CN113670531B - Method and system for detecting leakage of water supply pipeline by using multi-probe array based on phase and amplitude attenuation - Google Patents
Method and system for detecting leakage of water supply pipeline by using multi-probe array based on phase and amplitude attenuation Download PDFInfo
- Publication number
- CN113670531B CN113670531B CN202111069319.6A CN202111069319A CN113670531B CN 113670531 B CN113670531 B CN 113670531B CN 202111069319 A CN202111069319 A CN 202111069319A CN 113670531 B CN113670531 B CN 113670531B
- Authority
- CN
- China
- Prior art keywords
- phase
- pipeline
- sensor
- signals
- amplitude attenuation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 45
- 239000000523 sample Substances 0.000 title claims abstract description 38
- 239000013598 vector Substances 0.000 claims abstract description 37
- 238000001514 detection method Methods 0.000 claims abstract description 33
- 238000001228 spectrum Methods 0.000 claims abstract description 27
- 238000013528 artificial neural network Methods 0.000 claims abstract description 19
- 238000012546 transfer Methods 0.000 claims description 43
- 230000008569 process Effects 0.000 claims description 12
- 238000010276 construction Methods 0.000 claims description 9
- 238000000605 extraction Methods 0.000 claims description 7
- 230000003595 spectral effect Effects 0.000 claims description 6
- 230000002123 temporal effect Effects 0.000 claims description 6
- 230000009466 transformation Effects 0.000 claims description 6
- 238000009933 burial Methods 0.000 claims description 4
- 229910000831 Steel Inorganic materials 0.000 claims description 3
- 238000009432 framing Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 239000002689 soil Substances 0.000 claims description 3
- 239000010959 steel Substances 0.000 claims description 3
- 239000008400 supply water Substances 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 210000004013 groin Anatomy 0.000 claims 1
- 238000012545 processing Methods 0.000 abstract description 4
- 230000006870 function Effects 0.000 description 32
- 238000004364 calculation method Methods 0.000 description 2
- 238000010219 correlation analysis Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Classifications
-
- 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
Abstract
A method and a system for detecting leakage of a water supply pipeline by a multi-probe array based on phase and amplitude attenuation belong to the field of leakage detection of the water supply pipeline. The invention solves the problems of limited operation conditions, low detection precision and low detection efficiency of the traditional acoustic leak detection method in the aspect of leak detection of the water supply network. According to the invention, a movable multi-probe acoustic sensor is laid on the ground, ground vibration noise signals are collected through a specific sensor arrangement mode to be used as acoustic signals for detecting whether leakage occurs in a pipeline, signal processing is carried out through fast Fourier transform, after phase spectrum characteristics and amplitude spectrum characteristics of the acoustic signals are obtained, characteristic vectors are constructed based on the obtained characteristics, and the constructed characteristic vectors are input into a BP neural network to obtain leakage detection results of the pipeline to be detected. The invention can be applied to leakage detection of water supply pipelines.
Description
Technical Field
The invention belongs to the field of water supply pipeline leakage detection, and particularly relates to a method and a system for detecting water supply pipeline leakage by a multi-probe array based on phase and amplitude attenuation.
Background
The urban water supply network is an important municipal field related to national living health level and national economy, but at present, partial domestic cities still have the problems of old water supply facilities, serious pipe network aging, slow updating speed and even lost pipe network embedded positions, and leakage accidents occur, so that a large amount of treated clean water resources are lost and wasted, and huge losses are caused to the national economy. At present, the leakage detection methods adopted at home and abroad are various in variety, and the acoustic leakage detection method is generally applied. Acoustic leak detection methods can be broadly divided into hardware-based leak detection methods and software-based leak detection methods. The method for detecting leakage based on hardware is mainly an acoustic leakage detection method based on a pipeline leakage acoustic signal at present, and is also a positioning method which is widely applied at present, the adopted instruments are mainly a leakage detecting rod and an electronic leakage detecting instrument, the method is seriously dependent on experience accumulation of leakage detecting workers, a qualified technician can be cultivated only after a long time, the method has high requirements on environmental noise, the method can work only when the workers feel quiet at night, and certain influence can be caused on the hearing and the body of the workers for a long time. Therefore, it is necessary to develop a leak detection method which is accurate in positioning and suitable for the condition of the China pipe network and further develop an instrument and equipment which is simple to operate.
