CN110701490A - Pipeline leakage monitoring method and equipment - Google Patents
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Abstract
The application discloses a pipeline leakage monitoring method and equipment thereof, wherein the method comprises the following steps of collecting pipeline stress waves and background noise, obtaining the difference values of the pipeline stress waves and the background noise at each corresponding time point, and determining the pipeline leakage state when the sum of the absolute values of the difference values of each corresponding time point is greater than a threshold value; the pipeline leakage monitoring device comprises an acquisition module, a data processing module and an identification module, wherein the acquisition module is used for acquiring stress waves and background noise of a pipeline; the data processing module is used for making a difference between the acquired pipeline stress wave and the background noise and calculating the sum of absolute values of the difference values of all corresponding time points; the identification module is used for determining a pipeline leakage state. By adopting the pipeline leakage monitoring method, the effects of reducing noise, improving the signal-to-noise ratio and filtering can be realized, and the real-time monitoring of the pipeline leakage is realized.
Description
Technical Field
The application relates to the technical field of pipeline transmission, in particular to a pipeline leakage monitoring method and equipment.
Background
Urban underground pipe networks are not only important public facilities related to national economic life lines and national energy safety, but also related to underground space safety. The technical means for maintaining the integrity and the safety of pipeline transmission is not enough, and risks of oil and gas pipeline fire, pipeline leakage, environmental pollution and the like exist. And the frequent events that the communication optical cable is broken, the underground cable is stolen and cut, and the like bring serious influences on the normal production and life of people and even the national security.
The leakage of the pipeline is caused by the phenomenon that the fluid in the pipeline leaks outwards due to the fact that the pipeline generates cracks or corrosion holes due to corrosion aging of materials or other external force actions and pressure difference exists between the inside and the outside of the pipeline. Wherein the fluid is ejected outwardly through the cracks or corrosion holes to form an acoustic source, which then radiates energy outwardly to form sound waves by interacting with the pipe. For example, when a water supply pipe leaks, a sound source is generated by water being ejected from the leakage point to the outside of the pipe by pressure and is transmitted to the outside through various media. When water leaks, the water and the pipe wall generate friction to generate stress waves, and the energy is mainly transmitted to the upstream and the downstream of a leakage source along the pipe wall. It has the lowest relatively attenuated energy but is highly susceptible to interference from ambient noise. Therefore, in practical application, the detected stress wave is relatively easy to be infected by peripheral noise, and the application scene is greatly limited.
Disclosure of Invention
The application provides a pipeline leakage monitoring method and system, which solve the problem of timely and accurately determining leakage collection of a pressure pipeline.
The embodiment of the application provides a pipeline leakage monitoring method which is characterized in that pipeline stress waves and background noise are collected, difference values of the pipeline stress waves and the background noise at corresponding time points are obtained, and when the sum of absolute values of the difference values of the corresponding time points is larger than a threshold value, a pipeline leakage state is determined.
Further, in the same time period, performing FFT on the difference values of the corresponding time points to obtain coefficients of the corresponding time points, and determining that the pipeline is in a suspected leakage state when the sum of the absolute values of the coefficients of the corresponding time points is greater than a first threshold.
Further, in the same time period, performing FFT on the collected pipeline stress wave and the background noise signal, and performing subtraction to obtain coefficients of each corresponding time point, and when the sum of absolute values of the coefficients of each corresponding time point is greater than a second threshold, determining that the pipeline is in a suspected leakage state.
Furthermore, the sensor for collecting the stress wave of the pipeline is in hard connection with the metal part of the pipeline to be detected through a screw or a strong magnet.
Furthermore, the sensor for collecting the background noise is arranged in the soil around the pipeline or the wall of the well.
The embodiment of the present application further provides a pipeline leakage monitoring device, which includes: the system comprises an acquisition module, a data processing module and an identification module, wherein the acquisition module is used for acquiring pipeline stress waves and background noise; the data processing module is used for making a difference between the acquired pipeline stress wave and the background noise and calculating the sum of absolute values of the difference values of all corresponding time points; the identification module is used for determining a pipeline leakage state.
Further, collecting pipeline stress waves and background noise, obtaining differences of the pipeline stress waves and the background noise at each corresponding time point, and determining the pipeline leakage state when the sum of absolute values of the differences at each corresponding time point is greater than a threshold value.
