include the material of the pipe, its nominal diameter, its thickness, its depth as well as the surrounding ground material. Contextual repair data, such as the type of backfill used, the flooding state around the leak or a photograph of the leak, can also be recorded in the database 12.

[0116] Once a large number of leaks 20 has thus been listed in the database 12, the neural network 13 is applied on the database 12 in order to perform its initial training. Once the initial training is over, the neural network 13 can then be used to automatically characterize new leaks 20.

[0117] Concretely, the leak characterization module 10 permanently receives the signals 21 recorded by the vibro-acoustic sensors 3. Insofar as the fluid network 1 can include different types or models of vibro-acoustic sensors 3, all the signals 21 thus recorded are converted, during a standardization step, into a common format using the transfer functions recorded in the memory 15.

[0118] In addition, each signal 21 undergoes a qualification step during which it is verified that the signal 21 is not corrupted and has not been made inoperable by an excessive interfering noise such as the passage of a vehicle for example. The signals 21 thus qualified then undergo a cleaning step during which they are filtered in order to remove most of the interference.

[0119] Therefore, when a new leak 20 is present in the network 1, the leak characterization module 10 detects the occurrence of a signal representative of a leak in the vibro-acoustic signal 21 of one or several vibro-acoustic sensors 3. The characterization module 10 then carries out the location of the leak 20 then the reconstruction of the vibro-acoustic signal during the leak 22 as described above.

[0120] As represented in FIG. 6, the vibro-acoustic signal during the leak 22 is then transmitted at the inlet of the neural network 13: thanks to its training, the neural network 13 is then capable of giving as output the characterization of the leak 20, that is to say its type Tf and/or its flow rate Qf.

[0121] The structural data of the pipe 2 having the leak 20, resulting from the digital mapping 11, can also be transmitted at the inlet of the neural network 13 in order to facilitate the characterization and, if necessary, increase its accuracy. In addition, the greater the training of the neural network 13, the finer the accuracy of the characterization: particularly, it is possible to expect an accurate estimation of the flow rate Qf to within 10% or within 5 m.sup.3/h.

[0122] In order to further increase the ease and accuracy of the characterization, it is also possible to include in the training, then in the input data provided to the neural network 13, all the vibro-acoustic signals 21 recorded by the vibro-acoustic sensors 3, in addition to or instead of the vibro-acoustic signal during the leak 22.

[0123] FIGS. 7-9 illustrate another method to further increase the ease and accuracy of the characterization. In this variant, the fluid network 101 includes the same vibro-acoustic sensors 103 as in the first example. However, in addition to these real sensors 103, the fluid network 101 now also includes virtual vibro-acoustic sensors 104.

[0124] These virtual vibro-acoustic sensors 104 are positioned in the digital map 11 so as to reduce the distance separating two real or virtual sensors. For example, two virtual sensors 104 can be simulated between two consecutive real sensors 103.

[0125] The calculation unit 14 is then capable, for each virtual sensor 104, of simulating the vibro-acoustic signal 123 which would actually be recorded if a real sensor were provided at this location. This simulation is possible from the vibro-acoustic signals 121 of the real sensors 103 provided in the vicinity of the virtual sensor 104 considered, by solving the digital simulation of the acoustic propagation using the propagation equations and transfer functions recorded in the memory 15 of the characterization module 10.

[0126] All the real vibro-acoustic signals 121, reconstructed during the leak 122 and simulated 123 from a given leak 120 can then be recorded in the database 112, which increases the amount of data on which the neural network 113 can perform its training.

[0127] In addition, in order to enhance the robustness of the training, each signal 121, 122, 123 recorded in the database can undergo a noise-adding step during which noise is added to the signal 121, 122, 123.

[0128] Accordingly, when a new leak 120 appears, the neural network 113 is capable of more easily and accurately characterizing the new leak 120, even by providing it as input with only the real vibro-acoustic signals 121 for example. Naturally, it is also possible to provide the neural network 113, in addition to or instead of the real acoustic signals 121, with the signal reconstructed to the source 122 and/or simulated signals 123.

[0129] Although the present disclosure has been described with reference to specific exemplary embodiments, it is obvious that modifications and changes can be made to these examples without departing from the general scope of the disclosure as defined by the claims. Particularly, individual characteristics of the different illustrated/mentioned embodiments can be combined in additional embodiments. Accordingly, the description and the drawings should be considered in an illustrative rather than restrictive sense.

[0130] It is also obvious that all the characteristics described with reference to a method can be transposed, alone or in combination, to one device, and conversely, all the characteristics described with reference to a device can be transposed, alone or in combination, to one method.