The leakage point positioning method based on software is leakage positioning and identification through a model and signal processing, such as a correlation analysis method, is a method which is commonly applied worldwide at present, and is characterized in that two sensors are respectively arranged at two ends of a pipeline or a valve and a fire hydrant and used for receiving vibration sound wave signals transmitted along the pipeline, and the position of a leakage point is accurately calculated through the time difference of the sound wave signals received by the two sensors and various parameters such as the length, the material, the pipe diameter and the like of the input pipeline. The method is simple to operate, accurate in positioning and widely applied to the field of detection and positioning of leakage in the world, but for complex pipe network conditions and sensor placement distance requirements of correlators in China, the position where two sensors can be placed is sometimes difficult to find, and the condition that the specific length of a pipeline between two points cannot be determined exists, if other accessories such as an elbow and a valve exist on a pipe section between the two sensors, analysis results are also influenced, moreover, the whole set of correlators are expensive, and for county and villages with low economic level in China, water supply companies cannot afford related expenses, so that the method is difficult to popularize in China.
In summary, the leak positioning method based on the leak acoustic signal has been widely used, but in the actual leak detection process, the detection efficiency, the detection precision and the operation condition of the correlation analysis method and the ground leak acoustic detection method are limited due to the influence of the pipeline condition and the environmental noise, and the ground leak acoustic detection method is the most widely used method in the actual leak detection work, so that the need of realizing the intellectualization by combining the modern sensing technology and the signal processing means is urgent.
Disclosure of Invention
The invention aims to solve the problems of limited operation conditions, low detection precision and low detection efficiency of the traditional acoustic leakage detection method in the aspect of water supply network leakage detection, and provides a method and a system for detecting leakage of a water supply pipeline by using a multi-probe array based on phase and amplitude attenuation.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for detecting leakage of a water supply pipeline based on a multi-probe array of phase and amplitude attenuation, the method specifically comprising the following steps:
step one, acquiring pipeline signals by adopting a three-probe sensor array formed by three A, B, C sensors;
the system comprises a pipeline signal acquisition device, a pipeline signal acquisition device and a pipeline signal acquisition device, wherein the pipeline signal acquisition device acquires a pipeline signal acquisition device, and the pipeline signal acquisition device acquires a pipeline signal acquisition device;
step two, after the collected signals are amplified, the amplified signals are connected to an upper computer;
step three, the upper computer sequentially denoises and preprocesses the signals to obtain preprocessed signals;
extracting phase characteristics and amplitude attenuation characteristics of the preprocessed signals, constructing a phase characteristic vector based on the extracted phase characteristics, and constructing an amplitude attenuation characteristic vector based on the extracted amplitude attenuation characteristics;
the specific process for extracting the phase characteristics of the preprocessed signals comprises the following steps:
after the signals collected by the A, B, C sensors under the same experimental condition are processed in the second step and the third step, the preprocessed signals corresponding to the sensor A, the sensor B and the sensor C are subjected to Fourier transformation respectively, so that the Fourier transformed signals corresponding to the sensor A, the Fourier transformed signals corresponding to the sensor B and the Fourier transformed signals corresponding to the sensor C are obtained;
respectively calculating the frequency f 1 、f 2 、f 3 、f 4 、f 5 The Phase difference (A-B)/Phase difference (B-C) value, wherein Phase difference (A-B) represents a Phase spectrum difference between the Fourier transformed signal corresponding to sensor A and the Fourier transformed signal corresponding to sensor B, and Phase difference (B-C) represents a Phase spectrum difference between the Fourier transformed signal corresponding to sensor B and the Fourier transformed signal corresponding to sensor CA phase spectrum difference value;
frequency f 1 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x1;
frequency f 2 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x2;
frequency f 3 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x3;
frequency f 4 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x4;
frequency f 5 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x5;
constructing a phase characteristic vector X= (X1, X2, X3, X4, X5) under the current experimental condition;
for the signals under other collected experimental conditions and under actual conditions, the phase characteristic extraction mode and the phase characteristic vector construction mode are the same;
fifthly, assigning values to the pipeline signals collected in the first step each time, taking the constructed phase characteristic vector and the amplitude attenuation characteristic vector as the input of the BP neural network, taking the corresponding assigned results as the output of the BP neural network, and training the BP neural network;
step six, collecting pipeline signals to be detected by using a three-probe sensor array, extracting phase characteristics and amplitude attenuation characteristics of the pipeline signals to be detected, then constructing feature vectors, inputting the feature vectors of the pipeline signals to be detected into a trained BP neural network, and obtaining leakage detection results of the pipeline to be detected.