Further, in the same time period, performing FFT on the difference values of the corresponding time points to obtain coefficients of the corresponding time points, and when the sum of the absolute values of the coefficients of the corresponding time points is greater than a first threshold, determining that the pipeline is in a suspected leakage state.
Further, in the same time period, performing FFT on the collected pipeline stress wave and the background noise signal, and performing subtraction to obtain coefficients of each corresponding time point, and when the sum of absolute values of the coefficients of each corresponding time point is greater than a second threshold, determining that the pipeline is in a suspected leakage state.
Furthermore, a sensor for collecting stress waves of the pipeline is in hard connection with a metal part of the pipeline to be detected through a screw or a strong magnet, and the sensor for collecting background noise is arranged in soil around the pipeline or a well wall.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects: by adopting the pipeline leakage monitoring method and the pipeline leakage monitoring equipment, the effect of reducing noise can be realized, the signal to noise ratio can be improved, and the real-time monitoring of the pipeline leakage can be realized.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart of a pipeline leakage monitoring method;
fig. 2 is a schematic structural diagram of a pipeline leakage monitoring device.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a pipeline leakage monitoring method. The method may be as follows.
Step 101: and collecting pipeline stress waves and background noise.
In step 101, under the condition that the electric energy of the whole operation is normally supplied, a stress wave signal of the pipeline and a background noise signal of the periphery of the pipeline are collected through a sensor.
The sensor for collecting pipeline stress wave and the sensor for collecting background noise are the same type.
In the method of this embodiment, the sensors of the same type are used to collect the pipeline stress wave signal and the background noise signal around the pipeline, respectively, so that the frequency spectrum of the same range of amplitude can be obtained.
In another embodiment of the present invention, the method further comprises:
and the sensor for collecting the stress wave of the pipeline is in hard connection with the metal part of the pipeline to be detected through a screw or a strong magnet.
In another embodiment of the present invention, the method further comprises:
the sensors for collecting background noise are arranged in the soil or the well wall around the pipeline.
Step 102: and obtaining the difference values of the pipeline stress wave and the background noise at each corresponding time point, and calculating the sum of the absolute values of the difference values at each corresponding time point.
In step 102, the sensors of the same type respectively collect signals of the pipeline stress wave and the background noise to obtain two sets of data of the same time period respectively monitored by the pipeline stress wave sensor and the background noise sensor, the two sets of data are normalized and differenced with each corresponding time point to obtain differences of the pipeline stress wave and the background noise at each corresponding time point, and then the absolute values of the differences of each corresponding time point are summed.
The same time period is preset empirically and is not particularly limited.
It should be noted that the normalization processing method includes Min-max normalization (Min-max normalization)/z-score normalization, such as Min-max normalization (Min-max normalization): traversing each data in the array to obtain each numberMax and Min in the group are recorded, and the normalization processing of the data is carried out by taking Max-Min as a base number:
and normalizing the assignment values in the two arrays respectively monitored by the pipeline stress wave sensor and the background noise sensor in the same time period to be in the same interval, wherein the assignment values are not limited by specific numerical values.
After normalization processing, obtaining differences of pipeline stress waves and background noises at corresponding time points by making differences of two groups of normalized data in the same time period respectively monitored by a pipeline stress wave sensor and a background noise sensor, and then summing absolute values of the differences at the corresponding time points.
For example: the time interval of adjacent sampling points is 1/7500 seconds, the signals of the pipeline stress wave and the background noise are respectively collected according to the sensors with the same model, and the pipeline stress wave signals are determined to be respectively: 1/7500 th second: 0.57164V; 2/7500 th second: 0.06561V; 3/7500 th second: -0.47738V; 4/7500 th second: -0.69833V; 5/7500 th second: -0.45361V; 6/7500 th second: 0.09978V; 7/7500 th second: 0.58922V; 8/7500 th second: 0.69649V; 9/7500 th second: 0.34913V; 10/7500 th second: -0.22669V; 11/7500 th second: -0.64323V; 12/7500 th second: -0.63115V; 13/7500 th second: -0.19473V; 14/7500 th second: 0.3769V; 15/7500 th second: 0.70241V; 16/7500 th second: 0.57228V; and determining the pipeline stress wave signal by analogy.
