Valve leakage detection method and device

Abstract

The invention discloses a valve leakage detection method, which comprises the following steps of: acquiring an acoustic emission signal of a pressure pipeline by using an acoustic emission sensor and amplifying the acoustic emission signal; respectively carrying out signal noise reduction processing and leakage signal feature extraction on the amplified acoustic emission signal and the non-leakage acoustic emission signal to correspondingly obtain noise-filtered leakage acoustic emission data and normal acoustic emission data; inputting the normal acoustic emission data into the GRU model to obtain prediction data, and determining a confidence rate value according to a difference value between the prediction data and real data; respectively inputting the normal acoustic emission data and the leakage acoustic emission data into the GRU model, calculating to obtain a normal difference value and a leakage difference value, and respectively comparing the normal difference value and the leakage difference value with confidence rate values to obtain a normal confidence rate and a leakage confidence rate; and calculating a difference value between the normal confidence rate and the leakage confidence rate to obtain a confidence rate difference value, if the confidence rate difference value is smaller than a preset value, judging that leakage does not occur, otherwise, judging that leakage occurs. Real-time and accurate detection of internal leakage of the valve is realized.

Description

Technical Field

[0001] The present invention relates to the field of valve detection technology, and more particularly to a valve leakage detection method and device. Background Art

[0002] At present, valve leakage mainly includes two forms: internal leakage and external leakage. When external leakage occurs, the sound of airflow can be heard, which is easy to detect. If the valve leaks internally, the leakage is highly concealed and difficult to detect, which can easily lead to safety hazards. Although there is a set of mature detection technologies for valve external leakage detection, valve internal leakage still mainly relies on traditional detection methods, such as manual inspection, ultrasonic detection, infrared temperature measurement, etc., but these methods all have certain defects, such as consuming manpower, being limited by the environment, and being unable to detect small leaks.

[0003] Patent CN202121538364.7 "An Ultrasonic Valve Leakage Detection Device" proposes an ultrasonic valve leakage detection device, including a base, an ultrasonic leakage sensor, a control chip and a status indicator light. The base is provided with a fixing assembly, and the cavity of the base is provided with an upper fixing block, a slide plate, a bidirectional screw, a left connecting rod and a right connecting rod. A lower fixing block is provided on the top of the slide plate, and an ultrasonic leakage sensor is provided on the bottom of the slide plate. The ultrasonic leakage sensor is aligned with the through hole, and the bidirectional screw is transversely arranged in the cavity and its right end passes through the base. A left slider and a right slider are provided on both sides of the bidirectional screw. The left slider / right slider is hinged to the upper fixing block and the lower fixing block through the left connecting rod / right connecting rod respectively. The ultrasonic leakage sensor is connected to the control chip, and the control chip is connected to the status indicator light. It effectively solves the problem that the existing valve fails to detect and repair the leakage in time when internal leakage occurs, increases production costs, causes economic losses, and even leads to major safety accidents.

[0004] However, existing patents for detecting internal leakage of valves are more focused on detection through mechanical mechanisms and alarm devices. The detection devices occupy a large space and are limited by the working environment of the valves. They can only be used in the quality inspection stage after the valves are produced, and cannot perform real-time detection on valves in use.

[0005] Therefore, how to perform real-time and accurate internal leakage detection on valves in use is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0006] In view of this, the present invention provides a valve leakage detection method and device, which realizes real-time and accurate detection of internal leakage of a valve in use.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A valve leakage detection method, comprising:

[0009] Acquiring an acoustic emission signal from a pressure pipe by means of an acoustic emission sensor, and amplifying the acoustic emission signal;

[0010] The amplified acoustic emission signal and the non-leaked acoustic emission signal are subjected to signal noise reduction processing and leakage signal feature extraction respectively, and leakage acoustic emission data with noise filtered out and normal acoustic emission data with noise filtered out are obtained accordingly;

[0011] Inputting the normal acoustic emission data into the GRU model to obtain predicted data, and determining a confidence value according to the difference between the predicted data and the real data;

[0012] The normal acoustic emission data and the leakage acoustic emission data are respectively input into the GRU model, and a normal difference and a leakage difference are obtained by calculation;

[0013] Compare the normal difference and the leakage difference with the confidence rate value respectively to obtain a normal confidence rate and a leakage confidence rate;

[0014] The difference between the normal confidence rate and the leakage confidence rate is calculated to obtain a confidence rate difference. If the confidence rate difference is less than a preset value, it is determined that no leakage has occurred; otherwise, it is determined that a leakage has occurred.

