CN110987318B - Automatic detection device and detection method for gas leakage of high-pressure pipeline - Google Patents
Automatic detection device and detection method for gas leakage of high-pressure pipeline Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
- G01M3/04—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point
- G01M3/24—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using infrasonic, sonic, or ultrasonic vibrations
- G01M3/243—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using infrasonic, sonic, or ultrasonic vibrations for pipes
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- 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
- F17D5/005—Protection or supervision of installations of gas pipelines, e.g. alarm
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- 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
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- F17D5/06—Preventing, monitoring, or locating loss using electric or acoustic means
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- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
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Abstract
The invention discloses an automatic detection device for gas leakage of a high-pressure pipeline, which comprises an outer collar, wherein a screw fixer for fastening the outer collar on the high-pressure pipeline is arranged on the outer collar, a mirror reflector is arranged at the outer edge of the outer collar, a shell sleeved outside the high-pressure pipeline is arranged outside the outer collar, a shell support is arranged at one end of the shell, a shell base supported on the horizontal plane is arranged at one end of the shell support, a sound receiver is arranged inside the high-pressure pipeline, and the sound receiver is fixed on the inner wall of the high-pressure pipeline through a pipeline support. The invention realizes the real-time detection of the three-dimensional vibration signals of the pipeline by utilizing the outer sleeve ring and the three groups of range finders, and realizes the high-precision automatic detection and positioning of the gas leakage of the high-pressure pipeline by utilizing an artificial intelligence method in combination with the real-time sound signals in the pipeline.
Description
Technical Field
The invention relates to the technical field of pipeline detection, in particular to an automatic detection device and a detection method for gas leakage of a high-pressure pipeline.
Background
China is one of 13 water-deficient countries in the world nowadays, the upgrading and the reconstruction of sewage treatment plants must depend on technological progress, and new technology and high-tech guarantee and support are required all the time. In the sewage treatment process of sewage treatment plants, there are a large number of high-pressure gas pipelines. Due to aging, corrosion, poor sealing performance and the like, gas leakage often occurs in the gas transmission pipelines, which can seriously affect the normal sewage treatment process and cause the loss of manpower and capital. When the pipeline has gas leakage, the sprayed gas flow can cause the vibration of the gas pipeline and generate strong air vibration due to the large difference of the internal pressure and the external pressure of the pipeline, and further the buzzing sound is caused.
Among the automatic detection techniques for gas leakage in high-pressure pipelines, the conventional detection techniques can be classified into three categories: the first type, automatic detection of gas leakage is achieved by detecting acoustic signals around the high-pressure pipe; the second type, automatic detection of gas leakage is realized by detecting vibration or temperature change of a high-pressure pipeline; and in the third category, the automatic detection of gas leakage is realized by detecting the type of gas around the high-pressure pipeline through physical or chemical reaction. The high-pressure gas of the sewage treatment plant is generally referred to as air, so that the third method cannot be used for detection, and the first method is easily interfered and has a limited detection distance, so that the second method is generally selected. In the patent (application No. 201110272386.8), optical fiber sensors are arranged at intervals along the longitudinal direction of the pipeline, and after the vibration of the pipeline is sensed by the optical fiber sensors, the vibration signal is transmitted back to a control center by using communication optical fibers arranged in parallel along the pipeline. Although the method can detect the vibration signal of the pipeline, the method compresses the vibration signal of three dimensions into one dimension, cannot comprehensively reflect the vibration signal of the pipeline, and does not utilize other types of signals. In the patent (application No. 201710790647.2), it transversely encircles the response ring along the pipeline around, and the response ring passes through the connecting band and links to each other with the elastic plate of pipeline below, has laid sensing optical fiber on the elastic plate, through sensing optical fiber detection pipeline vibration, and then detect the pipeline and leak. In the patent (application No. 201711424985.0), sensing optical fibers are arranged in parallel along the longitudinal direction of the circumference of the heat distribution pipeline, and the temperature change and noise disturbance of the heat distribution pipeline are sensed through the sensing optical fibers, so that the pipeline leakage is detected. Although the two methods can detect the vibration signal of the pipeline, the two methods are indirect detection methods, and the vibration signals of three dimensions are compressed into one dimension, so that the vibration signal of the pipeline cannot be comprehensively and accurately reflected, and other types of signals are not utilized. Therefore, in order to comprehensively and accurately detect the three-dimensional vibration signal of the pipeline and realize the automatic detection of the gas leakage with higher precision by fusing various signals of different types. This patent utilizes artificial intelligence detection technology to fuse the signal of multiple different grade type through the three-dimensional vibration signal and the inside sound signal of pipeline that detect the pipeline, has realized the gas leakage automated inspection of higher accuracy.