Determining the background noise signals are respectively: 1/7500 th second: 0.56993V; 2/7500 th second: 0.06282V; 3/7500 th second: -0.47935V; 4/7500 th second: -0.6981V; 5/7500 th second: -0.45107V; 6/7500 th second: 0.10289V; 7/7500 th second: 0.59104V; 8/7500 th second: 0.69595V; 9/7500 th second: 0.34666V; 10/7500 th second: -0.22936V; 11/7500 th second: -0.6444V; 12/7500 th second: -0.63007V; 13/7500 th second: -0.19212V; 14/7500 th second: 0.37924V; 15/7500 th second: 0.70283V; 16/7500 th second: 0.5704V; and determining the background noise signal by analogy.
The absolute values of the differences between the pipeline stress wave and the background noise at each corresponding time point are respectively: at 1/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00171V; at 2/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00279V; at 3/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00197V; at 4/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00023V; at 5/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00254V; at 6/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00311V; at 7/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00182V; at 8/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00054V; at 9/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00247V; at 10/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00267V; at 11/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00117V; at 12/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00108V; at 13/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00261V; at 14/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00234V; at 15/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00042V; at 16/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00188V; and determining the difference value of the pipeline stress wave and the background noise at each corresponding time point by analogy.
The absolute values of the differences of the pipeline stress wave and the background noise at the corresponding time points are respectively as follows: the absolute values of the differences between the pipeline stress wave and the background noise at each corresponding time point are respectively: at 1/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00171V; at 2/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00279V; at 3/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00197V; at 4/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00023V; at 5/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00254V; at 6/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00311V; at 7/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00182V; at 8/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00054V; at 9/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00247V; at 10/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00267V; at 11/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00117V; at 12/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00108V; at 13/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00261V; at 14/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00234V; at 15/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00042V; at 16/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00188V; and determining the absolute value of the difference value of the pipeline stress wave and the background noise at each corresponding time point by analogy.
The sum of the absolute values of the differences of the pipeline stress wave and the background noise at the corresponding time points is 0.02935V.
Step 103: and when the sum of the absolute values of the difference values at the corresponding time points is greater than the threshold value, determining the leakage state of the pipeline.
In step 103, according to the sum of the absolute values of the differences at the corresponding time points determined in step 102, when the sum of the absolute values of the differences at the corresponding time points is greater than a threshold, the line leakage state is determined.
The threshold is preset empirically, and is not particularly limited herein.
The technical method provided by the embodiment of the invention comprises the steps of collecting pipeline stress waves and background noise signals by using sensors with the same type, respectively carrying out normalization processing on the pipeline stress waves and the background noise signals, normalizing assignments in two arrays in the same time period, which are respectively monitored by the pipeline stress wave sensors and the background noise sensors, into the same interval, obtaining difference values of the pipeline stress waves and the background noise at corresponding time points in the same time period, calculating the sum of absolute values of the difference values at the corresponding time points, and determining the leakage state of a pipeline when the sum of the absolute values of the difference values at the corresponding time points is greater than a threshold value. The signal-to-noise ratio of the pressure wave signal is improved, the noise influence of peripheral noise is reduced, and the real-time monitoring of the pressure pipeline leakage is realized.
In another embodiment of the present invention, in the same time period, the FFT is performed on the difference values at each corresponding time point to obtain the coefficients at each corresponding time point, and when the sum of the absolute values of the coefficients at each corresponding time point is greater than a first threshold, the pipeline is determined to be in a suspected leakage state.
In the monitoring method of this embodiment, the sensors of the same model respectively acquire information of the pipeline stress wave and the background noise, obtain two sets of data of the same time period respectively monitored by the pipeline stress wave sensor and the background noise sensor, and perform normalization processing on the two sets of data to obtain the difference value of each corresponding time point.
For example: the time interval of adjacent sampling points is 1/7500 seconds, the signals of the pipeline stress wave and the background noise are respectively collected according to the sensors with the same model, and the pipeline stress wave signals are determined to be respectively: 1/7500 th second: 0.57164V; 2/7500 th second: 0.06561V; 3/7500 th second: -0.47738V; 4/7500 th second: -0.69833V; 5/7500 th second: -0.45361V; 6/7500 th second: 0.09978V; 7/7500 th second: 0.58922V; 8/7500 th second: 0.69649V; 9/7500 th second: 0.34913V; 10/7500 th second: -0.22669V; 11/7500 th second: -0.64323V; 12/7500 th second: -0.63115V; 13/7500 th second: -0.19473V; 14/7500 th second: 0.3769V; 15/7500 th second: 0.70241V; 16/7500 th second: 0.57228V; and determining the pipeline stress wave signal by analogy.