[0015] Preferably, the amplified acoustic emission signal and the non-leaked acoustic emission signal are subjected to signal noise reduction processing and leakage signal feature extraction, respectively, specifically including:

[0016] Decomposing the collected acoustic emission signal layer by layer by using wavelet packet decomposition method to obtain frequency domain signals of multiple frequency bands;

[0017] Determining the frequency band of the leakage acoustic emission signal according to the spectrum peak frequency band of the frequency domain signal;

[0018] The acoustic emission signal is reconstructed by combining the leakage acoustic emission signal frequency band through a threshold denoising method, and noise in other frequency bands in the environment outside the frequency band where the acoustic emission signal is located is filtered out.

[0019] Preferably, the wavelet packet decomposition method adopts the db3 wavelet function, selects 4 layers of decomposition, and obtains frequency domain signals of 16 frequency bands.

[0020] Preferably, the reconstructing the acoustic emission signal and filtering out noise in other frequency bands also includes: combining the frequency band of the leakage acoustic emission signal, retaining the wavelet packet node coefficients of the frequency band, reducing the wavelet packet node coefficients of noise signals in other frequency bands to 0, reconstructing the acoustic emission signal, and filtering out noise in other frequency bands in the environment outside the frequency band where the acoustic emission signal is located.

[0021] Preferably, determining the confidence value according to the difference between the predicted data and the actual data specifically includes:

[0022] The difference between the predicted data and the real data is calculated, the difference is sorted from small to large, the first 99% of the difference is selected as the first credible data, and the largest value in the first credible data is set as the confidence value.

[0023] Preferably, obtaining the normal difference and the leakage difference by calculation specifically includes:

[0024] Inputting the normal acoustic emission data and the leakage acoustic emission data into the GRU model respectively to obtain normal prediction data and leakage prediction data;

[0025] Calculating the difference between the normal prediction data and the normal real data to obtain a normal difference; calculating the difference between the leakage prediction data and the leakage real data to obtain a leakage difference;

[0026] Preferably, the step of comparing the normal difference and the leakage difference with the confidence rate value to obtain a normal confidence rate and a leakage confidence rate also includes:

[0027] The normal difference and the leakage difference are respectively compared with the confidence value, the data less than the confidence value is the credible data, and the data greater than the confidence value is the uncredible data, to obtain normal credible data, normal uncredible data, leakage credible data and leakage uncredible data;

[0028] A normal confidence rate is determined according to the normal trusted data and the normal untrusted data; and a leakage confidence rate is determined according to the leakage trusted data and the leakage untrusted data.

[0029] A valve leakage detection device, comprising: an acoustic emission sensor, a preamplifier and a signal acquisition processing module, the signal acquisition processing module comprising: a signal preprocessing module and an internal leakage diagnosis module, the acoustic emission sensor is electrically connected to the preamplifier, the preamplifier is electrically connected to the signal preprocessing module, and the signal preprocessing module is electrically connected to the internal leakage diagnosis module;

[0030] The acoustic emission sensor is used to collect the acoustic emission signal of the pressure pipeline and send the acoustic emission signal to the preamplifier;

[0031] The preamplifier is used to receive the acoustic emission signal sent by the acoustic emission sensor and amplify the signal, and send the amplified acoustic emission signal to the signal preprocessing module;

[0032] The signal preprocessing module is used to receive the amplified acoustic emission signal and the non-leaking acoustic emission signal, perform signal noise reduction processing and leakage signal feature extraction respectively, and obtain leakage acoustic emission data with noise filtered out and normal acoustic emission data with noise filtered out; send the leakage acoustic emission data and the normal acoustic emission data with noise filtered out to the internal leakage diagnosis module;