Disclosure of Invention
The invention aims to provide an automatic detection device for gas leakage of a high-pressure pipeline, which comprises an outer collar, wherein a screw fixer for fastening the outer collar on the high-pressure pipeline is arranged on the outer collar, a mirror reflector is arranged at the outer edge of the outer collar, a shell sleeved outside the high-pressure pipeline is arranged outside the outer collar, a shell support is arranged at one end of the shell, a shell base supported on a horizontal plane is arranged at one end of the shell support, interference range finders are arranged on the inner top, the left side wall and the inner bottom of the shell, a sound receiver is arranged inside the high-pressure pipeline, and the sound receiver is fixed on the inner wall of the high-pressure pipeline through a pipeline support.
Preferably, the outer collar, the screw holder and the specular reflector are all rigid bodies.
Preferably, the sound receiver is located at the center of the high pressure pipe, and is flush with the outer collar along the pipe.
Preferably, the sound receiver and the interferometric distance meter are both connected to the processor by signals.
The invention also provides a detection method of the automatic detection device for the gas leakage of the high-pressure pipeline, which comprises the following specific steps:
s1: firstly, respectively intercepting a section of time domain signal on eight time domain signals from two different position detectors by taking fixed duration as a window;
s2: then, respectively carrying out signal transformation on the eight time domain signals to obtain a time-frequency graph of the eight time domain signals, wherein the time-frequency graph is an input picture with the depth of eight;
s3: sending the time-frequency diagram into a convolutional neural network, and finally obtaining a feature vector through feature extraction;
s4: sending the characteristic vector into a long-short term memory neural network, and obtaining a classification result and a fitting result of the characteristic vector through the long-short term memory neural network;
s5: and finally, sliding the window with the fixed duration and the interval, and repeating the steps to detect the classification result and the fitting result of each window.
Preferably, the signal converter is a mathematical transformation capable of extracting a short-time spectrum of a time-domain signal, and transforms the input time-domain signal into a one-to-one corresponding time-frequency diagram;
the input time domain signals are eight sets of time domain signals from two different position detectors, each detector containing three sets of vibration signals and one set of sound signals.
Preferably, the time-frequency diagram is a time-frequency diagram generated by signal transformation for a plurality of groups of time-domain signals.
Preferably, the convolutional neural network is a neural network comprising a plurality of convolutional layers, pooling layers and dropout layers, and the network needs to be trained in advance by using historical data.
Preferably, the feature vector time-frequency diagram is a one-dimensional feature vector generated by a convolutional neural network.
Preferably, the long-short term memory neural network is used for classifying and fitting the feature vectors extracted in the previous step, and the network needs to be trained by using historical data in advance.
Compared with the prior art, the invention has the beneficial effects that: the invention realizes the real-time detection of the three-dimensional vibration signals of the pipeline by utilizing the outer sleeve ring and the three groups of range finders, and realizes the high-precision automatic detection and positioning of the gas leakage of the high-pressure pipeline by utilizing an artificial intelligence method in combination with the real-time sound signals in the pipeline. Therefore, the invention solves the problem that the three-dimensional vibration signals of the pipeline cannot be collected and different types of signals are fused for high-precision automatic detection of gas leakage. The precision of automatic detection of the gas leakage of the high-pressure pipeline is improved, and the application range of the automatic detection device of the gas leakage of the high-pressure pipeline is expanded.
Drawings
FIG. 1 is a schematic view of the installation of the sound receiver of the present invention;
FIG. 2 is one of the overall installation schematic of the present invention;
FIG. 3 is a second schematic view of the overall installation of the present invention;
FIG. 4 is a third schematic view of the overall installation of the present invention;
FIG. 5 is a fourth view of the overall installation of the present invention;
FIG. 6 is a flow chart of the detection method of the present invention.
