GB2575669A - Leak detection apparatus and method - Google Patents
Leak detection apparatus and method Download PDFInfo
- Publication number
- GB2575669A GB2575669A GB1811797.8A GB201811797A GB2575669A GB 2575669 A GB2575669 A GB 2575669A GB 201811797 A GB201811797 A GB 201811797A GB 2575669 A GB2575669 A GB 2575669A
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- Prior art keywords
- leak
- autocorrelation
- acoustic data
- frequency spectrum
- leak detection
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
- G01M3/04—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point
- G01M3/24—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using infrasonic, sonic, or ultrasonic vibrations
- G01M3/243—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using infrasonic, sonic, or ultrasonic vibrations for pipes
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
- F17D5/06—Preventing, monitoring, or locating loss using electric or acoustic means
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- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Acoustics & Sound (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Examining Or Testing Airtightness (AREA)
Abstract
A method for detecting a leak in a pipeline is provided to improve the ease with which remote maintenance of utilities networks are managed. The method comprises the steps of: obtaining acoustic data from a location using an audio probe – step 12; computing an autocorrelation of the acoustic data; carrying out a frequency analysis on the autocorrelation and identifying persistent frequencies; and using the persistent frequencies to detect a leak. The aim of the method of the present invention is to provide easy remote detection and management of leaks in a utilities management system (for example, water networks) using acoustic signals.
Description
Leak Detection Apparatus and Method
Field of the Invention
The present invention relates to leak detection systems, particularly to leak detection systems for use in remote management of utilities networks.
Background to the Invention
Detection of leaks is an important part of utilities network management. Non-detection of leaks, inappropriately addressing leaks, and ineffective identification and repair of leakages in pipeline systems can cause excess water loss and pressure reductions in the pipeline system, which would normally result in a sub-standard service to consumers, a negative economic impact on utilities management service providers, and excess damage to utilities system infrastructure.
Leakage identification can help to inform compensatory pressure valve adjustment and accelerate repair, reducing water loss and the negative environmental impacts associated.
Advances in leakage detection technology have enabled remote detection of leaks and a number of currently available technologies employ varying techniques to aid remote leak detection in pipeline systems.
An example can be found in, for example, WO 2008150411, which provides a system which uses pressure sensors to detect leaks using pressure data. The sensors can be in an active mode by generating pressure waves.
There are drawbacks associated with using pressure sensors, particularly when providing pressure waves as a stimulus to the sensors. Such systems typically depend upon the reflection of a signal in order to determine accurate leak detection. However, therefore pressure sensors have to be connected to the pipeline so that it can be exposed to the pipe internal pressure. This requires the sensors to be attached to a valve or a fitting of the pipeline. Furthermore, multiple sensors have to be used, which can also be use in active mode, where one sensor generates a pressure variation that is measured by the other sensors on the pipe network.
Similar systems are provided in, for example, US 4435974, which uses multiple probes to aid location. Systems using multiple probes can be invasive and increases the time required for leak detection.
Acoustic leak detection, as performed by the known DXMic system can be an effective way to circumvent many issues with the technology detailed above. Advantageously, such sound sensors for acoustic leak detection can be applied anywhere on the outside of the pipe. Also, the sound sensor is often used to record pipe noise through the ground, such that there is no need to open manholes or digging. Sound sensors are generally less invasive. This method doesn't necessarily require multiple sensors and the sensors can be passive sensors. Passive sensors are less expensive and require less power to work. There are still, however, a number of drawbacks associated.
Acoustic leak detection can involve a trained and experienced engineer listening to an unprocessed or processed audible signal, assessing the signal for known audio patterns characteristic of, and attributable to, a leak in a pipeline. This form of acoustic leak detection requires skilled engineers having experience with characteristic sounds of leaks. Along with the economic overhead associated with skilled and experienced personnel visiting the site of a suspected leak, there is also a probability of human error associated with this approach. In regions facing a skills shortage, there may also be an insufficient number of such engineers to cope with the demand.
