Online positive and negative pressure micro-leakage detection system

Abstract

The invention relates to the technical field of micro-leakage detection, in particular to an online positive and negative pressure micro-leakage detection system. According to the technical scheme, the system comprises a foaming reagent module, a positive pressure and negative pressure detection equipment module, a data acquisition unit, an online operation module, a micro-leakage positioning module and a visual interaction module. According to the invention, the advantages of multiple modules are integrated, the foaming reagent has optimized components, is clean and convenient, is suitable for wide-temperature and high-calcium magnesium ion environments, and does not need surface pretreatment; the positive and negative pressure detection module realizes safe and flexible detection and accurate impurity separation through multi-stage adjustable pressurization, anti-explosion design, an oil-free vortex pump and a condenser; the data acquisition and online operation module records data in real time and ensures continuous and reliable detection through efficient switching of A / B sides; the micro-leakage positioning module utilizes a sound wave and infrared multi-source data fusion algorithm and an intelligent optimization algorithm to realize accurate positioning of leakage points, and the whole device has the advantages of high efficiency and accuracy in detection, safety and reliability, high environmental adaptability and convenience in operation.

Description

Technical Field

[0001] The present invention relates to the technical field of micro-leakage detection, and in particular to an online positive and negative pressure micro-leakage detection system. Background Art

[0002] Microleakage is a long-standing problem in sealed containers, piping systems, reaction equipment, and storage and transportation devices in industries such as power, petroleum, chemicals, defense, and nuclear energy. These leaks not only lead to energy loss and reduced equipment efficiency, but can also pose safety risks (such as leakage of flammable and explosive media and the spread of radioactive materials). Rapidly locating and repairing these leaks is crucial to ensuring production safety, especially in high-temperature, high-pressure, and corrosive environments.

[0003] The current mainstream micro-leakage detection technologies mainly include the following methods:

[0004] Ammonia mass spectrometry: This method uses a mass spectrometer to detect leaks by injecting a tracer gas (such as helium). It has high sensitivity, but requires downtime, and the equipment is expensive and complex to operate, making it difficult to adapt to online detection needs.

[0005] Ultrasonic detection method: relies on the ultrasonic signal generated by the leak to locate the leak point, but is sensitive to environmental noise and cannot detect low-speed or extremely small flow rate leaks (<0.1mL / min);

[0006] Infrared imaging method: Identifies leaks by heat distribution differences, but is susceptible to interference on wet, oily, or rusty surfaces and cannot distinguish leaks from background heat sources.

[0007] Foam method: Traditional foam reagents rely on manual coating and visual observation, and have problems such as poor adhesion (especially on vertical / reverse surfaces), low environmental adaptability (such as seawater ions causing foam to break), and the need to stop the machine for pre-cleaning. In addition, a complex recovery process is required after testing, which is inefficient.

[0008] In addition, existing technologies generally have the following limitations:

[0009] Insufficient online detection capabilities: Most methods require equipment shutdown or local isolation, affecting continuous production;

[0010] Poor adaptability to complex working conditions: lack of effective detection methods for wet, corrosive, high-temperature or irregular surfaces;

[0011] Low positioning accuracy: Relying on a single sensor (such as sound waves or infrared), the false alarm rate of leak points is high;

[0012] Environmental and safety defects: Some reagents contain toxic or corrosive ingredients, and the cost of residue disposal is high.

[0013] In view of the above problems, it is urgent to develop a micro-leakage detection system that can take into account online operation, high-precision positioning, wide environmental adaptability, and environmental safety. This application proposes an online positive and negative pressure micro-leakage detection system. Summary of the Invention

[0014] The purpose of the present invention is to propose an online positive and negative pressure micro-leakage detection system to address the problem that there is no micro-leakage detection system in the background technology that can take into account online operation, high-precision positioning, wide environmental adaptability and environmental safety.