Determining the background noise signals are respectively: 1/7500 th second: 0.56993V; 2/7500 th second: 0.06282V; 3/7500 th second: -0.47935V; 4/7500 th second: -0.6981V; 5/7500 th second: -0.45107V; 6/7500 th second: 0.10289V; 7/7500 th second: 0.59104V; 8/7500 th second: 0.69595V; 9/7500 th second: 0.34666V; 10/7500 th second: -0.22936V; 11/7500 th second: -0.6444V; 12/7500 th second: -0.63007V; 13/7500 th second: -0.19212V; 14/7500 th second: 0.37924V; 15/7500 th second: 0.70283V; 16/7500 th second: 0.5704V; and determining the background noise signal by analogy.
The absolute values of the differences between the pipeline stress wave and the background noise at each corresponding time point are respectively: at 1/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00171V; at 2/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00279V; at 3/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00197V; at 4/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00023V; at 5/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00254V; at 6/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00311V; at 7/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00182V; at 8/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00054V; at 9/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00247V; at 10/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00267V; at 11/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00117V; at 12/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00108V; at 13/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00261V; at 14/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00234V; at 15/7500 seconds, the difference between the pipeline stress wave and the background noise is-0.00042V; at 16/7500 seconds, the difference between the pipeline stress wave and the background noise is 0.00188V; and determining the difference value of the pipeline stress wave and the background noise at each corresponding time point by analogy.
And performing FFT (fast Fourier transform) on the difference value of the pipeline stress wave and the background noise at each corresponding time point to obtain a coefficient of each corresponding time point.
The method comprises the steps of adopting a Fourier principle, and transforming absolute values of differences of stress waves and background noises of the pipeline at corresponding time points by using an FFT (fast Fourier transform) method to obtain
Taking the part of 0.2-5 KHZ to obtain the coefficient of each corresponding time point.
And when the sum of the absolute values of the coefficients of each corresponding time point is greater than a first threshold value, determining that the pipeline is in a suspected leakage state.
When the sum of the absolute values of the coefficients at the respective time points is greater than a first threshold value, the pipeline may be determined to be in a suspected leakage state.
That is, when the pipeline leaks, the acquired pipeline stress wave signal at the corresponding time point is strong, so that the difference value between the pipeline stress wave and the background noise at each corresponding time point is relatively large, the spectrum amplitude after FFT is large, the absolute value of the coefficient at the corresponding time point is also large under the same fundamental frequency, the sum of the absolute values of the coefficients at each corresponding time point of the pipeline stress wave and the background noise is also large, and when the sum of the absolute values of the coefficients at each corresponding time point is larger than a first threshold, it can be determined that the pipeline is in a suspected leakage state.
The first threshold is empirically preset, and is not particularly limited herein.
The technical method provided by the embodiment of the invention comprises the steps of collecting pipeline stress waves and background noise signals by using sensors with the same type, respectively carrying out normalization processing on the pipeline stress waves and the background noise signals, normalizing assignments in two arrays in the same time period, which are respectively monitored by the pipeline stress wave sensors and the background noise sensors, to the same interval, carrying out FFT (fast Fourier transform) on difference values of corresponding time points in the same time period by adopting a Fourier principle to obtain coefficients of the corresponding time points, and determining that the pipeline is in a suspected leakage state when the sum of absolute values of the coefficients of the corresponding time points is greater than a first threshold. The signal-to-noise ratio of the pressure wave signal is improved, the noise influence of peripheral noise is reduced, the filtering effect is achieved, and the real-time monitoring of pressure pipeline leakage is realized.
In another embodiment of the present invention, in the same time period, FFT transformation and difference are performed on the collected pipeline stress wave and the background noise signal to obtain coefficients of each corresponding time point, and when the sum of absolute values of the coefficients of each corresponding time point is greater than a second threshold, the pipeline is determined to be in a suspected leakage state.