Other problems associated with acoustic leak detection methods include the presence of excess background noise which is unattributed to the leak. This background noise prevents the accurate determination of a leak and prevents the inaccurate location of a leak. Such drawbacks lead to false positives and false negatives, leading to wasted repair team labour time and an enhanced risk of worsening of leaks and excess water loss. Such drawbacks also cause an economic impact, an increased requirement for man power, wasted resources, and reduced overall efficiency in water supply, along with other forms of utilities.
US 2005246112 includes a system which provides a model of a pipeline using, for example, different deterministic models. The pipeline is simulated using parameters gathered using multiple sensors and leak detection can rely on an assumption of the structure of the pipeline.
The system of US 6668619 is based on the use of a pattern match filter. The system compares a current audio sample with a list of leak noise samples to determine likelihood of a leak. The requirement for a list of previously obtained samples leads to a lack of flexibility in such a system and can also provide false positives in systems with large variations in environmental noise.
In other available leak detection methods, also multiple sensors are used. Data is sent to a base station and successively analysed. Data from multiple sensors is used to find a leak. The requirement for multiple types of sensors and transmission and analysis of data from a remote location can lead to an extended detection time, leading to a delay in repairs and requires a larger overhead.
It is therefore desirable to optimise the detection of leak events which may provide for a more effective remote management of utilities networks.
Summary of the invention
The present invention provides a leak detection system, particularly to a leak detection system for use in remote management of utilities networks. With the presented leak detection system, leaks can be detected in pipelines for fluid or gaseous materials.
In accordance with a first aspect of the present invention, there is provided a method for detecting a leak in a pipeline, the method comprising the steps of:
a) obtaining acoustic data from a location using an audio probe;
b) computing an autocorrelation of the acoustic data;
c) carrying out a frequency analysis on the autocorrelation and identifying persistent frequencies; and
d) using the persistent frequencies to detect a leak.
In many currently available technologies, obtaining signals for leak detection requires a stimulus, and the required signal is often detected in the form of a reflected signal. Preferably obtaining the acoustic data in step a) does not require a stimulus. More preferably obtaining the acoustic data in step a) requires passive listening using the audio probe. Still more preferably the acoustic data is not received at the audio probe from a dedicated signal reflector.
In many currently available technologies, when attempting to detect a leak in a pipeline network, the structure of the pipeline network is modelled, in some cases using deterministic models. Using these currently available technologies, the pipeline is normally simulated using parameters gathered using sensors along, proximate to, and/or remote to the pipeline system. Preferably in the method of the first aspect, the persistent frequencies are preferably used to detect a leak without any assumption or modelling of how a pipeline is structured.
In currently available technology, many systems use pattern match filtering by comparing a detected signal with one or more previously known signals that are associated with a leak. In such a way the said currently available technology relies on legacy data to detect a leak. Preferably the method of the first aspect provides a real-time detection of a leak without the need for such legacy information. The acoustic data of the present method permits the detection of a leak using autocorrelation.
In currently available technology, many available systems require the transmission of data and/or information detected by a probe to a remote location in order to detect a leak. Preferably, the method of the present invention does not require the transmission of the acoustic data or the persistent frequencies in order to detect a leak. In currently available technology, a plurality of sensors and/or probes are used. Preferably, the method of the first aspect utilises a single audio probe for step a).
In many currently available methods, multiple sensors are required, detecting a variety of signal types in order to inform on the presence of a leak. The first aspect of the present invention preferably uses only acoustic data to detect a leak.
Preferably, the pipeline is comprised within a utilities management network. Preferably the leak is a fluid leak. Preferably the leak is a water leak.
Preferably, between steps a) and b), a sampling of the acoustic data is performed at a known sampling frequency. In embodiments wherein sampling of the acoustic data is performed, providing sampled acoustic data, the sampled acoustic data is used in place of the acoustic data in subsequent steps.