[0015] The technical solution of the present invention is an online positive and negative pressure micro-leakage detection system, comprising:

[0016] The foaming reagent module includes a non-toxic, non-corrosive, and non-radioactive foaming reagent, wherein the foaming reagent is compounded from an acrylic polymer, a surfactant, and a thickener;

[0017] Positive and negative pressure detection equipment modules, including:

[0018] Positive pressure detection device, including pressure regulating valve, pressure sensor and gas booster unit, suitable for working conditions with system internal pressure of 0.1-5.0MPa;

[0019] The negative pressure detection device includes a vacuum pump, a negative pressure sensor and a sealed cavity, and is suitable for working conditions where the internal pressure of the system is -0.1MPa to -0.01MPa;

[0020] Data acquisition unit, real-time recording of pressure fluctuations and foam morphology change data;

[0021] The online operation module integrates a programmable logic controller (PLC) and supports testing while the equipment is in operation. It is suitable for pipes with a diameter range of DN20-DN1000, special-shaped interfaces, and curved equipment. It also realizes single-side detection and operation synchronization through the A / B side switching unit, with a switching response time of ≤30 seconds.

[0022] The micro-leakage positioning module includes a top-door positioning detector and an infrared imaging unit. The top-door positioning detector has a built-in high-frequency acoustic wave sensor (frequency range 20-50kHz) to capture the acoustic wave signal generated by micro-leakage. Combined with the infrared imaging unit, it performs thermal distribution analysis on the leak area, with a positioning accuracy of ≤2mm.

[0023] The visual interaction module, including a touch screen and a remote terminal, displays the pressure curve, leak point coordinates, leak estimation value and three-dimensional thermal imaging map in real time, and generates a test report.

[0024] Optionally, the foam layer of the foaming agent has a thickness of 1-5 mm, a bursting time of 3-10 seconds, and the residue after bursting is soluble in water and does not require mechanical removal.

[0025] Optionally, the gas boosting unit of the positive pressure detection device adopts multi-stage compression technology, the boosting rate can be adjusted in the range of 0.1-2.0MPa / min, and is equipped with an explosion-proof casing (protection grade IP67), which is suitable for flammable and explosive environments.

[0026] Optionally, the vacuum pump of the negative pressure detection device adopts an oil-free vortex structure with a pumping rate of 10-50L / min, and is equipped with a condenser to separate liquid impurities in the gas to ensure detection accuracy.

[0027] Optionally, the A / B side switching unit of the online operation module includes a two-way solenoid valve group and a redundant sealing component. The sealing component is made of fluororubber, has a temperature resistance range of -40°C to 150°C, and a leakage rate of ≤1×10 -6 Pa·m 3 / s.

[0028] Optionally, the high-frequency acoustic wave sensor of the micro-leakage locating module is associated with the infrared imaging unit through a multi-source data fusion algorithm, specifically:

[0029] The acoustic signal is subjected to Fourier transform to extract the characteristic frequency;

[0030] Thermal distribution data uses the grayscale gradient method to identify abnormal temperature areas;

[0031] The two data are combined to generate a leakage point probability distribution map, and the final coordinates are determined through weighted optimization.

[0032] Optionally, the multi-source data fusion algorithm of the micro-leakage positioning module specifically includes the following steps:

[0033] Acoustic signal processing:

[0034] The original signal collected by the high-frequency acoustic wave sensor is subjected to noise suppression through a bandpass filter;

[0035] Perform fast Fourier transform (FFT) on the filtered signal to extract the peak frequency and harmonic components, and calculate the sound intensity level;

[0036] Generate a sound source intensity distribution heat map based on the mapping relationship between sound intensity level and leak point size;

[0037] Infrared thermal imaging processing:

[0038] The infrared imaging unit collects thermal distribution data of the target area at a rate of 30 frames per second;

[0039] Grayscale gradient method is used to enhance the edge of thermal images and identify temperature abnormality areas (ΔT ≥ 0.5°C);

[0040] Morphological filtering is used to eliminate environmental thermal noise interference and generate a temperature anomaly probability map;

[0041] Data fusion and optimization:

[0042] The sound source intensity distribution heat map is spatially registered with the temperature anomaly probability map, and the Kalman filter algorithm is used for time series synchronization.

[0043] The registered data were assigned weight coefficients (acoustic weight 0.6, thermal imaging weight 0.4), and the leak point coordinates were optimized using the least squares method.