Further, the three sensors A, B, C are arranged in a right triangle.
Further, the hook lengths of the right triangle are 0.7m, 0.39m and 0.8m respectively.
Further, the acquisition process of the signals under the laboratory condition is as follows:
the method comprises the steps of adopting a steel plate to weld a box body, taking the box body as a soil carrier, enabling a pipeline to longitudinally penetrate through the box body from a position H height away from the bottom of the box body, and adopting a water tank to circularly supply water to the pipeline;
the three-probe sensor array is arranged right above the pipeline, the size and the orientation of a leakage opening of the pipeline and the burial depth in the box body are continuously adjusted, and signals are acquired by the three-probe sensor array during each adjustment.
Further, the preprocessing includes pre-emphasis, framing, and windowing.
Further, the specific process of extracting the amplitude attenuation characteristic of the preprocessed signal is as follows:
after the signals collected by the A, B, C sensors under the same experimental condition are processed in the second step and the third step, the preprocessed signals corresponding to the sensor A, the sensor B and the sensor C are subjected to Fourier transformation respectively to obtain a signal time spectrum corresponding to the sensor A, a signal time spectrum corresponding to the sensor B and a signal time spectrum corresponding to the sensor C;
construction function y=transfer function (a-B)/transfer function (B-C), wherein transfer function (a-B) represents the difference in the temporal spectral magnitudes of sensor a and sensor B, and transfer function (B-C) represents the difference in the temporal spectral magnitudes of sensor B and sensor C;
respectively calculating the frequency f 1 、f 2 、f 3 、f 4 、f 5 The following y values:
frequency f 1 The value of transfer function (A-B)/transfer function (B-C) is denoted as y1;
frequency f 2 The value of transfer function (A-B)/transfer function (B-C) is denoted as y2;
frequency f 3 The value of transfer function (A-B)/transfer function (B-C) is denoted as y3;
frequency f 4 The value of transfer function (A-B)/transfer function (B-C) is denoted as y4;
frequency f 5 The value of transfer function (A-B)/transfer function (B-C) is denoted as y5;
constructing an amplitude attenuation characteristic vector Y= (Y1, Y2, Y3, Y4, Y5) under the current experimental condition;
and for the signals under other acquired experimental conditions and under actual conditions, the amplitude attenuation characteristic extraction mode and the amplitude attenuation characteristic vector construction mode are the same.
Further, the pipeline signal collected in the first step is assigned to be 0 or 1.
Further, the number of input layer nodes of the BP neural network is 10, the number of hidden layer nodes is 12, and the number of output layer nodes is 1.
Further, the amplified signals are connected to an upper computer through a dynamic acquisition analyzer.
A system for detecting leakage of a water supply pipe based on a multi-probe array of phase and amplitude attenuation, the system being for performing a method for detecting leakage of a water supply pipe based on a multi-probe array of phase and amplitude attenuation.
The beneficial effects of the invention are as follows:
according to the invention, a movable multi-probe acoustic sensor is laid on the ground, ground vibration noise signals are collected through a specific sensor arrangement mode to be used as acoustic signals for detecting whether leakage occurs in a pipeline, signal processing is carried out through fast Fourier transform, and after phase spectrum characteristics and amplitude spectrum characteristics of the acoustic signals are obtained, characteristic vectors are constructed based on the obtained characteristics; and (3) inputting the constructed feature vector into the BP neural network to obtain a leakage detection result of the pipeline to be detected by establishing the BP neural network. The method can be realized by only collecting the acoustic signals on the ground, is not limited by operation conditions, and can improve the detection efficiency and simultaneously enable the leakage detection to be more accurate by comprehensively considering the phase spectrum characteristics and the amplitude spectrum characteristics.
Drawings
FIG. 1 is a flow chart of a method of detecting leakage of a water supply pipe based on a multi-probe array of phase and amplitude attenuation in accordance with the present invention.