More preferably, the sampling returns a number of samples selected from the range: 2,000 samples to 10,000 samples. Still more preferably, the sampling frequency is selected from the range 10,000 Hz to 50,000 Hz. Still more preferably, a Fast Fourier Transform is applied to the acquired samples, the Fast Fourier Transform providing a complex frequency spectrum.
Still more preferably, a frequency spectrum of the autocorrelation of the acoustic data is obtained by multiplying the Fast Fourier Transform result by its complex conjugate. Still more preferably, an Inverse Fast Fourier Transform is applied to the frequency spectrum, the Inverse Fast Fourier Transform extracting the autocorrelation of the acoustic data.
Preferably the autocorrelation is used as a reference signal in a Least Mean Square digital filter. More preferably, the acoustic data is filtered using the Least Mean Square digital filter to isolate leak noise.
The frequency spectrum of the autocorrelation can further be divided by the number of samples, whereby a frequency composition, the power spectral density (PSD), is obtained. The frequency analysis can further comprise a normalisation, whereby the power spectral density can then further be transformed so that it satisfies the requirement of a probability density function.
In mathematical terms, this relates to the integral of the power spectral density must be equal to one. More preferably, the frequency analysis further comprises a spectral entropy calculation, the spectral entropy calculation comprising the probability density function. Still more preferably, the acoustic data is analysed to obtain level and spread information. Still more preferably, the spectral entropy is complemented with the level and spread information to detect a leak.
Preferably step a) includes storing the acoustic data. More preferably the acoustic data is stored in a local memory and/or on a remote data server. The processing of the acoustic data through steps b), c) and d) can be executed at the time of the data acquisition or at a delayed point in time[FPi]. Preferably step b) includes storing the autocorrelation. More preferably the autocorrelation is stored in a memory. Preferably step c) includes storing the persistent frequencies. More preferably the persistent frequencies are stored in a memory. Preferably the audio probe comprises one selected from the range: R-Mic, DXmic handprobe, Leak Noise Sensor (LNS).
In accordance with a second aspect ofthe present invention there is provided a leak detection computer program product including a program for a leak detection apparatus, comprising software code portions for performing the steps of the method according to the first aspect of the present invention, when the program is run on the leak detection apparatus.
Preferably the computer program product comprises a computer-readable medium on which the software code portions are stored, wherein the program is directly loadable into an internal memory of the leak detection apparatus.
In accordance with a third aspect of the present invention, there is provided a leak detection apparatus for detecting leaks using acoustic data, the leak detection apparatus comprising, a processor, a memory, and an audio probe; the leak detection apparatus arranged to process the leak detection computer program product according to the second aspect of the present invention.
Detailed Description
Specific exemplary embodiments will now be described by way of example only, and with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of an exemplary method of the present invention, and
FIG. 2 is a data flow diagram of an exemplary method of the present invention.
With reference to FIG. 1, there is provided a flow chart of a method 10 according to the first example of the present invention. The method can comprise the following steps:
a) obtaining acoustic data from a first location proximate to a utilities management pipeline using an audio probe 12;
b) performing sampling of the acoustic data at a known sampling frequency 14;
c) performing a Fast Fourier Transform on the samples to give a Fast Fourier Transform result 16;
d) obtaining a frequency spectrum of the autocorrelation of the acoustic data by multiplying the Fast Fourier Transform result by its complex conjugate 18;
e) extracting the autocorrelation of the acoustic data by performing an Inverse Fast Fourier Transform on the frequency spectrum 20;
f) filtering the acoustic data using the Least Mean Square digital filter to isolate leak noise, using the autocorrelation as a reference signal in a Least Mean Square digital filter 22;
g) normalising the frequency composition providing a probability density function, and using the probability density function to determine spectral entropy 24;
h) analysing the acoustic data to obtain level and spread information 26; and
i) using the spectral entropy and the level and spread information to detect a leak
28.