[0044] Output the three-dimensional coordinates (X, Y, Z) and confidence level (≥95%), and mark the leak location in the visual interaction module.

[0045] Optionally, the preparation method of the foaming reagent module includes:

[0046] Acrylate polymer and deionized water were mixed in a ratio of 1:3, with a stirring rate of 300 rpm and a temperature of 40°C;

[0047] Add surfactant (sodium lauryl sulfate) and thickener (sodium carboxymethyl cellulose) and stir until the viscosity stabilizes;

[0048] The mixture was treated with a high-pressure homogenizer (pressure 50 MPa) to form a homogeneous dispersion system.

[0049] Optionally, the preparation method of the foaming reagent module specifically includes the following steps:

[0050] Raw material mixing stage: Add acrylic polymer (molecular weight 50,000-80,000Da) and deionized water in a mass ratio of 1:3 to the reactor, control the stirring rate to 300±10rpm, raise the temperature to 40±2°C, and continue stirring for 30 minutes until completely dissolved; then add the surfactant sodium dodecyl sulfate (SDS) dropwise at an amount of 1.5% of the total mass, and simultaneously add sodium carboxymethyl cellulose (CMC-Na) as a thickener at an amount of 0.8% of the total mass, and continue stirring for 20 minutes until the system is uniform;

[0051] Homogenization stage: transfer the mixed liquid to a high-pressure homogenizer, set the homogenization pressure to 50±5MPa, and cycle the process three times with an interval of 5 minutes each time to ensure that the particle size distribution D90 ≤ 10μm; after homogenization, filter the reagent through a 0.22μm microporous filter membrane to remove undispersed particles.

[0052] Optionally, the foaming agent has a viscosity range of 500-1500 mPa·s, a pH value of 6.5-8.0, an applicable temperature range of -10°C to 85°C, and can form a uniform foam layer on a wet surface containing seawater calcium and magnesium ion concentrations ≤5000 mg / L without the need for pre-drying or polishing.

[0053] Compared with the prior art, this application has at least one of the following beneficial technical effects:

[0054] The foaming reagent module is a compound of acrylic polymers, surfactants, and thickeners that form high-performance foam. Through specialized raw material mixing and homogenization, the reagent ensures stable quality and uniform particle size distribution, eliminating the need for pre-drying or polishing.

[0055] The gas booster unit utilizes multi-stage compression technology, with an adjustable boost rate range of 0.1-2.0 MPa / min, flexibly adapting to diverse testing requirements. It features an explosion-proof housing for enhanced safety. The vacuum pump utilizes an oil-free vortex structure, delivering a pumping rate of 10-50 L / min for stable operation. A condenser separates liquid impurities from the gas, ensuring accurate testing.

[0056] The integrated programmable logic controller achieves single-side detection and operation synchronization through the A / B side switching unit, improving detection efficiency and system reliability. The bidirectional solenoid valve group and redundant sealing components of the A / B side switching unit are made of fluororubber, with a temperature resistance range of -40°C to 150°C and a leakage rate of ≤1×10-6Pa·m. 3 / s, ensuring reliable switching and good sealing performance.

[0057] The top-door positioning detector has a built-in high-frequency acoustic wave sensor to capture micro-leakage acoustic wave signals, combined with the thermal distribution analysis of the infrared imaging unit, and through a multi-source data fusion algorithm (including acoustic wave signal processing, infrared thermal imaging processing, and data fusion and optimization steps), it generates a leak point probability distribution map and determines the final coordinates, thereby improving the accuracy and reliability of leak point positioning.

[0058] The present invention integrates the advantages of multiple modules. The foaming reagent composition is optimized, clean and convenient, and it can adapt to a wide temperature and high calcium and magnesium ion environment without the need for surface pretreatment. The positive and negative pressure detection module realizes safe and flexible detection and precise impurity separation through multi-stage adjustable pressurization, explosion-proof design, and oil-free vortex pump and condenser. The data acquisition and online operation module records data in real time and ensures continuous and reliable detection through efficient switching of the A / B side. The micro-leak positioning module uses the sound wave and infrared multi-source data fusion algorithm and the intelligent optimization algorithm to realize precise positioning of the leakage point. The overall advantages are efficient and accurate detection, safety and reliability, strong environmental adaptability, and convenient operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a principle block diagram of an online positive and negative pressure micro-leakage detection system. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is further described below with reference to the accompanying drawings and specific embodiments.