Detailed Description
Detailed description of the inventionin the first embodiment, this embodiment will be described with reference to fig. 1. The method for detecting leakage of the water supply pipeline by using the multi-probe array based on phase and amplitude attenuation in the embodiment specifically comprises the following steps:
step one, acquiring pipeline signals by adopting a three-probe sensor array formed by three A, B, C sensors;
the system comprises a pipeline signal acquisition device, a pipeline signal acquisition device and a pipeline signal acquisition device, wherein the pipeline signal acquisition device acquires a pipeline signal acquisition device, and the pipeline signal acquisition device acquires a pipeline signal acquisition device;
step two, after the collected signals are amplified, the amplified signals are connected to an upper computer;
step three, the upper computer sequentially denoises and preprocesses the signals to obtain preprocessed signals;
extracting phase characteristics and amplitude attenuation characteristics of the preprocessed signals, constructing a phase characteristic vector based on the extracted phase characteristics, and constructing an amplitude attenuation characteristic vector based on the extracted amplitude attenuation characteristics;
the specific process for extracting the phase characteristics of the preprocessed signals comprises the following steps:
after the signals collected by the three A, B, C sensors under the same experimental condition (the same experimental condition means that the size and the orientation of the leakage opening are the same as the burial depth in the box body) are processed in the second step and the third step, the preprocessed signals corresponding to the sensor A, the sensor B and the sensor C are subjected to Fourier transformation respectively to obtain the Fourier transformed signals corresponding to the sensor A, the Fourier transformed signals corresponding to the sensor B and the Fourier transformed signals corresponding to the sensor C;
respectively calculating the frequency f 1 、f 2 、f 3 、f 4 、f 5 A lower Phase difference (a-B)/Phase difference (B-C) value, wherein Phase difference (a-B) represents a Phase spectrum difference between the post-fourier-transform signal corresponding to sensor a and the post-fourier-transform signal corresponding to sensor B, and Phase difference (B-C) represents a Phase spectrum difference between the post-fourier-transform signal corresponding to sensor B and the post-fourier-transform signal corresponding to sensor C; phase difference (A-B)/Phase difference (B-C) represents the ratio of Phase difference (A-B) to Phase difference (B-C);
frequency f 1 The Phase difference (A-B)/Phase difference (B-C) values are recordedIs x1;
frequency f 2 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x2;
frequency f 3 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x3;
frequency f 4 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x4;
frequency f 5 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x5;
constructing a phase characteristic vector X= (X1, X2, X3, X4, X5) under the current experimental condition;
for the signals under other collected experimental conditions and under actual conditions, the phase characteristic extraction mode and the phase characteristic vector construction mode are the same;
before the method of the invention starts, the number (A, B, C) of the three sensors can be arbitrarily given, and after the number is once determined, the whole treatment process is not changed any more;
since the fourier transformed (FFT) signal satisfies the following relationship:
(1) At a fixed frequency, the phase spectrum difference value between the Fourier transformed signal corresponding to the sensor A and the Fourier transformed signal corresponding to the sensor B is equal to the phase spectrum difference value between the Fourier transformed signal corresponding to the sensor B and the Fourier transformed signal corresponding to the sensor C;
(2) Under different frequencies, the phase spectrum difference value between the Fourier transformed signals corresponding to the sensor A and the Fourier transformed signals corresponding to the sensor B linearly changes along with the distance from the three-probe sensor array to the position right above the leakage point;
therefore, the phase characteristic extraction mode of the invention is designed;
fifthly, assigning values to the pipeline signals collected in the first step each time, taking the constructed phase characteristic vector and the amplitude attenuation characteristic vector as the input of the BP neural network, taking the corresponding assigned results as the output of the BP neural network, and training the BP neural network;
the signals collected each time have corresponding feature vectors and assignment values;
step six, collecting pipeline signals to be detected by using a three-probe sensor array, extracting phase characteristics and amplitude attenuation characteristics of the pipeline signals to be detected, then constructing feature vectors, inputting the feature vectors of the pipeline signals to be detected into a trained BP neural network, and obtaining leakage detection results of the pipeline to be detected.
After the characteristic vector of the pipeline signal to be detected is input, the value output by the BP neural network is between 0 and 1, 0 represents the determination of no leakage, 1 represents the determination of leakage, and the value between 0 and 1 represents the probability of leakage.
The propagation rule of the leakage signal on the ground meets the following conditions: displacement u=ae i(kx-wt) Where k is an imaginary number, the real part of k re (k) =frequency/velocity=w/v, and the imaginary part of k is an attenuation coefficient, denoted Im (k). Phase refers to kx, and it can be seen that for the same t, phase kx is a function of distance, based on this principle, the phase and amplitude decay characteristics are taken as characteristics of leak detection.