[0033] The internal leakage diagnosis module is a gated recurrent unit neural network GRU, which is used to receive the normal acoustic emission data, obtain predicted data, and determine the confidence value according to the difference between the predicted data and the real data;

[0034] It is also used to calculate a normal difference and a leakage difference based on the leakage acoustic emission data and the normal acoustic emission data; compare the normal difference and the leakage difference with the confidence rate value respectively to obtain a normal confidence rate and a leakage confidence rate; calculate the difference between the normal confidence rate and the leakage confidence rate to obtain a confidence rate difference; if the confidence rate difference is less than a preset value, it is determined that no leakage has occurred, otherwise it is determined that a leakage has occurred.

[0035] Preferably, it also includes: a preamplifier power supply separation signal device electrically connected to the preamplifier and a power supply module electrically connected to the signal acquisition and processing module, the preamplifier power supply separation signal device is used to power the preamplifier, and the power supply module is used to power the signal acquisition and processing module.

[0036] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a valve leakage detection method and device, which has the following advantages:

[0037] 1. The acoustic emission sensor can collect weak leakage signals, amplify the collected signals, reduce noise and extract leakage signal feature values ​​to make the detection results more accurate.

[0038] 2. The gated recurrent unit neural network GRU uses time series to identify valve failures. It is very sensitive to long time series data and can preserve long-term memory, which is used to correct the information contained in short-term memory. It can also accurately identify leaks when the leakage is small.

[0039] 3. The gated recurrent unit neural network GRU is used to effectively shorten the training time of the model while ensuring high prediction accuracy, thereby improving the efficiency of valve leakage fault identification.

[0040] 4. The device of the present invention has a simple structure, a small size, and an independent power supply unit. It can be used in various narrow spaces for valve leakage detection, has a wide range of application scenarios, and is highly practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0042] Figure 1 The accompanying drawing is a flow chart of the detection method provided by the present invention.

[0043] Figure 2 The accompanying drawing is a connection schematic diagram of the detection device provided by the present invention.

[0044] Figure 3 The accompanying drawing is a flow chart of signal noise reduction processing and leakage signal feature extraction provided by the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] like Figure 1 As shown, an embodiment of the present invention discloses a valve leakage detection method, comprising:

[0047] Acoustic emission signals of pressure pipes are collected by acoustic emission sensors, and the acoustic emission signals are amplified and processed;

[0048] The amplified acoustic emission signal and the non-leaking acoustic emission signal are subjected to signal noise reduction processing and leakage signal feature extraction respectively, and the leakage acoustic emission data after noise filtering and the normal acoustic emission data after noise filtering are obtained accordingly;

[0049] Input normal acoustic emission data into the GRU model to obtain predicted data, and determine the confidence value based on the difference between the predicted data and the actual data;

[0050] The normal acoustic emission data and the leakage acoustic emission data are input into the GRU model respectively, and the normal difference and the leakage difference are obtained by calculation;

[0051] Compare the normal difference and the leakage difference with the confidence rate value respectively to obtain the normal confidence rate and the leakage confidence rate;

[0052] The difference between the normal confidence rate and the leakage confidence rate is calculated to obtain the confidence rate difference. If the confidence rate difference is less than a preset value, it is determined that no leakage has occurred; otherwise, it is determined that a leakage has occurred.

[0053] Preferably, under the same pressure, when the valve leaks, the confidence rate difference will change significantly. As the leakage rate increases, the confidence rate deviation also increases. By the confidence rate deviation, it can be judged whether the valve has internal leakage. Under different pressures, when the valve leaks, as the leakage rate increases, the confidence rate difference also increases. Under different pressures, it can still be judged whether the valve has internal leakage by the confidence rate deviation.

[0054] Preferably, Figure 3 As shown, the acoustic emission signal after amplification and the non-leaking acoustic emission signal are subjected to signal noise reduction processing and leakage signal feature extraction respectively, specifically including:

[0055] The collected acoustic emission signals are decomposed layer by layer using the wavelet packet decomposition method to obtain frequency domain signals of multiple frequency bands;

[0056] Determine the frequency band of the leakage acoustic emission signal according to the spectrum peak frequency band of the frequency domain signal;

[0057] The acoustic emission signal is reconstructed by combining the frequency band of the leakage acoustic emission signal through the threshold denoising method, and the noise in other frequency bands in the environment outside the frequency band of the acoustic emission signal is filtered out.