[0061] Example 1

[0062] like Figure 1 As shown, the present invention proposes an online positive and negative pressure micro-leakage detection system, comprising:

[0063] The foaming agent module includes a non-toxic, non-corrosive, and non-radioactive foaming agent compounded from an acrylic polymer, a surfactant, and a thickener. The foam layer thickness of the foaming agent is 1-5 mm, the bursting time is 3-10 seconds, and the residue after rupture is soluble in water, eliminating the need for mechanical removal. The foaming agent has a viscosity range of 500-1500 mPa·s, a pH value of 6.5-8.0, and an applicable temperature range of -10°C to 85°C. It can form a uniform foam layer on wet surfaces containing seawater calcium and magnesium ion concentrations ≤5000 mg / L, without the need for pre-drying or polishing.

[0064] The preparation method of the foaming reagent module comprises:

[0065] The acrylic polymer and deionized water were mixed in a ratio of 1:3, with a stirring rate of 300 rpm and a temperature of 40°C; a surfactant, sodium lauryl sulfate, and a thickener, sodium carboxymethyl cellulose, were added and stirred until the viscosity was stable; and the mixture was processed by a high-pressure homogenizer at a pressure of 50 MPa to form a homogeneous dispersion system.

[0066] The preparation method of the foaming reagent module specifically comprises the following steps:

[0067] Raw material mixing stage: Add acrylic polymer (molecular weight 50,000-80,000Da) and deionized water in a mass ratio of 1:3 to the reactor, control the stirring rate to 300±10rpm, raise the temperature to 40±2°C, and continue stirring for 30 minutes until completely dissolved; then add the surfactant sodium dodecyl sulfate (SDS) dropwise at an amount of 1.5% of the total mass, and simultaneously add sodium carboxymethyl cellulose (CMC-Na) as a thickener at an amount of 0.8% of the total mass, and continue stirring for 20 minutes until the system is uniform;

[0068] Homogenization stage: transfer the mixed liquid to a high-pressure homogenizer, set the homogenization pressure to 50±5MPa, and cycle the process three times with an interval of 5 minutes each time to ensure that the particle size distribution D90 ≤ 10μm; after homogenization, filter the reagent through a 0.22μm microporous filter membrane to remove undispersed particles.

[0069] Positive and negative pressure detection equipment modules, including:

[0070] The positive pressure detection device includes a pressure regulating valve, a pressure sensor and a gas boosting unit, which is suitable for working conditions where the internal system pressure is 0.1-5.0MPa; the gas boosting unit of the positive pressure detection device adopts multi-stage compression technology, the boosting rate can be adjusted in the range of 0.1-2.0MPa / min, and is equipped with an explosion-proof casing (protection level IP67), which is suitable for flammable and explosive environments.

[0071] The negative pressure detection device includes a vacuum pump, a negative pressure sensor and a sealed cavity, and is suitable for working conditions where the internal pressure of the system is -0.1MPa to -0.01MPa; the vacuum pump of the negative pressure detection device adopts an oil-free vortex structure, with a pumping rate of 10-50L / min, and is equipped with a condenser to separate liquid impurities in the gas to ensure detection accuracy.

[0072] The data acquisition unit records the pressure fluctuation and foam morphology change data in real time. The specific implementation method includes the following steps and components:

[0073] 1. Hardware composition and sensor configuration

[0074] Pressure sensor array: Deploy high-precision piezoresistive pressure sensors (accuracy ±0.1% FS) at key nodes of the system to be tested (such as pipeline interfaces, valves, and container walls), with a sampling frequency of ≥1kHz and covering a pressure range of -0.1MPa to 5.0MPa;

[0075] The sensor signals are connected to a multi-channel data acquisition card (such as NI-9237) through a shielded cable, which supports synchronous acquisition and anti-interference filtering.