The amplitude attenuation characteristic is very different between the case of pipe leakage and the case of no pipe leakage, and can attenuate by 30dB within 2m from the leakage point, so that the amplitude attenuation characteristic is suitable for being used as the judgment basis of pipe leakage, but the characteristic is easy to be interfered by noise. And the phase information feature is a feature that is more stable than the amplitude in the presence of noise, and is less affected by noise. According to the invention, the amplitude attenuation characteristic and the phase information characteristic are selected as consideration factors for detecting the leakage of the pipeline, so that the accuracy of detecting the leakage can be obviously improved, and the amplitude attenuation characteristic and the phase information characteristic are indispensable.
The second embodiment is as follows: this embodiment differs from the specific embodiment in that the three sensors A, B, C are arranged in a right triangle.
Other steps and parameters are the same as in the first embodiment.
And a third specific embodiment: this embodiment differs from the first or second embodiment in that the hook lengths of the right triangle are 0.7m, 0.39m and 0.8m, respectively.
This embodiment is through reasonable in design collude strand length to guarantee the precision that detects.
Other steps and parameters are the same as in the first or second embodiment.
The specific embodiment IV is as follows: this embodiment differs from one to three embodiments in that the signal acquisition process in the laboratory case is:
the method comprises the steps of adopting a steel plate to weld a box body, taking the box body as a soil carrier, enabling a pipeline to longitudinally penetrate through the box body from a position H height away from the bottom of the box body, and adopting a water tank to circularly supply water to the pipeline;
the three-probe sensor array is arranged right above the pipeline, the size and the orientation of a leakage opening of the pipeline and the burial depth in the box body are continuously adjusted, and signals are acquired by the three-probe sensor array during each adjustment.
The water tank is used for circulating water supply, the vertical multi-stage pump is used for supplying energy, the water leakage is supplemented by the water tank, the air pressure tank is used for supplying water, the water pump is used for pressing water into the air pressure tank, and the water pressure is closed when the water pressure reaches a set value. In the experimental process, high-pressure water flow is pumped into the simulation pipeline by the air pressure tank, and the pressure in the pipeline is controlled to be constant by the pressure reducing valve and the pressure gauge. The pipeline is replaceable, and the size, the orientation and the position of the leakage opening can be adjusted to acquire signals under various leakage conditions.
Other steps and parameters are the same as in one to three embodiments.
Fifth embodiment: this embodiment differs from the one to four embodiments in that the preprocessing includes pre-emphasis, framing, and windowing.
Other steps and parameters are the same as in one to four embodiments.
Specific embodiment six: the difference between this embodiment and one to fifth embodiments is that the specific process of extracting the amplitude attenuation characteristic of the preprocessed signal is:
after the signals collected by the A, B, C sensors under the same experimental condition are processed in the second step and the third step, the preprocessed signals corresponding to the sensor A, the sensor B and the sensor C are subjected to Fourier transformation respectively to obtain a signal time spectrum corresponding to the sensor A, a signal time spectrum corresponding to the sensor B and a signal time spectrum corresponding to the sensor C;
the two sets of attenuation are in principle identical, and the closer to the leakage point, the larger the spectrum is in relation thereto due to the influence of the bulk wave near the leakage point;
construction function y=transfer function (a-B)/transfer function (B-C), wherein transfer function (a-B) represents the difference in the temporal spectral magnitudes of sensor a and sensor B, and transfer function (B-C) represents the difference in the temporal spectral magnitudes of sensor B and sensor C;
respectively calculating the frequency f 1 、f 2 、f 3 、f 4 、f 5 The following y values:
frequency f 1 The value of transfer function (A-B)/transfer function (B-C) is denoted as y1;
frequency f 2 The value of transfer function (A-B)/transfer function (B-C) is denoted as y2;
frequency f 3 The value of transfer function (A-B)/transfer function (B-C) is denoted as y3;
frequency f 4 The value of transfer function (A-B)/transfer function (B-C) is denoted as y4;
frequency f 5 The value of transfer function (A-B)/transfer function (B-C) is denoted as y5;
constructing an amplitude attenuation characteristic vector Y= (Y1, Y2, Y3, Y4, Y5) under the current experimental condition;
and for the signals under other acquired experimental conditions and under actual conditions, the amplitude attenuation characteristic extraction mode and the amplitude attenuation characteristic vector construction mode are the same.
And obtaining the amplitude of the corresponding frequency from the frequency spectrum, constructing a transfer function, and obtaining a characteristic vector Y.
Other steps and parameters are the same as in one of the first to fifth embodiments.
Seventh embodiment: this embodiment differs from one of the first to sixth embodiments in that the pipeline signal collected in the first step is assigned a value of 0 or 1.