In the monitoring method of this embodiment, the sensors of the same model respectively acquire information of the pipeline stress wave and the background noise, obtain two sets of data of the same time period respectively monitored by the pipeline stress wave sensor and the background noise sensor, and perform normalization processing on the two sets of data to obtain two sets of data of the pipeline stress wave and the background noise in the same interval at each corresponding time point.
For example: the time interval of adjacent sampling points is 1/7500 seconds, the signals of the pipeline stress wave and the background noise are respectively collected according to the sensors with the same model, and the pipeline stress wave signals are determined to be respectively: 1/7500 th second: 0.57164V; 2/7500 th second: 0.06561V; 3/7500 th second: -0.47738V; 4/7500 th second: -0.69833V; 5/7500 th second: -0.45361V; 6/7500 th second: 0.09978V; 7/7500 th second: 0.58922V; 8/7500 th second: 0.69649V; 9/7500 th second: 0.34913V; 10/7500 th second: -0.22669V; 11/7500 th second: -0.64323V; 12/7500 th second: -0.63115V; 13/7500 th second: -0.19473V; 14/7500 th second: 0.3769V; 15/7500 th second: 0.70241V; 16/7500 th second: 0.57228V; and determining the pipeline stress wave signal by analogy.
Determining the background noise signals are respectively: 1/7500 th second: 0.56993V; 2/7500 th second: 0.06282V; 3/7500 th second: -0.47935V; 4/7500 th second: -0.6981V; 5/7500 th second: -0.45107V; 6/7500 th second: 0.10289V; 7/7500 th second: 0.59104V; 8/7500 th second: 0.69595V; 9/7500 th second: 0.34666V; 10/7500 th second: -0.22936V; 11/7500 th second: -0.6444V; 12/7500 th second: -0.63007V; 13/7500 th second: -0.19212V; 14/7500 th second: 0.37924V; 15/7500 th second: 0.70283V; 16/7500 th second: 0.5704V; and determining the background noise signal by analogy.
The same time period is preset empirically and is not particularly limited.
It should be noted that the normalization processing method includes Min-max normalization (Min-max normalization)/z-score normalization, such as Min-max normalization (Min-max normalization): recording Max and Min in each array by traversing each data in the array, and carrying out normalization processing on the data by taking Max-Min as a base number: and normalizing the assignment values in the two arrays respectively monitored by the pipeline stress wave sensor and the background noise sensor in the same time period to be in the same interval, wherein the assignment values are not limited by specific numerical values.
After normalization processing, FFT conversion is carried out on two groups of data after normalization processing, which are respectively monitored by a pipeline stress wave sensor and a background noise sensor in the same time period, and difference is carried out to obtain coefficients of corresponding time points.
The method comprises the steps of adopting a Fourier principle, and transforming absolute values of differences of stress waves and background noises of the pipeline at corresponding time points by using an FFT (fast Fourier transform) method to obtain
Taking the part of 0.2-5 KHZ to obtain the coefficient of each corresponding time point.
And when the sum of the absolute values of the coefficients of each corresponding time point is greater than a second threshold value, determining that the pipeline is in a suspected leakage state.
When the sum of the absolute values of the coefficients at the respective time points is greater than a second threshold value, the pipeline may be determined to be in a suspected leakage state.
That is, when the pipeline leaks, the acquired pipeline stress wave signal corresponding to the time point is strong, the spectrum amplitude after FFT is large, the absolute value of the coefficient corresponding to the time point is also large under the condition of the same fundamental frequency, the sum of the absolute values of the coefficients corresponding to the time points of the pipeline stress wave and the background noise is also relatively large, and when the sum of the absolute values of the coefficients corresponding to the time points is larger than a second threshold, it can be determined that the pipeline is in a suspected leakage state.
It should be noted that the second threshold is preset empirically, and is not limited specifically here.