[0076] Optical imaging module: uses an industrial-grade high-speed camera (frame rate ≥ 60fps, resolution 1920×1080) equipped with a ring LED fill light (color temperature 5000K) to capture the real-time morphological changes of the foam layer;

[0077] The camera is mounted on an adjustable bracket that supports pitch angle and focal length adjustment to ensure full coverage of the detection area.

[0078] Central processing unit: Based on an embedded system (such as the ARM Cortex-A72 processor), it integrates an FPGA module to achieve parallel data processing; it has a built-in storage module (SSD 256GB) and a wireless transmission unit (supporting 4G / 5G and Wi-Fi 6) for local data caching and remote upload.

[0079] 2. Data collection and synchronization mechanism

[0080] Pressure data collection:

[0081] The pressure sensor outputs analog signals in real time, which are converted into digital signals by the data acquisition card and transmitted to the central processing unit through the SPI interface;

[0082] Timestamp synchronization technology is used to ensure the time alignment of multi-sensor data (deviation ≤ 1ms).

[0083] Foam morphology data collection:

[0084] The camera continuously captures images of the foam layer and transmits them to the processing unit via an image acquisition card (e.g., PCIe-1433);

[0085] The trigger signal is synchronized with the pressure sensor to realize the correlation analysis between pressure fluctuation and foam morphology change.

[0086] 3. Data processing and feature extraction

[0087] Pressure fluctuation analysis:

[0088] Perform sliding window mean filtering (window width 100ms) on the pressure signal to eliminate instantaneous noise;

[0089] The frequency domain features of pressure fluctuations are extracted by short-time Fourier transform (STFT) to identify periodic leakage signals.

[0090] Foam morphology analysis:

[0091] Image preprocessing: grayscale, histogram equalization and Gaussian filtering are performed on the collected images to enhance the contrast of the foam edge;

[0092] Morphological detection: Use the contour extraction algorithm in the OpenCV library to identify the foam rupture area (area ≥ 5mm 2 ) and morphological distortion (such as bubble expansion and collapse);

[0093] Dynamic tracking: Based on the optical flow method (Lucas-Kanade algorithm), the foam change trajectory is tracked and the rupture rate and diffusion range are calculated.

[0094] 4. Data fusion and anomaly determination

[0095] Spatiotemporal correlation model:

[0096] The frequency domain characteristics of pressure fluctuations are matched with the location and rate of foam rupture in time and space to construct a leakage probability matrix;

[0097] If the overlap between the pressure drop area and the foam rupture area is ≥80%, it is determined to be an effective leak point.

[0098] Threshold warning mechanism: preset pressure fluctuation thresholds (such as ±5% of rated pressure) and foam burst rate thresholds (such as ≥3 places / second). When the thresholds are exceeded, sound and light alarms are triggered, and high-risk areas are marked through a visual interactive module.

[0099] 5. Data storage and output

[0100] Local storage: Raw data (pressure signals, image sequences) are named with timestamps and stored in HDF5 format according to detection task partitions, supporting fast retrieval and playback;

[0101] Remote transmission: key characteristic data (leak point coordinates, leakage volume, pressure curve) are uploaded to the cloud platform via the MQTT protocol to generate a structured test report (PDF / Excel format);

[0102] Real-time interaction: Dynamically display pressure waveforms, foam morphology heat maps, and leak point coordinates in the visual interaction module, supporting touch zoom and historical data comparison.

[0103] 6. Anti-interference and reliability design

[0104] Electromagnetic shielding: The pressure sensor and data cable use a metal shielding shell with a grounding resistance of ≤1Ω to suppress electromagnetic interference in industrial environments;

[0105] Environmental adaptability: The optical imaging module is equipped with a waterproof and anti-fog coating (IP65 rating), which allows it to operate stably in humidity ≤ 95% or in oily environments.

[0106] Redundant backup: The data acquisition unit has a built-in dual power supply module (AC / DC dual input), and critical data is backed up to redundant storage chips in real time to prevent data loss due to sudden power outages.