The pipe signal with the leak point is assigned a value of 1 and the ambient noise is assigned a value of 0.
Other steps and parameters are the same as in one of the first to sixth embodiments.
Eighth embodiment: the difference between this embodiment and one of the first to seventh embodiments is that the number of input layer nodes of the BP neural network is 10, the number of hidden layer nodes is 12, and the number of output layer nodes is 1.
Other steps and parameters are the same as those of one of the first to seventh embodiments.
Detailed description nine: the embodiment is different from one to eight of the specific embodiments in that the amplified signal is connected to an upper computer through a dynamic acquisition analyzer.
Other steps and parameters are the same as in one to eight of the embodiments.
Detailed description ten: a system for detecting leakage of a water supply pipe based on a multi-probe array of phase and amplitude attenuation according to the present embodiment is for performing a method for detecting leakage of a water supply pipe based on a multi-probe array of phase and amplitude attenuation.
The above examples of the present invention are only for describing the calculation model and calculation flow of the present invention in detail, and are not limiting of the embodiments of the present invention. Other variations and modifications of the above description will be apparent to those of ordinary skill in the art, and it is not intended to be exhaustive of all embodiments, all of which are within the scope of the invention.
Claims (10)
1. A method for detecting leakage of a water supply pipeline by a multi-probe array based on phase and amplitude attenuation, which is characterized by comprising the following steps:
step one, acquiring pipeline signals by adopting a three-probe sensor array formed by three A, B, C sensors;
the system comprises a pipeline signal acquisition device, a pipeline signal acquisition device and a pipeline signal acquisition device, wherein the pipeline signal acquisition device acquires a pipeline signal acquisition device, and the pipeline signal acquisition device acquires a pipeline signal acquisition device;
step two, after the collected signals are amplified, the amplified signals are connected to an upper computer;
step three, the upper computer sequentially denoises and preprocesses the signals to obtain preprocessed signals;
extracting phase characteristics and amplitude attenuation characteristics of the preprocessed signals, constructing a phase characteristic vector based on the extracted phase characteristics, and constructing an amplitude attenuation characteristic vector based on the extracted amplitude attenuation characteristics;
the specific process for extracting the phase characteristics of the preprocessed signals comprises the following steps:
after the signals collected by the A, B, C sensors under the same experimental condition are processed in the second step and the third step, the preprocessed signals corresponding to the sensor A, the sensor B and the sensor C are subjected to Fourier transformation respectively, so that the Fourier transformed signals corresponding to the sensor A, the Fourier transformed signals corresponding to the sensor B and the Fourier transformed signals corresponding to the sensor C are obtained;
respectively calculating the frequency f 1 、f 2 、f 3 、f 4 、f 5 A lower Phase difference (a-B)/Phase difference (B-C) value, wherein Phase difference (a-B) represents a Phase spectrum difference between the post-fourier-transform signal corresponding to sensor a and the post-fourier-transform signal corresponding to sensor B, and Phase difference (B-C) represents a Phase spectrum difference between the post-fourier-transform signal corresponding to sensor B and the post-fourier-transform signal corresponding to sensor C;
frequency f 1 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x1;
frequency f 2 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x2;
frequency f 3 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x3;
frequency f 4 The lower Phase difference (A-B)/Phase difference (B-C) value is noted as x4;
frequency f 5 The Phase difference (A-B)/Phase difference (B-C) values are recordedIs x5;
constructing a phase characteristic vector X= (X1, X2, X3, X4, X5) under the current experimental condition;
for the signals under other collected experimental conditions and under actual conditions, the phase characteristic extraction mode and the phase characteristic vector construction mode are the same;
fifthly, assigning values to the pipeline signals collected in the first step each time, taking the constructed phase characteristic vector and the amplitude attenuation characteristic vector as the input of the BP neural network, taking the corresponding assigned results as the output of the BP neural network, and training the BP neural network;
step six, collecting pipeline signals to be detected by using a three-probe sensor array, extracting phase characteristics and amplitude attenuation characteristics of the pipeline signals to be detected, then constructing feature vectors, inputting the feature vectors of the pipeline signals to be detected into a trained BP neural network, and obtaining leakage detection results of the pipeline to be detected.
2. The method for detecting leakage of a water supply pipe based on a multi-probe array of phase and amplitude attenuation according to claim 1, wherein the A, B, C three sensors are arranged in a right triangle.