The technical method provided by the embodiment of the invention comprises the steps of collecting pipeline stress waves and background noise signals by using sensors with the same type, respectively carrying out normalization processing on the pipeline stress waves and the background noise signals, normalizing assignments in two arrays in the same time period, which are respectively monitored by the pipeline stress wave sensors and the background noise sensors, to the same interval, carrying out FFT (fast Fourier transform) on two sets of normalized data of each corresponding time point in the same time period by adopting a Fourier principle to obtain coefficients of each corresponding time point, and determining that the pipeline is in a suspected leakage state when the sum of absolute values of the coefficients of each corresponding time point is greater than a second threshold value. The signal-to-noise ratio of the pressure wave signal is improved, the noise influence of peripheral noise is reduced, the filtering effect is achieved, and the real-time monitoring of pressure pipeline leakage is realized.
In the monitoring method according to the embodiment, specifically, the sensor for collecting the stress wave of the pipeline is in hard connection with the metal part of the pipeline to be detected through a screw or a strong magnet.
Specifically, the sensor for collecting background noise is arranged in soil around the pipeline or a well wall.
Fig. 2 is a schematic structural diagram of a pipeline leakage monitoring device. The device comprises an acquisition module 201, a data processing module 202 and an identification module 203, wherein:
the acquisition module 201 is used for acquiring pipeline stress waves and background noise;
the data processing module 202 is configured to make a difference between the acquired pipeline stress wave and the background noise, and calculate a sum of absolute values of differences at each corresponding time point;
the identification module 203 is used for determining a pipeline leakage state.
Preferably, the data processing module 202 is configured to perform a difference between the acquired pipeline stress wave and the background noise, and calculate a sum of absolute values of difference values at each corresponding time point; the identification module 203 determines a pipeline leakage state, and the data processing module 202 and the identification module 203 may adopt any one of the following data processing and identification processes:
and in the same time period, acquiring pipeline stress waves and background noise, obtaining differences of the pipeline stress waves and the background noise at each corresponding time point, and determining the leakage state of the pipeline when the sum of absolute values of the differences at each corresponding time point is greater than a threshold value.
Or, in the same time period, performing FFT on the difference values of the corresponding time points to obtain coefficients of the corresponding time points, and when the sum of the absolute values of the coefficients of the corresponding time points is greater than a first threshold, determining that the pipeline is in a suspected leakage state.
Or, in the same time period, performing FFT on the collected pipeline stress wave and the background noise signal and performing difference to obtain the coefficients of each corresponding time point, and when the sum of the absolute values of the coefficients of each corresponding time point is greater than a second threshold, determining that the pipeline is in a suspected leakage state.
Furthermore, a sensor for collecting stress waves of the pipeline is in hard connection with a metal part of the pipeline to be detected through a screw or a strong magnet, and the sensor for collecting background noise is arranged in soil around the pipeline or a well wall.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A pipeline leakage monitoring method is characterized in that pipeline stress waves and background noise are collected, difference values of the pipeline stress waves and the background noise at corresponding time points are obtained, and when the sum of absolute values of the difference values of the corresponding time points is larger than a threshold value, a pipeline leakage state is determined.
2. The method according to claim 1, wherein the difference values at the corresponding time points are FFT-transformed in the same time period to obtain coefficients at the corresponding time points, and when the sum of the absolute values of the coefficients at the corresponding time points is greater than a first threshold, the pipeline is determined to be in a suspected leakage state.
3. The method according to claim 1, wherein in the same time period, FFT transformation and difference are performed on the collected pipeline stress wave and background noise signal to obtain coefficients of each corresponding time point, and when the sum of the absolute values of the coefficients of each corresponding time point is greater than a second threshold, the pipeline is determined to be in a suspected leakage state.
4. The pipeline leakage monitoring method according to any one of claims 1 to 3, wherein the sensor for collecting the pipeline stress wave is hard-connected with the metal part of the pipeline to be monitored through a screw or a strong magnet.
5. The pipeline leakage monitoring method according to any one of claims 1 to 3, wherein the sensor for collecting background noise is arranged in soil around the pipeline or in a well wall.
6. A line leak monitoring apparatus, comprising: the system comprises an acquisition module, a data processing module and an identification module;
the acquisition module is used for acquiring pipeline stress waves and background noise;
the data processing module is used for making a difference between the acquired pipeline stress wave and the background noise and calculating the sum of absolute values of the difference values of all corresponding time points;
the identification module is used for determining a pipeline leakage state.
7. The line leak monitoring device of claim 6, wherein the line stress wave and the background noise are collected to obtain differences between the line stress wave and the background noise at each corresponding time point, and the line leak condition is determined when the sum of the absolute values of the differences at each corresponding time point is greater than a threshold value.