[0058] Preferably, the wavelet packet decomposition method uses the db3 wavelet function, selects 4 layers of decomposition, obtains frequency domain signals of 16 frequency bands, and decomposes them layer by layer according to the frequency through multiple iterations of the signal, so that the decomposition is more refined, which is conducive to the precise determination of the subsequent leakage signal frequency band.

[0059] Preferably, the db3 wavelet function is a symmetric wavelet function with biorthogonality, which can process complex leakage acoustic emission signals with large data volume, and the b3 wavelet function has tight support, which can accurately obtain the local characteristics of the signal.

[0060] Preferably, reconstructing the acoustic emission signal and filtering out noise in other frequency bands also includes: combining the frequency band of the leakage acoustic emission signal, retaining the wavelet packet node coefficients of the frequency band, reducing the wavelet packet node coefficients of noise signals in other frequency bands to 0, reconstructing the acoustic emission signal, and filtering out noise in other frequency bands in the environment outside the frequency band where the acoustic emission signal is located.

[0061] Preferably, the confidence value is determined according to the difference between the predicted data and the actual data, specifically including:

[0062] Calculate the difference between the predicted data and the real data, sort the differences from small to large, select the first 99% of the differences as the first credible data, and set the largest value in the first credible data as the confidence value.

[0063] Preferably, the normal difference and the leakage difference are obtained by calculation, specifically including:

[0064] The normal acoustic emission data and the leakage acoustic emission data are respectively input into the GRU model to obtain the normal prediction data and the leakage prediction data;

[0065] The difference between the normal prediction data and the normal real data is calculated to obtain the normal difference; the difference between the leakage prediction data and the leakage real data is calculated to obtain the leakage difference;

[0066] Preferably, the normal difference and the leakage difference are compared with the confidence value respectively to obtain the normal confidence rate and the leakage confidence rate, and further comprises:

[0067] The normal difference and the leakage difference are compared with the confidence value respectively. The data less than the confidence value is the credible data, and the data greater than the confidence value is the uncredible data, so as to obtain normal credible data, normal uncredible data, leakage credible data and leakage uncredible data.

[0068] A normal confidence rate is determined based on normal trusted data and normal untrusted data; a leakage confidence rate is determined based on leakage trusted data and leakage untrusted data.

[0069] like Figure 2 As shown, a valve leakage detection device includes: an acoustic emission sensor, a preamplifier and a signal acquisition processing module, the signal acquisition processing module includes: a signal preprocessing module and an internal leakage diagnosis module, the acoustic emission sensor is electrically connected to the preamplifier, the preamplifier is electrically connected to the signal preprocessing module, and the signal preprocessing module is electrically connected to the internal leakage diagnosis module;

[0070] The acoustic emission sensor is used to collect the acoustic emission signal of the pressure pipeline and send the acoustic emission signal to the preamplifier;

[0071] Preferably, the acoustic emission sensor is a piezoelectric crystal probe based on the acoustic emission detection principle. The acoustic emission signal generated by the leakage of the pressure pipeline is a generalized acoustic emission signal. The pipe wall itself does not release energy but only serves as a propagation medium. During the leakage process, due to the pressure difference between the inside and outside of the pipe at the leakage point, the fluid in the pipe forms a multiphase turbulent jet at the leakage point. The multiphase turbulent jet not only disrupts the normal flow of the fluid, but also interacts with the pipe and the surrounding medium to radiate energy outward, generating high-frequency stress waves on the pipe wall. The acoustic emission sensor is arranged on the outer valve wall of the valve to be tested. When the movement of the leaking particle in the valve is transmitted to the sensor contact surface, it drives the proton movement on the piezoelectric ceramic on the sensor contact surface, thereby producing a compression and stretching effect on the piezoelectric ceramic, which is then converted into a voltage signal, completing the transformation process from stress wave to electrical signal wave, and can detect weak signals.

[0072] The preamplifier is used to receive the acoustic emission signal sent by the acoustic emission sensor and amplify the signal, and send the amplified acoustic emission signal to the signal preprocessing module;

[0073] Preferably, the preamplifier uses an amplifier that is compatible with the acoustic emission sensor and has a gain of 40 dB. Its function is to amplify the weak signal collected by the acoustic emission sensor through an analog circuit so that the subsequent signal preprocessing module can collect and process the signal.

[0074] The signal preprocessing module is used to receive the amplified acoustic emission signal and the non-leaking acoustic emission signal, perform signal noise reduction processing and leakage signal feature extraction respectively, and obtain the leakage acoustic emission data with noise filtered out and the normal acoustic emission data with noise filtered out; send the leakage acoustic emission data and the normal acoustic emission data with noise filtered out to the internal leakage diagnosis module;

[0075] The internal leakage diagnosis module is a gated recurrent unit neural network GRU, which is used to receive normal acoustic emission data, obtain predicted data, and determine the confidence value according to the difference between the predicted data and the real data;

[0076] It is also used to calculate the normal difference and the leakage difference based on the leakage acoustic emission data and the normal acoustic emission data; compare the normal difference and the leakage difference with the confidence rate value respectively to obtain the normal confidence rate and the leakage confidence rate; calculate the difference between the normal confidence rate and the leakage confidence rate to obtain the confidence rate difference; if the confidence rate difference is less than the preset value, it is determined that no leakage has occurred, otherwise it is determined that a leakage has occurred.

[0077] Preferably, the gated recurrent unit neural network GRU uses time series to identify valve failures. It is very sensitive to long-term series data and can store long-term memory for correcting the information contained in short-term memory. It can also accurately identify leaks when the leakage is small. When a leak occurs, the small change data received by the GRU is different from the information stored in the long-term memory, generating a difference different from the non-leakage condition. Combined with the collected non-leakage data, it identifies and judges the small data changes that occur when the valve leaks, thereby achieving real-time detection of valve leaks.

[0078] Preferably, it also includes: a preamplifier power supply separation signal device electrically connected to the preamplifier and a power supply module electrically connected to the signal acquisition and processing module, the preamplifier power supply separation signal device is used to power the preamplifier, and the power supply module is used to power the signal acquisition and processing module.

[0079] Preferably, the power supply module supplies power to the signal preprocessing module and the internal leakage diagnosis module respectively.

[0080] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0081] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A valve leakage detection method, characterized in that: include: Acquiring an acoustic emission signal from a pressure pipe by means of an acoustic emission sensor, and amplifying the acoustic emission signal; The amplified acoustic emission signal and the non-leaked acoustic emission signal are subjected to signal noise reduction processing and leakage signal feature extraction respectively, and leakage acoustic emission data with noise filtered out and normal acoustic emission data with noise filtered out are obtained accordingly; Inputting the normal acoustic emission data into the GRU model to obtain predicted data, and determining a confidence value according to the difference between the predicted data and the real data; The normal acoustic emission data and the leakage acoustic emission data are respectively input into the GRU model, and a normal difference and a leakage difference are obtained by calculation; Compare the normal difference and the leakage difference with the confidence rate value respectively to obtain a normal confidence rate and a leakage confidence rate; The difference between the normal confidence rate and the leakage confidence rate is calculated to obtain a confidence rate difference. If the confidence rate difference is less than a preset value, it is determined that no leakage has occurred; otherwise, it is determined that a leakage has occurred.

2. A valve leakage detection method according to claim 1, characterized in that: The performing signal noise reduction processing and leakage signal feature extraction on the acoustic emission signal after amplification processing and the non-leaking acoustic emission signal respectively specifically includes: Decomposing the collected acoustic emission signal layer by layer by using wavelet packet decomposition method to obtain frequency domain signals of multiple frequency bands; Determining the frequency band of the leakage acoustic emission signal according to the spectrum peak frequency band of the frequency domain signal; The acoustic emission signal is reconstructed by combining the leakage acoustic emission signal frequency band through a threshold denoising method, and noise in other frequency bands in the environment outside the frequency band where the acoustic emission signal is located is filtered out.

3. A valve leakage detection method according to claim 2, characterized in that: The wavelet packet decomposition method adopts the db3 wavelet function, selects 4 layers of decomposition, and obtains frequency domain signals of 16 frequency bands.

4. A valve leakage detection method according to claim 3, characterized in that: The reconstructing the acoustic emission signal and filtering out noise in other frequency bands also includes: combining the frequency band of the leakage acoustic emission signal, retaining the wavelet packet node coefficients of the frequency band, reducing the wavelet packet node coefficients of noise signals in other frequency bands to 0, reconstructing the acoustic emission signal, and filtering out noise in other frequency bands in the environment outside the frequency band where the acoustic emission signal is located.

5. A valve leakage detection method according to claim 1, characterized in that: The confidence value is determined according to the difference between the predicted data and the actual data, specifically including: The difference between the predicted data and the real data is calculated, the difference is sorted from small to large, the first 99% of the difference is selected as the first credible data, and the largest value in the first credible data is set as the confidence value.

6. A valve leakage detection method according to claim 1, characterized in that: The normal difference and the leakage difference are obtained by calculation, specifically including: Inputting the normal acoustic emission data and the leakage acoustic emission data into the GRU model respectively to obtain normal prediction data and leakage prediction data; The difference between the normal prediction data and the normal real data is calculated to obtain the normal difference; the difference between the leakage prediction data and the leakage real data is calculated to obtain the leakage difference.

7. A valve leakage detection method according to claim 6, characterized in that: The step of comparing the normal difference and the leakage difference with the confidence rate value to obtain a normal confidence rate and a leakage confidence rate also includes: The normal difference and the leakage difference are respectively compared with the confidence value, the data less than the confidence value is the credible data, and the data greater than the confidence value is the uncredible data, to obtain normal credible data, normal uncredible data, leakage credible data and leakage uncredible data; A normal confidence rate is determined according to the normal trusted data and the normal untrusted data; and a leakage confidence rate is determined according to the leakage trusted data and the leakage untrusted data.

8. A valve leakage detection device, characterized in that: include: An acoustic emission sensor, a preamplifier and a signal acquisition and processing module, wherein the signal acquisition and processing module comprises: a signal preprocessing module and an internal leakage diagnosis module, wherein the acoustic emission sensor is electrically connected to the preamplifier, wherein the preamplifier is electrically connected to the signal preprocessing module, and wherein the signal preprocessing module is electrically connected to the internal leakage diagnosis module; The acoustic emission sensor is used to collect the acoustic emission signal of the pressure pipeline and send the acoustic emission signal to the preamplifier; The preamplifier is used to receive the acoustic emission signal sent by the acoustic emission sensor and amplify the signal, and send the amplified acoustic emission signal to the signal preprocessing module; The signal preprocessing module is used to receive the amplified acoustic emission signal and the non-leaking acoustic emission signal, perform signal noise reduction processing and leakage signal feature extraction respectively, and obtain leakage acoustic emission data with noise filtered out and normal acoustic emission data with noise filtered out; send the leakage acoustic emission data and the normal acoustic emission data with noise filtered out to the internal leakage diagnosis module; The internal leakage diagnosis module is a gated recurrent unit neural network GRU, which is used to receive the normal acoustic emission data, obtain predicted data, and determine the confidence value according to the difference between the predicted data and the real data; It is also used to calculate a normal difference and a leakage difference based on the leakage acoustic emission data and the normal acoustic emission data; compare the normal difference and the leakage difference with the confidence rate value respectively to obtain a normal confidence rate and a leakage confidence rate; calculate the difference between the normal confidence rate and the leakage confidence rate to obtain a confidence rate difference; if the confidence rate difference is less than a preset value, it is determined that no leakage has occurred, otherwise it is determined that a leakage has occurred.

9. A valve leakage detection device according to claim 8, characterized in that: Also includes: A preamplifier power supply separation signal device electrically connected to the preamplifier and a power supply module electrically connected to the signal acquisition and processing module, wherein the preamplifier power supply separation signal device is used to power the preamplifier, and the power supply module is used to power the signal acquisition and processing module.

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