[0107] The data acquisition unit utilizes high-precision sensors, simultaneous multimodal data acquisition, and intelligent analysis algorithms to accurately correlate pressure fluctuations with changes in foam morphology, significantly improving the real-time and reliability of micro-leak detection. Furthermore, the hardware's anti-interference capabilities and data redundancy ensure stable system operation in complex industrial environments.

[0108] The online operation module integrates a programmable logic controller (PLC) and supports detection without stopping the equipment. It is suitable for pipes with a diameter range of DN20-DN1000, special-shaped interfaces, and curved equipment. It realizes single-side detection and operation synchronization through the A / B side switching unit, with a switching response time of ≤30 seconds. The A / B side switching unit of the online operation module includes a two-way solenoid valve group and redundant sealing components. The sealing components are made of fluororubber, with a temperature resistance range of -40°C to 150°C and a leakage rate of ≤1×10 -6 Pa·m 3 / s.

[0109] The micro-leakage positioning module includes a top-door positioning detector and an infrared imaging unit. The top-door positioning detector has a built-in high-frequency acoustic wave sensor (frequency range 20-50kHz) for capturing the acoustic wave signals generated by micro-leaks, and combined with the infrared imaging unit to perform thermal distribution analysis on the leakage area, with a positioning accuracy of ≤2mm; the high-frequency acoustic wave sensor and the infrared imaging unit of the micro-leakage positioning module are linked through a multi-source data fusion algorithm, specifically: the acoustic wave signal is Fourier transformed to extract the characteristic frequency; the thermal distribution data is used to identify abnormal temperature areas through the grayscale gradient method; the two data are combined to generate a leakage point probability distribution map, and the final coordinates are determined through weighted optimization.

[0110] Furthermore, the multi-source data fusion algorithm of the micro-leakage positioning module specifically includes the following steps:

[0111] Acoustic signal processing:

[0112] The original signal collected by the high-frequency acoustic wave sensor is subjected to noise suppression through a bandpass filter;

[0113] Perform fast Fourier transform (FFT) on the filtered signal to extract the peak frequency and harmonic components, and calculate the sound intensity level;

[0114] Generate a sound source intensity distribution heat map based on the mapping relationship between sound intensity level and leak point size;

[0115] Infrared thermal imaging processing:

[0116] The infrared imaging unit collects thermal distribution data of the target area at a rate of 30 frames per second;

[0117] Grayscale gradient method is used to enhance the edge of thermal images and identify temperature abnormality areas (ΔT ≥ 0.5°C);

[0118] Morphological filtering is used to eliminate environmental thermal noise interference and generate a temperature anomaly probability map;

[0119] Data fusion and optimization:

[0120] The sound source intensity distribution heat map is spatially registered with the temperature anomaly probability map, and the Kalman filter algorithm is used for time series synchronization.

[0121] The registered data were assigned weight coefficients (acoustic weight 0.6, thermal imaging weight 0.4), and the leak point coordinates were optimized using the least squares method.

[0122] Output the three-dimensional coordinates (X, Y, Z) and confidence level (≥95%), and mark the leak location in the visual interaction module.

[0123] The visual interaction module, including a touch screen and a remote terminal, displays the pressure curve, leak point coordinates, leak estimation value and 3D thermal imaging map in real time, and generates a test report. The specific implementation is as follows:

[0124] 1. Hardware composition

[0125] Main control touch screen:

[0126] Adopt industrial grade 10.1-inch capacitive touch screen (resolution 1920×1200, brightness ≥500cd / m 2 ), protection grade IP65, supports operation with gloves;

[0127] Built-in quad-core processor (such as Rockchip RK3399), equipped with 4GB RAM and 64GB storage space, running a customized Linux system;

[0128] Interface configuration: HDMI output (for extended display), USB 3.0 (for connecting peripherals), RS-485 (for accessing sensor networks).

[0129] Remote Terminal:

[0130] Supports access from PC (Windows / Linux) and mobile (iOS / Android), and synchronizes data with the main control screen through an encrypted VPN tunnel;

[0131] The cloud server is deployed on the Alibaba Cloud ECS instance and uses a MySQL database to store historical detection records.

[0132] Auxiliary equipment:

[0133] Thermal printer: embedded design, real-time printing of test reports (including leak point coordinates, pressure curve thumbnail and operator signature column);

[0134] Sound and light alarm: integrated LED warning light (red / yellow / green) and buzzer, alarm volume ≥85dB.

[0135] Software function design

[0136] Real-time data display interface:

[0137] Pressure curve:

[0138] Dynamic line chart displays pressure sensor data (time axis accuracy 0.1 second), supports horizontal scrolling and zooming;

[0139] Different colors distinguish positive pressure (red), negative pressure (blue) and threshold line (dashed line).

[0140] 3D thermal imaging atlas:

[0141] Based on the infrared imaging unit data, a three-dimensional grid model of the detection area is constructed (accuracy 1mm 3 );

[0142] The temperature gradient is rendered in pseudo-color (blue for low temperature, red for high temperature), and abnormal temperature areas are automatically highlighted and flash.

[0143] Leakage point coordinates panel:

[0144] The list shows the leak number, coordinates (X, Y, Z), confidence level (%) and recommended repair priority (high / medium / low);

[0145] Click an entry to jump to the thermal image of the corresponding location.

[0146] Interactive control function:

[0147] Mode switch: select "Auto Detection", "Manual Calibration" or "History Playback" mode through the sidebar button;

[0148] Parameter setting: input parameters such as detection pressure range, foam coating thickness, alarm threshold, etc. and save them as preset schemes;

[0149] Gesture operation: pinch to zoom the thermal image, long press and drag to adjust the viewing angle, and swipe with three fingers to switch data views.

[0150] 3. Data processing and communication process

[0151] Data input: Pressure sensor data is transmitted to the main control screen via the RS-485 bus with a frame rate of 100Hz; infrared thermal imaging data is transmitted via Gigabit Ethernet with a delay of ≤50ms; foam morphology images are imported in real time via the USB 3.0 interface, with the resolution adapted to the screen ratio.

[0152] Data fusion and rendering: OpenGL acceleration: Utilize GPU to render 3D thermal imaging data in real time, with a frame rate of ≥30fps;

[0153] Data alignment: Synchronize pressure, temperature, and image data through timestamps to ensure temporal and spatial consistency;

[0154] Smart Marking: When a leak is detected, a marker sphere (2mm diameter) is automatically inserted into the 3D model and a log entry is generated.

[0155] Alarm and report generation: When the leak confidence level is ≥90% or the pressure exceeds the threshold, a full-screen red warning pop-up window and buzzer alarm are triggered; after the test is completed, a PDF report (including test time, leak distribution map, operator ID and system self-test status) is automatically generated.

[0156] Through highly integrated hardware design, intelligent software functions and user-friendly interactive logic, operators can intuitively grasp the detection status, quickly locate leaks, and efficiently complete data recording and report generation, significantly improving the automation level and user experience of industrial micro-leak detection.

[0157] The working principle of this system is as follows:

[0158] 1. Reagent Application and Foam Layer Formation: A specially formulated foaming reagent is applied to the surface to be tested (such as a pipe joint, container wall, or sealed box). This reagent, formulated from an acrylic polymer, surfactant, and thickener, exhibits strong adhesion and forms a uniform foam layer (1-5mm thick) on wet, oily, rusted, or irregularly shaped surfaces. After the reagent is applied, no pre-drying or polishing is required, allowing direct testing.

[0159] 2. Positive / negative pressure application

[0160] Positive pressure testing: Gas (such as air or inert gas) is injected into the system through a gas booster unit to raise the internal pressure to a set value (0.1-5.0 MPa). If there is a micro-leak, gas will escape from the leak point, causing local foam rupture or morphological distortion.

[0161] Negative pressure test: Use a vacuum pump to pump the system to a negative pressure state (-0.1MPa to -0.01MPa). If there is a leak, external gas will be sucked into the system, triggering the foam layer to collapse or bubbles to form.

[0162] Positive pressure and negative pressure modes can be used individually or alternately according to detection requirements to cover different leakage scenarios.