3. The method for detecting leakage of a water supply pipe based on a multi-probe array of phase and amplitude attenuation according to claim 2, wherein the groin lengths of the right triangle are 0.7m, 0.39m and 0.8m, respectively.
4. The method for detecting leakage of a water supply pipe based on a multi-probe array of phase and amplitude attenuation according to claim 1, wherein the acquisition process of the signals in the laboratory is:
the method comprises the steps of adopting a steel plate to weld a box body, taking the box body as a soil carrier, enabling a pipeline to longitudinally penetrate through the box body from a position H height away from the bottom of the box body, and adopting a water tank to circularly supply water to the pipeline;
the three-probe sensor array is arranged right above the pipeline, the size and the orientation of a leakage opening of the pipeline and the burial depth in the box body are continuously adjusted, and signals are acquired by the three-probe sensor array during each adjustment.
5. The method for detecting leakage of a water supply pipe based on a multi-probe array of phase and amplitude attenuation of claim 1, wherein the pre-processing includes pre-emphasis, framing, and windowing.
6. The method for detecting leakage of a water supply pipeline based on a multi-probe array of phase and amplitude attenuation as claimed in claim 1, wherein the specific process of extracting the amplitude attenuation characteristics of the preprocessed signals is as follows:
after the signals collected by the A, B, C sensors under the same experimental condition are processed in the second step and the third step, the preprocessed signals corresponding to the sensor A, the sensor B and the sensor C are subjected to Fourier transformation respectively to obtain a signal time spectrum corresponding to the sensor A, a signal time spectrum corresponding to the sensor B and a signal time spectrum corresponding to the sensor C;
construction function y=transfer function (a-B)/transfer function (B-C), wherein transfer function (a-B) represents the difference in the temporal spectral magnitudes of sensor a and sensor B, and transfer function (B-C) represents the difference in the temporal spectral magnitudes of sensor B and sensor C;
respectively calculating the frequency f 1 、f 2 、f 3 、f 4 、f 5 The following y values:
frequency f 1 The value of transfer function (A-B)/transfer function (B-C) is denoted as y1;
frequency f 2 The value of transfer function (A-B)/transfer function (B-C) is denoted as y2;
frequency f 3 The value of transfer function (A-B)/transfer function (B-C) is denoted as y3;
frequency f 4 The value of transfer function (A-B)/transfer function (B-C) is denoted as y4;
frequency f 5 The value of transfer function (A-B)/transfer function (B-C) is denoted as y5;
constructing an amplitude attenuation characteristic vector Y= (Y1, Y2, Y3, Y4, Y5) under the current experimental condition;
and for the signals under other acquired experimental conditions and under actual conditions, the amplitude attenuation characteristic extraction mode and the amplitude attenuation characteristic vector construction mode are the same.
7. The method for detecting leakage of a water supply pipe based on a multi-probe array of phase and amplitude attenuation as claimed in claim 1, wherein the value of each acquired pipe signal in the step one is assigned to be 0 or 1.
8. The method for detecting leakage of a water supply pipeline by using the multi-probe array based on phase and amplitude attenuation according to claim 1, wherein the number of nodes of an input layer of the BP neural network is 10, the number of nodes of a hidden layer is 12, and the number of nodes of an output layer is 1.
9. The method for detecting leakage of a water supply pipeline based on a multi-probe array of phase and amplitude attenuation according to claim 1, wherein the amplified signal is connected to an upper computer through a dynamic acquisition analyzer.