8. The apparatus according to claim 6, wherein the difference values at the corresponding time points are FFT-transformed in the same time period to obtain coefficients at the corresponding time points, and when the sum of the absolute values of the coefficients at the corresponding time points is greater than the first threshold, the pipeline is determined to be in a suspected leakage state.
9. The pipeline leakage monitoring device according to claim 6, wherein in the same time period, the collected pipeline stress wave and the background noise signal are FFT-transformed and subtracted to obtain coefficients of each corresponding time point, and when the sum of the absolute values of the coefficients of each corresponding time point is greater than the second threshold, the pipeline is determined to be in a suspected leakage state.
10. The pipeline leakage monitoring device according to any one of claims 6 to 9, wherein the sensor for collecting the stress wave of the pipeline is hard-connected with the metal part of the pipeline to be detected through a screw or a strong magnet, and the sensor for collecting the background noise is arranged in the soil around the pipeline or the wall of the well.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE3607913C1 (en) * | 1986-03-10 | 1987-08-13 | Fraunhofer Ges Forschung | Method for locating leaks in pipelines |
| JP2014074654A (en) * | 2012-10-04 | 2014-04-24 | Nihon Suiko Consultant:Kk | Detection method of abnormal place of low hydraulic pressure water conduction facility |
| CN104132250A (en) * | 2014-07-14 | 2014-11-05 | 上海师范大学 | Pipeline leakage feature vector extraction method based on improved wavelet packet |
| CN104633461A (en) * | 2014-12-31 | 2015-05-20 | 天津大学 | PCCP pipe failure forewarning and monitoring signal processing method based on sound signals |
| CN106157173A (en) * | 2016-07-15 | 2016-11-23 | 杭州电子科技大学 | Public supply mains booster tuning on-line method based on SCADA data and real-time model |
| CN106907577A (en) * | 2017-04-19 | 2017-06-30 | 广西壮族自治区气象技术装备中心 | A kind of gas pipe leakage Acoustic Emission location method |
| CN107228700A (en) * | 2016-03-25 | 2017-10-03 | 艾默生过程控制流量技术有限公司 | For detecting the apparatus and method that the electrode of electromagnetic flowmeter is leaked |
| CN109357171A (en) * | 2018-11-30 | 2019-02-19 | 北京无线电计量测试研究所 | A kind of monitoring of underground pipeline leak and localization method and device |
-
2019
- 2019-10-08 CN CN201910951303.4A patent/CN110701490B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE3607913C1 (en) * | 1986-03-10 | 1987-08-13 | Fraunhofer Ges Forschung | Method for locating leaks in pipelines |
| JP2014074654A (en) * | 2012-10-04 | 2014-04-24 | Nihon Suiko Consultant:Kk | Detection method of abnormal place of low hydraulic pressure water conduction facility |
| CN104132250A (en) * | 2014-07-14 | 2014-11-05 | 上海师范大学 | Pipeline leakage feature vector extraction method based on improved wavelet packet |
| CN104633461A (en) * | 2014-12-31 | 2015-05-20 | 天津大学 | PCCP pipe failure forewarning and monitoring signal processing method based on sound signals |
| CN107228700A (en) * | 2016-03-25 | 2017-10-03 | 艾默生过程控制流量技术有限公司 | For detecting the apparatus and method that the electrode of electromagnetic flowmeter is leaked |
| CN106157173A (en) * | 2016-07-15 | 2016-11-23 | 杭州电子科技大学 | Public supply mains booster tuning on-line method based on SCADA data and real-time model |
| CN106907577A (en) * | 2017-04-19 | 2017-06-30 | 广西壮族自治区气象技术装备中心 | A kind of gas pipe leakage Acoustic Emission location method |
| CN109357171A (en) * | 2018-11-30 | 2019-02-19 | 北京无线电计量测试研究所 | A kind of monitoring of underground pipeline leak and localization method and device |
Non-Patent Citations (2)
| Title |
|---|
| 李媛等: "基于扩展谱减法的语音增强算法及实现", 《内蒙古大学学报(自然科学版)》 * |
| 潘碧霞等: "管道泄漏声发射信号的传播特性", 《油气储运》 * |
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