In use, the method is performed using a leak detection apparatus according to the third aspect of the present invention arranged to run a leak detection computer program according to the second aspect of the present invention.
In the example embodiment described and shown in FIG. 1, a leak detection apparatus is used, the leak detection apparatus comprising an audio probe, a memory and a processor. The audio probe is used to obtain acoustic data from a first location, the first location being proximate to a utilities management pipeline. The acoustic data comprises a continuous audio signal, which is then stored on the memory of the leak detection apparatus.
Sampling is performed on the acoustic data, the sampling comprising. In one embodiment the sampling provides 4096 samples at 44,117 Hz. Fast Fourier transform is applied to the samples yielding a fast Fourier transform result, which is a complex frequency spectrum. A frequency spectrum of an autocorrelation of the acoustic data is obtained by multiplying the Fast Fourier Transform result by its complex conjugate.
Further details are discussed in the following in reference to FIG. 2. The left hand side elements of the flow diagram of Figure 2 relate to method steps undertaken, the central portion of the flow diagram contains data features and the flow chart features on the right hand side details the application or summary results. From the pipe with a leak, the acoustic data is obtained in step 12 by means of a probe. This probe can be one of R-Mic, DXmic Hand Probe, Leak Noise Sensor. The result of this operation is an analogue signal.
The analogue signal is then sampled in step 14 with sampling period Ts, the result of this operation is sampled data in the form of a digital signal x[t] (with t = nxTs, n e N) which represents the acoustic noise.
The sampled data can be analysed in step 26 to obtain level and spread information.
In step 16 a Fast Fourier Transformation (FFT) is performed on the sampled data, which is digital data: X[f] = FFT(x[t]'). The sampling frequency and the quantity of samples determine the observable bandwidth and the resolution of the resulting frequency spectrum. According to the Nyquist theorem, the bandwidth BW/that can be reconstructed without aliasing from a signal that is sampled with sampled frequency fs is defined as follows: BW <
Further, the resolution fem of an FFT that is performed on a digital signal consisting in N samples is: fBin =
In the present exemplary embodiment, as a calculation example, 1024 samples at can be collected at 11029 Hz which allow the observation of a frequency up to 5kHz with a resolution of approximately 11 Hz. To improve the quality of the sampled signal anti-aliasing filters can to be used to limit the bandwidth of the observed signal.
The result of the FFT is the complex spectrum X[f](with f = n x fBin, n e KT).
In step 18, the complex spectrum is multiplied with its own complex conjugate. Thereby the Autocorrelation Frequency Spectrum is obtained. A[f] = X[f] x X[f]
The autocorrelation is obtained in step 20 by transforming the Autocorrelation Frequency Spectrum with an inverse FFT: a[t] = tFFT(A[f])
The obtained autocorrelation can be filtered in step 22 in a Least Mean Square (LMS) filter to obtain isolated leak noise. The autocorrelation is used as a reference signal in the Least Mean Square digital filter to isolate persistent frequencies, thus enhancing the leak noise. Thereby leak noise can be isolated. The LMS filter tracks and isolates persistent components of the measured signal, and the leak noise is contained in these persistent components.
In step 24 the Autocorrelation Frequency Spectrum is divided by the number of samples,
ΑΓ/Ί thereby the Power spectral density (PSD) of the autocorrelation is obtained: P[f] =
Further, the PDS is normalized to fit the requirements for a probability function pt = // Finally, the Spectral Entropy is obtained as follows: PSE = -ΣίΡίΧ ln(pi)
The Spectral Entropy and the level and spread information can be used in step 28 to detect a leak in the pipeline.
Using the method of the present invention, acoustic data is used to identify recurring patterns, the recurring patterns potentially indicating the presence of a noise originating from a persistent source, such as a leak. Autocorrelation, performed on the acoustic data, is preferably used to indicate peaks at frequencies similar to the detected pattern frequency.
It is possible to use the method of the embodiment shown to provide first leak detection data at a first location and second leak detection data at a second location by carrying out steps of the method of the invention at both the first location and a second location respectively. In such a way, using the method of the present invention, the persistent frequencies at a first location and a second location can be used to increase the confidence of the result. The determination whether there is a leak or not can thereby be improved.
It will be appreciated that the above described embodiments are given by way of example only and that various modifications thereto may be made without departing from the scope of the invention as defined in the appended claims. For example, the resolution of the sampling used in the sampling of the embodiment shown is measured in Hz. Additional embodiments will be appreciated wherein a higher sampling resolution is used having a sub-Hz resolution.
Claims (17)
1. A method for detecting a leak in a pipeline, the method comprising steps of:
a) obtaining acoustic data from a location using an audio probe;
b) computing an autocorrelation of the acoustic data;
c) carrying out a frequency analysis on the autocorrelation and identifying persistent frequencies; and
d) using the persistent frequencies to detect a leak.
2. A method as claimed in claim 1, wherein the pipeline is comprised within a utilities management network, and wherein the leak is a fluid leak, a water leak or a gas leak.
3. A method as claimed in any one of the preceding claims, wherein between steps a) and b), a sampling of the acoustic data is performed at a known sampling frequency.
4. A method as claimed in any one of the preceding claims, wherein a Fast Fourier Transform is applied to the acquired samples, the Fast Fourier Transform providing a complex frequency spectrum.
5. A method as claimed in claim 4, wherein a frequency spectrum of the autocorrelation is obtained by multiplying the complex frequency spectrum by its complex conjugate.
6. A method as claimed in claim 5, wherein an Inverse Fast Fourier Transform is applied to the frequency spectrum, the Inverse Fast Fourier Transform extracting the autocorrelation of the acoustic data.
7. A method as claimed in any one of the preceding claims, wherein the autocorrelation is used as a reference signal in a Least Mean Square digital filter to obtain isolated leak noise.
8. A method as claimed in any one of claims 5 to 7, wherein the method further comprises dividing the frequency spectrum of the autocorrelation by the number of samples, whereby a power spectral density is obtained.
9. A method as claimed in claim 8, wherein the method further comprises normalising the power spectral density, whereby a probability density function is obtained.
10. A method as claimed in claim 9, wherein the method further comprises a spectral entropy calculation, the spectral entropy calculation comprising the probability density function.
11. A method as claimed in any one of the preceding claims, wherein the method further comprises analysing the acoustic data to obtain level and spread information.
12. A method as claimed in claim 11, wherein the method further comprises complementing the power spectral density with the level and spread information to detect a leak.
13. A method as claimed in any one of the preceding claims, wherein step a) includes storing the acoustic data, step b) includes storing the autocorrelation, and/or step c) includes storing the persistent frequencies.
14. A method as claimed in any one of the preceding claims, wherein the audio probe comprises one selected from the range: R-Mic, DXmic handprobe, and Leak Noise Sensor.
15. A computer program product including a program for a processing device, comprising software code portions for performing the steps of any one of claims 1 to 14 when the program is run on the processing device.
16. The computer program product according to claim 15, wherein the computer program product comprises a computer-readable medium on which the software code portions are stored, wherein the program is directly loadable into an internal memory of the processing device.
17. A leak detection apparatus for detecting leaks using acoustic data, the leak detection apparatus comprising a processor, a memory, and an audio probe; and the leak detection apparatus arranged to process the computer program product as claimed in claim 15 or 16.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GB1811797.8A GB2575669A (en) | 2018-07-19 | 2018-07-19 | Leak detection apparatus and method |
| PCT/GB2019/052022 WO2020016595A1 (en) | 2018-07-19 | 2019-07-19 | Leak detection apparatus and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GB1811797.8A GB2575669A (en) | 2018-07-19 | 2018-07-19 | Leak detection apparatus and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| GB201811797D0 GB201811797D0 (en) | 2018-09-05 |
| GB2575669A true GB2575669A (en) | 2020-01-22 |
Family
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| GB1811797.8A Withdrawn GB2575669A (en) | 2018-07-19 | 2018-07-19 | Leak detection apparatus and method |
Country Status (2)
| Country | Link |
|---|---|
| GB (1) | GB2575669A (en) |
| WO (1) | WO2020016595A1 (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115420247B (en) * | 2022-11-03 | 2023-01-06 | 核工业北京地质研究院 | Method and Experimental System for Determining Shape and Area of Vacuum Leakage Hole |
| CN118998642B (en) * | 2024-10-23 | 2025-01-03 | 安徽至博光电科技股份有限公司 | Pipeline leakage monitoring and early warning system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| JPS63394A (en) * | 1986-06-04 | 1988-01-05 | ユニリ−バ− ナ−ムロ−ゼ ベンノ−トシヤ−プ | Fractionation of fat compound |
| JPH0587669A (en) * | 1991-09-25 | 1993-04-06 | Nikko Kyodo Co Ltd | Pipe-leakage inspecting method |
| WO2000079425A1 (en) * | 1999-06-22 | 2000-12-28 | Thames Water Utilities Limited | Correlation analysis in the phase domain |
| JP3488579B2 (en) * | 1996-09-06 | 2004-01-19 | 東京都 | Water leak position detecting method and water leak position detecting device |
| JP2008051776A (en) * | 2006-08-28 | 2008-03-06 | Toshiba Corp | Water leakage detection device and water leakage detection method |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE3045660C2 (en) | 1980-12-04 | 1982-10-14 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V., 8000 München | Method and device for locating leaks in pipelines |
| US6389881B1 (en) | 1999-05-27 | 2002-05-21 | Acoustic Systems, Inc. | Method and apparatus for pattern match filtering for real time acoustic pipeline leak detection and location |
| US6970808B2 (en) | 2004-04-29 | 2005-11-29 | Kingsley E. Abhulimen | Realtime computer assisted leak detection/location reporting and inventory loss monitoring system of pipeline network systems |
| WO2008150411A1 (en) | 2007-05-29 | 2008-12-11 | Hadronex, Llc | Leak detection system and method |
| JP6790086B2 (en) * | 2016-05-19 | 2020-11-25 | 株式会社東芝 | Leakage determination device and leak determination method |
-
2018
- 2018-07-19 GB GB1811797.8A patent/GB2575669A/en not_active Withdrawn
-
2019
- 2019-07-19 WO PCT/GB2019/052022 patent/WO2020016595A1/en not_active Ceased
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS63394A (en) * | 1986-06-04 | 1988-01-05 | ユニリ−バ− ナ−ムロ−ゼ ベンノ−トシヤ−プ | Fractionation of fat compound |
| JPH0587669A (en) * | 1991-09-25 | 1993-04-06 | Nikko Kyodo Co Ltd | Pipe-leakage inspecting method |
| JP3488579B2 (en) * | 1996-09-06 | 2004-01-19 | 東京都 | Water leak position detecting method and water leak position detecting device |
| WO2000079425A1 (en) * | 1999-06-22 | 2000-12-28 | Thames Water Utilities Limited | Correlation analysis in the phase domain |
| JP2008051776A (en) * | 2006-08-28 | 2008-03-06 | Toshiba Corp | Water leakage detection device and water leakage detection method |
Non-Patent Citations (1)
| Title |
|---|
| "On the selection of acoustic/vibration sensors for leak detection in plastic water pipes", Y Goa et al; Journal of Sound and Vibration, 283(3), May 2005, pages 927 - 941 * |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2020016595A1 (en) | 2020-01-23 |
| GB201811797D0 (en) | 2018-09-05 |
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