In the figure: 1 high-pressure pipeline, 2 outer collars, 3 screw fasteners, 4 mirror reflectors, 5 interferometric distance measuring instruments, 6 shells, 7 shell supports, 8 shell bases, 9 processors, 10 pipe inner supports and 11 sound receivers.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
An automatic detection device for gas leakage of a high-pressure pipeline comprises an outer collar 2, a screw fixer 3, a mirror reflector 4, an interference distance meter 5, a shell 6, a shell support 7, a shell base 8, a processor 9, an inner support 10 and a sound receiver 11;
the outer sleeve 2 is a steel detachable sleeve ring, which is not in direct contact with the pipeline 1, but is fixed with the high-pressure pipeline 1 through a screw fixer 3, and a mirror reflector 4 is fixed on the screw fixer;
the screw fixing devices 3 are steel screws which are uniformly distributed on the outer sleeve ring 2, and the outer sleeve ring 2 can be fixed on the pipeline 1 by controlling the depth of each screw entering the outer sleeve ring 2;
the mirror reflector 4 is a steel cube, is fixed right above the outer collar 2, is covered with a reflector on the surface, and can be used for matching with the interference distance meter 5 to realize accurate relative distance measurement;
the interference range finder 5 is a laser interference range finder which has three groups and is respectively fixed inside the shell 6, and the specific position is on a three-dimensional coordinate axis relative to the mirror reflector 4;
the shell 6 is a steel shell and is used for fixing the three groups of interferometric distance meters 5;
the shell bracket 7 is a steel cylinder, and the shell bracket 7 is used for connecting the shell 6 and the shell base 8 together;
the shell base 8 is a steel shell base, and the shell base 8 is connected with the shell bracket 7 and used for fixing the shell 6;
the pipe inner support 10 is a steel cross and is used for fixing the sound receiver 11 inside the high-pressure pipeline 1;
the sound receiver 11 is fixed at the right center inside the pipeline 1 and used for monitoring sound signals inside the high-pressure pipeline 1, and the longitudinal position of the sound receiver is flush with the outer collar 2;
the processor 9 is responsible for collecting continuous signals from three groups of interferometric distance meters 5 and one group of sound receivers 11, and combining four groups of detection signals transmitted by detectors at other positions, and processing the eight groups of signals by an artificial intelligence method to obtain a real-time detection result;
the working process is as follows: when gas leakage occurs in the high-pressure pipeline 1, the high-pressure pipeline 1 vibrates in high frequency and buzzes, and vibration signals and the buzzes are transmitted to two sides along the pipeline 1; when the vibration signal is transmitted to the position of the detector, the vibration signal is transmitted to the outer ring 2 through the screw fixing device 3, the outer ring 2 is transmitted to the mirror reflector 4, and meanwhile, the three groups of interference distance measuring instruments 5 record the vibration signal in real time and transmit the vibration signal to the processor 9; meanwhile, buzzing sound is transmitted to two sides along the inside of the high-pressure pipeline 1, and when sound signals are transmitted to the position of the detector, the sound signals are received by the sound receiver 11 and transmitted to the processor 9 in real time; the processor 9 analyzes the vibration signals and sound signals from the two detectors at different positions in real time, and obtains whether the gas leaks or not and the specific leakage position through an artificial intelligence method, and finally the leakage position is only positioned between the two detectors at different positions.
The method also comprises a high-pressure pipeline gas leakage automatic detection method based on three-dimensional vibration and sound. Inputting a time domain signal, short-time FFT (fast Fourier transform), a time-frequency graph, a convolutional neural network, a characteristic vector, an LSTM neural network, a classification result and a fitting result;
the input time domain signals are eight groups of time domain signals from different position detectors, and each detector comprises three groups of vibration signals and one group of sound signals;
the short-time FFT is a windowed shifted Fourier transform;
the time-frequency diagram is a time-frequency diagram with the depth of eight generated by short-time FFT conversion of an input time-domain signal;
the convolutional neural network is a neural network comprising 5 convolutional layers, 5 pooling layers and 1 dropout layer;
the feature vector is a one-dimensional feature vector generated by a time-frequency graph with the depth of eight through a convolutional neural network;
the LSTM neural network is used for classifying and fitting the feature vectors extracted in the previous step, and the network needs to be trained by using historical data in advance;
the classification result is a final judgment result on whether leakage exists or not, and the fitting result is an estimation result on a final leakage position;
the signal processing flow comprises the following steps: firstly, respectively intercepting a section of time domain signal on eight input time domain signals of a detection device from two positions by taking fixed duration as a window, and then respectively carrying out FFT (fast Fourier transform) on the eight time domain signals to obtain a time frequency graph with the depth of eight; sending the time-frequency diagram into a convolutional neural network, and finally obtaining a feature vector through feature extraction; sending the feature vector into an LSTM neural network, and obtaining a classification result and a fitting result of the feature vector through the LSTM neural network; and finally, sliding the window with the fixed duration and the interval, and repeating the steps to detect the classification result and the fitting result of each window.
The working principle is as follows: the short-time Fourier transform is to decompose the whole time domain process into a plurality of small processes with equal length, each small process is approximately stable, then the Fourier transform is carried out to know what frequency appears at which time point, then the frequency distribution curve obtained at each time point is listed as a vertical coordinate, the time points are listed as horizontal coordinates, and a time-frequency graph can be obtained. The process of dividing the time domain is actually windowing, the length of the window needs to be moderate, and the window is too wide and too narrow. The window is too narrow, and the signal in the window is too short, which may result in inaccurate frequency analysis and poor frequency resolution. The window is too wide, not fine enough in time domain, and low in time resolution.