10. A system for detecting leakage of a water supply pipe based on a multi-probe array of phase and amplitude attenuation, characterized in that the system is adapted to perform the method for detecting leakage of a water supply pipe based on a multi-probe array of phase and amplitude attenuation as claimed in any one of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111069319.6A CN113670531B (en) | 2021-09-13 | 2021-09-13 | Method and system for detecting leakage of water supply pipeline by using multi-probe array based on phase and amplitude attenuation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111069319.6A CN113670531B (en) | 2021-09-13 | 2021-09-13 | Method and system for detecting leakage of water supply pipeline by using multi-probe array based on phase and amplitude attenuation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113670531A CN113670531A (en) | 2021-11-19 |
| CN113670531B true CN113670531B (en) | 2023-12-01 |
Family
ID=78549333
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111069319.6A Active CN113670531B (en) | 2021-09-13 | 2021-09-13 | Method and system for detecting leakage of water supply pipeline by using multi-probe array based on phase and amplitude attenuation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113670531B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106018561A (en) * | 2016-05-13 | 2016-10-12 | 中国石油大学(华东) | System and method for measuring sound wave amplitude attenuation coefficients in different pipeline structures |
| CN107063584A (en) * | 2017-04-22 | 2017-08-18 | 中南大学 | A kind of Boiler Tubes Leakage differentiates and localization method |
| CN109668058A (en) * | 2018-12-24 | 2019-04-23 | 哈尔滨工业大学 | Water supply line leakage loss discrimination method based on linear prediction residue error and lyapunov index |
| CN112066272A (en) * | 2020-09-17 | 2020-12-11 | 昆明理工大学 | Gas pipeline leakage detection device and detection method based on infrasonic waves |
| CN112945477A (en) * | 2021-03-30 | 2021-06-11 | 禹班工程科技(上海)有限公司 | Leakage detection/monitoring system, method and medium for pressure water pipeline |
-
2021
- 2021-09-13 CN CN202111069319.6A patent/CN113670531B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106018561A (en) * | 2016-05-13 | 2016-10-12 | 中国石油大学(华东) | System and method for measuring sound wave amplitude attenuation coefficients in different pipeline structures |
| CN107063584A (en) * | 2017-04-22 | 2017-08-18 | 中南大学 | A kind of Boiler Tubes Leakage differentiates and localization method |
| CN109668058A (en) * | 2018-12-24 | 2019-04-23 | 哈尔滨工业大学 | Water supply line leakage loss discrimination method based on linear prediction residue error and lyapunov index |
| CN112066272A (en) * | 2020-09-17 | 2020-12-11 | 昆明理工大学 | Gas pipeline leakage detection device and detection method based on infrasonic waves |
| CN112945477A (en) * | 2021-03-30 | 2021-06-11 | 禹班工程科技(上海)有限公司 | Leakage detection/monitoring system, method and medium for pressure water pipeline |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113670531A (en) | 2021-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Leak location in gas pipelines using cross-time–frequency spectrum of leakage-induced acoustic vibrations | |
| CN104747912B (en) | Fluid conveying pipe leakage acoustic emission time-frequency positioning method | |
| CN107435817B (en) | A kind of pressure pipeline two o'clock leak detection accurate positioning method | |
| CN101592288B (en) | Method for identifying pipeline leakage | |
| CN102606891A (en) | Water leakage detector, water leakage detecting system and water leakage detecting method | |
| CN111520615B (en) | Pipe network leakage identification and positioning method based on line spectrum pair and cubic interpolation search | |
| CN109827081B (en) | Buried drainage pipeline blocking fault and pipeline tee part diagnosis method based on acoustic active detection | |
| CN103234121A (en) | Acoustic signal based device and method for detecting gas pipeline leakages | |
| CN107085216A (en) | A kind of deep-sea underwater sound passive ranging depth detecting method based on single hydrophone | |
| CN105956577A (en) | Sound wave signal feature extraction method for micro leakage of gas pipeline based on random resonance | |
| CN107356666A (en) | A kind of extraction method and system of halmeic deposit parameters,acoustic | |
| Li et al. | Leak detection and location in gas pipelines by extraction of cross spectrum of single non-dispersive guided wave modes | |
| CN109387565A (en) | A method of brake block internal flaw is detected by analysis voice signal | |
| CN105674065A (en) | Variational mode decomposition-based method for locating leakage point of pipeline by acoustic emission | |
| CN108181059A (en) | Multiphase flow pipeline leakage acoustic signals recognition methods based on small echo signal | |
| CN113670531B (en) | Method and system for detecting leakage of water supply pipeline by using multi-probe array based on phase and amplitude attenuation | |
| CN109538946B (en) | Urban tap water pipeline leakage detection positioning method | |
| Han et al. | Localization of CO2 gas leakages through acoustic emission multi-sensor fusion based on wavelet-RBFN modeling | |
| CN110953485B (en) | Gas pipeline leakage point positioning method and system | |
| Tang et al. | Leak detection of water pipeline using wavelet transform method | |
| CN112198232A (en) | Drainage pipeline working condition detection and identification method | |
| CN105928666B (en) | Leakage acoustic characteristic extracting method based on Hilbert-Huang transform and blind source separating | |
| CN106195648B (en) | A kind of experimental test procedures of the equivalent pipe range of reducer pipe | |
| CN111506870B (en) | Time-varying structure damage identification method based on wavelet transformation | |
| Gao et al. | Acoustic emission-based small leak detection of propulsion system pipeline of sounding rocket |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |