A method and device for identifying water supply pipeline leakage based on active low-frequency acoustic wave excitation

Abstract

The present invention discloses a method for identifying water supply pipeline leakage based on active low-frequency acoustic wave excitation, comprising: acquiring acoustic signals in the pipeline by arranging low-frequency acoustic emission transducers and hydrophones at both ends of the pipeline, and generating corresponding leakage point locations based on a pre-constructed acoustic wave equation; processing the acoustic signals to obtain corresponding time-frequency images, and labeling the time-frequency images according to whether they are leakage signals, and forming a data set with the signal spectrum, leakage point locations, and labels; constructing a recognition network; using the data set to train the recognition network to obtain a water supply pipeline leakage recognition model for identifying whether a pipeline is leaking; inputting the acoustic signals in the pipeline to be identified into the water supply pipeline leakage recognition model to output recognition results of whether the pipeline is leaking and the location of the leakage point. The present invention also provides a water supply pipeline leakage recognition device. The method provided by the present invention can effectively improve the accuracy of leakage recognition.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban water supply pipe networks, and in particular relates to a method and device for identifying water supply pipe leakage based on active low-frequency acoustic wave excitation. Background Art

[0002] Leakage in urban water supply pipelines is becoming increasingly serious, posing challenges to water supply management and the safety of drinking water for residents. According to the Ministry of Housing and Urban-Rural Development, 8 billion tons of water leaks nationwide annually, with leakage rates in cities generally ranging from 10% to 15%.

[0003] Common methods for identifying water pipeline leaks include acoustic detection, hydraulic detection, radar detection, and robotic endoscopic detection. Acoustic leak detection technology has become mainstream due to its non-invasive nature, high sensitivity, and low cost. Currently, all commercially available acoustic leak detection technologies are passive acoustic methods, passively sensing the acoustic signal at the leak source. Firstly, because the acoustic signal at the leak point typically has a low signal-to-noise ratio, making it difficult to distinguish between the signal and background noise, and due to pipeline damping, the signal decays rapidly. To achieve effective leak diagnosis, existing sensors are typically spaced no more than 200 meters apart, resulting in high investment costs due to the large number of sensors installed. Furthermore, the spacing of sensors is limited by the distribution of manholes, making it often difficult to achieve the required spacing of less than 200 meters between sensors. Because passive acoustic methods relying on sensors have far lower-than-expected performance in practical applications, manual leak detection using listening rods remains the primary detection method for water utilities. However, this traditional passive acoustic leak detection technology relies on the experience of leak detectors and is labor-intensive and time-consuming. Therefore, there is an urgent need to develop new leak diagnosis methods to address the limitations of current technologies.

[0004] Patent document CN103521424A discloses a low-frequency acoustic wave excitation device, including a liquid-filled pipe, a wall opening of the liquid-filled pipe connected to an outer closed cavity through a connecting cavity, the shells of the cavity and the connecting cavity are fastened and sealed, the connected cavity and the connecting cavity form a measuring cavity, the excitation piston is sealedly connected to the inner wall of the measuring cavity, the excitation piston is fixed to the piston rod, the piston rod passes through the cavity and is slidably connected to the cavity, the shell of the cavity and the piston rod are sealed, the exciter drives the excitation piston to slide in the measuring cavity through the piston rod, an air inlet is provided on the cavity, the excitation piston divides the measuring cavity into an upper cavity and a lower cavity, the air inlet is connected to the upper cavity, and an accelerometer is further arranged on the connecting surface between the hollow piston rod and the excitation piston.

[0005] Patent document CN118309943A discloses a monitoring device for a heating pipe network, which includes: an insulated collection component, a sensor, a data collection component and a server; the insulated collection component is filled with air and is used to collect the acoustic wave signal of the heating pipe network and transmit the acoustic wave signal of the heating pipe network to the sensor; the sensor is used to receive the acoustic wave signal of the heating pipe network, convert the acoustic wave signal into an electrical signal, and then send the electrical signal to the data collection component; the data collection component is used to convert the electrical signal into a digital signal and send the data signal to the server; the server is used to determine whether the heating pipe network has a leak based on the digital signal. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and device for identifying water supply pipeline leakage based on active low-frequency acoustic wave excitation, which can effectively improve the accuracy of leakage identification.

[0007] To achieve the first objective of the present invention, the following technical solution is provided: a method for identifying water supply pipeline leakage based on active low-frequency acoustic wave excitation, comprising: acquiring acoustic signals in the pipeline by low-frequency acoustic emission transducers and hydrophones arranged at both ends of the pipeline, and generating corresponding leakage point locations based on a pre-constructed acoustic wave equation;

[0008] Based on the time axis, the acquired acoustic signal is processed using the STFT analysis method to obtain the corresponding time-frequency image, and the time-frequency image is labeled according to whether it is a leakage signal. The signal spectrum, leakage point location and label form a data set;

[0009] Constructing a recognition network, wherein the recognition network includes a feature extraction module, a fusion module, and an analysis module;

[0010] The feature extraction module is used to extract time domain features and frequency domain features from the input signal spectrum;

[0011] The fusion module is used to fuse the time domain features and the frequency domain features to output fusion features;

[0012] The analysis module performs analysis based on the fusion features to obtain a recognition result;

[0013] Using the data set to train a recognition network to obtain a water supply pipeline leakage recognition model for identifying whether a pipeline is leaking;

[0014] The acoustic signal in the pipeline to be identified is input into the water supply pipeline leakage identification model to output the identification results of whether the pipeline is leaking and the location of the leakage point.

[0015] The present invention utilizes the advantages of slow attenuation of low-frequency excitation sound wave signals and the presence of plane waves in the pipe to overcome the shortcoming of a small monitoring range of a passive acoustic leakage identification method.

[0016] Specifically, the low-frequency acoustic emission transducer generates sound waves with optimal excitation frequency and intensity based on the pipe material, pipe diameter, wall thickness, water pressure in the pipe, flow velocity in the pipe, and soil cover depth of the target pipe. The sound waves propagate forward and backward along the pipe.

[0017] Specifically, the acoustic wave equation includes the equation of motion, the continuity equation and the equation of state.

[0018] Specifically, the expression of the acoustic wave equation is as follows: ; ; ; Where, ρ Liquid density; v particle velocity; p sound pressure; t time; c Speed ​​of sound waves; P pressure; ω angular velocity; k i and k r is the wave number; x 、 y and z is the spatial direction; i incident wave; r Reflected wave.

[0019] Specifically, the recognition network is constructed based on the pre-trained neural network model SqueezeNet framework and the convolutional neural network model framework.

[0020] Specifically, the STFT analysis method uses short-time Fourier transform to analyze the input sound signal segment by segment to output a corresponding frequency spectrum.

[0021] Specifically, the expression of the short-time Fourier transform is as follows: Where, t For time, is the window function, is the frequency, It is used for signal The time variable to be windowed and Fourier transformed.

[0022] Specifically, the low-frequency acoustic emission transducer is arranged in a fire hydrant at one end of the pipeline.

[0023] In order to achieve the second object of the present invention, the following technical solution is provided: steps for executing the above-mentioned water supply pipeline leakage identification method based on active low-frequency acoustic wave excitation.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention acquires the acoustic signal in the pipeline by setting a low-frequency acoustic emission transducer, and determines whether the pipeline is leaking and the location based on a preset model that has been constructed. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flow chart of the water supply pipeline leakage identification method based on active low-frequency acoustic wave excitation provided in this embodiment;

[0027] Figure 2 A schematic diagram of the water supply pipeline leakage identification device provided in this embodiment;

[0028] Figure 3 Installation layout diagram of the low-frequency acoustic emission transducer provided in this embodiment;

[0029] In the figure, 1. Low-frequency acoustic emission transducer; 2. Near-end fire hydrant; 3. Far-end fire hydrant; 4. Hydrophone; 5. GPS equipment; 6. Near-end fire hydrant control valve; 7. Far-end fire hydrant control valve; 8. Computer; 9. Water supply pipe; 10. Leakage point; 11. Fire hydrant; 12. Flange; 13. Valve; 14. Water outlet pipe; 15. Skid-mounted casing. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] like Figure 1 As shown in FIG, the water supply pipeline leakage identification method provided by this embodiment has the following specific steps:

[0032] Obtain a training data set for the machine learning model: the active low-frequency sound waves and the sound wave reflection coefficient, transmission coefficient and leakage coefficient under different leakage system characteristics are affected by the pipeline characteristics, the hydraulic characteristics of the water flow in the pipe, and the characteristics of the soil covering the outside of the pipe. By conducting thousands of experiments, the experimental variables involve the sound source frequency, the sound source sound pressure level, the size of the erosion cavity, the pipe diameter, the pipe material, the soil covering height, the sand-soil ratio, the water pressure in the pipe, the shape of the leak, the area of ​​the leak and the direction of the leak. The experiment also includes non-leakage conditions and leakage conditions, with a ratio of about 3:7, to jointly construct a data set between the water-pipe-soil three-phase leakage system and the low-frequency excitation sound wave echo in the pipe.

[0033] The signal data collected by a hydrophone is the amplitude information of a specific duration, which may contain various interference noises and leakage status information. The signal itself will vary depending on the leakage situation, pipeline, and environmental conditions. The method of the present invention combines the time-domain and frequency-domain characteristics of the acoustic signal during feature extraction, extracting nonlinear features that describe the non-stationary nature of the leakage signal. It also applies signal time-frequency analysis methods to depict how the signal frequency distribution changes over time, providing effective data support for the automatic identification of leakage signals in water supply networks.

[0034] STFT is a simple time-frequency analysis tool. Its principle is to use a window function of appropriate width to divide the signal into several small segments in time, perform Fourier transform on each segment of data, and shift the window function on the time axis to analyze the signal segment by segment, and combine them to obtain the spectrum of the original signal.

[0035] For a continuous signal in the time domain , its short-time Fourier transform is defined as: The expression of short-time Fourier transform is as follows: Where, t For time, is the window function, is the frequency, It is used for signal The time variable to be windowed and Fourier transformed.

[0036] Using STFT to obtain the time-frequency image of the signal requires determining several important parameters, including the window function, fast Fourier transform length, frame length, and frame overlap length.

[0037] Training a water supply pipeline leakage identification model based on active low-frequency acoustic wave excitation: A dataset consisting of various leakage signals and non-leakage signals collected experimentally is used to train the machine learning model and verify its identification effect.

[0038] The pre-trained neural network model SqueezeNet was used to classify time-frequency images of different signals. SqueezeNet is a lightweight convolutional neural network with up to 18 deep learning layers, capable of classifying images into 1,000 different categories. The training objective was to determine whether a signal was leaking. The dataset consisted of only two types of data: leakage signals, labeled "1," and non-leakage signals, labeled "0." These two signals, combined, constituted the dataset and were used to train the machine learning model and verify its recognition performance. The training process was supervised, meaning that the signal labels were also included as input to the model.

[0039] The specific training process is as follows:

[0040] (1) Use signal spectrum images to train the SqueezeNet model;

[0041] (2) Use the trained models to identify and apply them to the validation set, and modify the hyperparameters of the corresponding models based on the application results to achieve better performance on the validation set;

[0042] (3) The test set is input into the trained model to determine whether the signal is leaked and obtain the recognition result.

[0043] To verify the accuracy of the leakage identification model in this method, 20 sets of experiments were conducted to test its performance. These tests included 15 leakage conditions and 5 non-leakage conditions. Furthermore, the experiments considered different weather conditions: 11 sunny days, 5 light rain days, and 4 heavy rain days. The experimental results showed that the model achieved a leakage identification accuracy of 75% under heavy rain conditions, while the accuracy reached 100% under both sunny and light rain conditions.

[0044] like Figure 2 As shown, this embodiment also provides a water supply pipeline leakage identification device, which is used to execute the steps of the water supply pipeline leakage identification method provided in the above embodiment.

[0045] The original pipeline includes a water supply pipeline 9 and a leakage point 10, a proximal fire hydrant 2 and a proximal fire hydrant control valve 6, a distal fire hydrant 3 and a distal fire hydrant control valve 7.

[0046] The method provided in this embodiment adds equipment to the existing pipeline, including a skid-mounted low-frequency acoustic emission transducer 1 and a hydrophone 4 for acoustic signal monitoring. The hydrophone 4 transmits the collected acoustic signal data to a remote computer 8 via a GPS device 5. The presence of a leak 10 will change the characteristics of the acoustic signal collected by the hydrophone.

[0047] like Figure 3The figure shows the connection method between the low-frequency acoustic emission transducer and the fire hydrant provided in this embodiment. The low-frequency acoustic emission transducer 1 is built into a skid-mounted housing 15, and the power supply cable passes through the skid-mounted housing 15 to ensure sealing performance. The low-frequency acoustic emission transducer 16 and the skid-mounted housing 15 are integrated and arranged on the water supply pipeline to replace the fire hydrant. The installation process includes: first, closing the water outlet control valve 13 of the fire hydrant 11, then removing the fire hydrant flange 12, then connecting the flange to the skid-mounted housing, and finally opening the fire hydrant outlet control valve 13 to ensure smooth water flow.

[0048] Because low-frequency sound waves appear as pure plane waves within water supply pipes, they experience minimal attenuation when propagating through water and can travel longer distances within the pipes. Furthermore, the plane waves formed by low-frequency sound waves maintain good directionality, resulting in high efficiency and stability when used to transmit information.

[0049] The device provided in this embodiment is based on the special scattering phenomenon that occurs when low-frequency sound waves encounter a leaking structure. Its principle is to establish a connection between the changes in sound pressure in space and time. This connection is established based on the equation of motion, the equation of continuity and the equation of state.

[0050] The specific formula is as follows: ; ; Where, ρ Liquid density; v particle velocity; p sound pressure; t time; c Speed ​​of sound waves; P pressure; ω angular velocity; k i and k r is the wave number; x 、 y and z is the spatial direction; i incident wave; r Reflected wave.

[0051] Due to the various parameters of sound waves in the water supply pipe p 、 v 、 And their changes in space and time are all tiny, so we can ignore the second-order and above trace quantities to obtain the linear acoustic wave equation and solve the equation.

[0052] Low-frequency sound waves propagate in a bounded pipe. When they encounter a leak, a reflected wave is generated in the pipe. , which will also produce transmitted waves , there will be leakage waves Out from the leak hole.

[0053] At this time, the sound pressure at any point in the tube is is the superposition of the incident wave and the reflected wave: The three-phase leakage system of water, pipe, and soil is the primary cause of low-frequency sound wave scattering. Therefore, the magnitude of reflected, transmitted, and outgoing waves is not only related to the extent of the leakage and the characteristics of the low-frequency sound wave, but also closely related to the acoustic impedance of the erosion cavity outside the leak.

[0054] In addition, the terms "upper", "lower", "inner", "outer", "front", and "back" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the present invention.

[0055] Of course, the above description is only a specific embodiment of the present invention and is not intended to limit the scope of implementation of the present invention. Any equivalent changes or modifications made based on the structure, features and principles described in the scope of the patent application of the present invention should be included in the scope of the patent application of the present invention.

[0056] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A water supply pipeline leakage identification method based on active low-frequency acoustic wave excitation, characterized in that: include: Acoustic signals in the pipeline are acquired by low-frequency acoustic emission transducers and hydrophones arranged at both ends of the pipeline, and the corresponding leakage point location is generated based on a pre-constructed acoustic wave equation; the acoustic wave equation includes the equation of motion, the equation of continuity, and the equation of state; The expression of the acoustic wave equation is as follows: ; ; ; Where, ρ Liquid density; v particle velocity; p sound pressure; t time; c Speed ​​of sound waves; P pressure; ω angular velocity; k i and k r is the wave number; x 、 y and z is the spatial direction; i incident wave; r Reflected wave; Based on the time axis direction, the acquired acoustic signal is processed using the STFT analysis method to obtain the corresponding time-frequency image, and the time-frequency image is labeled according to whether it is a leakage signal, and the signal spectrum, leakage point location and label are combined into a data set; Constructing a recognition network, wherein the recognition network includes a feature extraction module, a fusion module, and an analysis module; The feature extraction module is used to extract time domain features and frequency domain features from the input signal spectrum; The fusion module is used to fuse the time domain features and the frequency domain features to output fusion features; The analysis module performs analysis based on the fusion features to obtain a recognition result; Using the data set to train a recognition network to obtain a water supply pipeline leakage recognition model for identifying whether a pipeline is leaking; The acoustic signal in the pipeline to be identified is input into the water supply pipeline leakage identification model to output the identification results of whether the pipeline is leaking and the location of the leakage point.

2. The water supply pipeline leakage identification method based on active low-frequency acoustic wave excitation according to claim 1 is characterized in that: The low-frequency acoustic emission transducer generates sound waves with optimal excitation frequency and intensity based on the pipe material, pipe diameter, wall thickness, water pressure in the pipe, flow rate in the pipe, and buried depth of the soil of the target pipe. The sound waves propagate forward and backward along the pipe.

3. The water supply pipeline leakage identification method based on active low-frequency acoustic wave excitation according to claim 1 is characterized in that: The recognition network is constructed based on the pre-trained neural network model SqueezeNet framework and the convolutional neural network model framework.

4. The water supply pipeline leakage identification method based on active low-frequency acoustic wave excitation according to claim 1 is characterized in that: The STFT analysis method uses short-time Fourier transform to analyze the input acoustic signal segment by segment to output the corresponding frequency spectrum.

5. The method for identifying water supply pipeline leakage based on active low-frequency acoustic wave excitation according to claim 4 is characterized in that: The expression of the short-time Fourier transform is as follows: Where, t For time, is the window function, is the frequency, It is used for signal The time variable to be windowed and Fourier transformed.

6. The water supply pipeline leakage identification method based on active low-frequency acoustic wave excitation according to claim 1 is characterized in that: The low-frequency acoustic emission transducer is arranged in a fire hydrant at one end of the pipeline.

7. A water supply pipeline leakage identification device, characterized in that: Used to execute the steps of the water supply pipeline leakage identification method based on active low-frequency acoustic wave excitation as described in any one of claims 1 to 6.

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