A Recurrent Neural Network (RNN) is a powerful type of neural network, one of the most promising algorithms at present, the only one with internal memory. As with many other deep learning algorithms, RNNs are also relatively old. The LSTM network is an extension of the RNN network that extends the memory function of the RNN network, which is well suited to learning from important experiences with long delays. The LSTM network is capable of including input information in its memory, much like the memory of a computer, from which it can read, write, and delete information.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (10)
1. The utility model provides a high pressure pipeline gas leakage automatic checkout device, includes the detector of two different positions, and wherein every detector includes outer collar (2), its characterized in that: be equipped with on outer thimble (2) and fasten screw fixation ware (3) on high pressure line (1) with outer thimble (2), outer thimble (2) outer fringe department is provided with mirror reflector (4), and is equipped with shell (6) of cover in the high pressure line (1) outside in the outer thimble (2) outside, shell (6) one end is equipped with shell support (7), shell support (7) one end is equipped with and supports in the shell base (8) of horizontal plane, all be equipped with interference range finder (5) on top, left side wall and the preceding inner wall in shell (6), and the concrete position is for on the three-dimensional coordinate axis of mirror reflector (4), high pressure line (1) inside is equipped with sound receiver (11), and sound receiver (11) are fixed in on high pressure line (1) inner wall through pipe support (10), and treater (9) are responsible for collecting the continuous signal that comes from three groups interference range finder (5) and a set of sound receiver (11) and combine it Four groups of detection signals transmitted by the detectors at other positions process the eight groups of signals to obtain real-time detection results.
2. The automatic detection device for gas leakage of high-pressure pipeline according to claim 1, characterized in that: the outer collar (2), the screw fixer (3) and the mirror reflector (4) are all rigid bodies.
3. The automatic gas leakage detection device for the high-pressure pipeline according to claim 1, wherein: the sound receiver (11) is positioned at the center of the high-pressure pipeline (1) and is level with the outer sleeve ring (2) along the pipeline.
4. The automatic gas leakage detection device for the high-pressure pipeline according to claim 1, wherein: and the sound receiver (11) and the interference distance meter (5) are both connected to the processor (9) through signals.
5. A detection method of the automatic detection device for gas leakage of high-pressure pipeline according to claim 1, characterized in that: the method comprises the following specific steps:
s1: firstly, respectively intercepting a section of time domain signal on eight time domain signals from two different position detectors by taking fixed duration as a window;
s2: then, respectively carrying out signal transformation on the eight time domain signals to obtain a time-frequency graph of the eight time domain signals, wherein the time-frequency graph is an input picture with the depth of eight;
s3: sending the time-frequency diagram into a convolutional neural network, and finally obtaining a feature vector through feature extraction;
s4: sending the characteristic vector into a long-short term memory neural network, and obtaining a classification result and a fitting result of the characteristic vector through the long-short term memory neural network;
s5: and finally, sliding the window with the fixed duration and the interval, and repeating the steps to detect the classification result and the fitting result of each window.
6. The detection method of the automatic detection device for the gas leakage of the high-pressure pipeline according to claim 5, characterized in that: the signal converter is a mathematical transformation which can extract the short-time frequency spectrum of the time domain signal and transforms the input time domain signal into a time-frequency diagram which corresponds to one; the input time domain signals are eight sets of time domain signals from two different position detectors, each detector containing three sets of vibration signals and one set of sound signals.
7. The detection method of the automatic detection device for the gas leakage of the high-pressure pipeline according to claim 5, characterized in that: the time-frequency diagram is generated by signal transformation aiming at a plurality of groups of time-domain signals.
8. The detection method of the automatic detection device for the gas leakage of the high-pressure pipeline according to claim 5, characterized in that: the convolutional neural network is a neural network comprising several convolutional layers, pooling layers and dropout layers, which needs to be trained in advance using historical data.
9. The detection method of the automatic detection device for the gas leakage of the high-pressure pipeline according to claim 5, characterized in that: the feature vector is a one-dimensional feature vector generated by the time-frequency diagram through a convolutional neural network.
10. The detection method of the automatic detection device for the gas leakage of the high-pressure pipeline according to claim 5, characterized in that: the long-short term memory neural network is used for classifying and fitting the feature vectors extracted in the previous step, and the network needs to be trained by using historical data in advance.
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