[0163] 3. Synchronous collection of multi-source data

[0164] Pressure data: High-precision pressure sensors monitor pressure fluctuations in the system in real time (sampling frequency ≥ 1kHz) and record pressure drops or abnormal fluctuation signals.

[0165] Foam morphology data: Industrial-grade high-speed cameras capture the dynamic changes of the foam layer (frame rate ≥ 60fps), and use image processing algorithms to identify the rupture area (area ≥ 5mm 2 ) and diffusion trajectories.

[0166] Acoustic and thermal imaging data:

[0167] High-frequency acoustic wave sensors (20-50kHz) capture the ultrasonic signals generated by the leak point and extract the characteristic frequencies through filtering and Fourier transform;

[0168] The infrared imaging unit scans the detection area, generates a thermal distribution map, and identifies abnormal temperature areas (ΔT ≥ 0.5°C).

[0169] 4. Data fusion and leak location

[0170] Through a multi-source data fusion algorithm, pressure fluctuations, foam burst locations, acoustic signals, and thermal imaging data are aligned in time and space:

[0171] The acoustic signal and thermal imaging data are weighted and optimized (acoustic weight 0.6, thermal imaging weight 0.4) to generate a leak probability distribution map;

[0172] The final leak point coordinates (accuracy ≤ 2 mm) are determined based on the matching degree between the pressure drop area and the foam rupture area (overlap ≥ 80%).

[0173] 5. Visual interaction and result output

[0174] The touch screen and remote terminal display the following content in real time:

[0175] Dynamic pressure curve: Use a line graph to show the positive / negative pressure change trend and mark abnormal fluctuation points;

[0176] 3D thermal imaging: False color rendering of temperature gradients and highlighting of leak locations;

[0177] Leak point coordinate list: includes confidence level, repair priority, and three-dimensional coordinates (X, Y, Z).

[0178] After the test is completed, a structured report (PDF / Excel) is automatically generated and uploaded to the cloud simultaneously, and a thermal printer is supported to output a paper version.

[0179] Online switching and continuous operation

[0180] Through the A / B side switching unit (two-way solenoid valve group), the area to be tested is isolated during the detection process, while the other side maintains normal operation to ensure that the equipment does not stop.

[0181] Switching response time ≤ 30 seconds, sealing component leakage rate ≤ 1×10 -6 Pa·m 3 / s, ensuring the safety of the detection process.

[0182] The present invention stimulates physical reactions at micro-leaks through changes in positive and negative pressure, and intuitively indicates the leak point in combination with the morphological changes of the foam layer; overcomes the limitations of a single technology through the complementarity of acoustic, thermal imaging, pressure and visual data; improves the accuracy and reliability of leak point positioning based on Kalman filtering and the least squares method; and achieves unmanned or low-manpower intervention detection from reagent coating, pressure application, data collection to report generation.

[0183] The above specific embodiments are merely several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant inspirations of the above embodiments, those skilled in the art may make various alternative improvements and combinations to the above specific embodiments.

Claims

1. An online positive and negative pressure micro-leakage detection system, characterized in that: include: The foaming agent module includes a foaming agent, wherein the foaming agent is compounded by an acrylic polymer, a surfactant and a thickener; Positive and negative pressure detection equipment modules, including: Positive pressure detection device, including a pressure regulating valve, a pressure sensor and a gas pressurizing unit; A negative pressure detection device, comprising a vacuum pump, a negative pressure sensor and a sealed cavity; Data acquisition unit, real-time recording of pressure fluctuations and foam morphology change data; Online operation module, integrated with programmable logic controller, and realizes single-side detection and operation synchronization through A / B side switching unit; The micro-leakage location module includes a top-door location detector and an infrared imaging unit. The top-door location detector has a built-in high-frequency acoustic wave sensor for capturing the acoustic wave signal generated by micro-leakage, and combines with the infrared imaging unit to perform thermal distribution analysis on the leakage area. The visual interaction module, including a touch screen and a remote terminal, displays the pressure curve, leak point coordinates, leak estimation value and three-dimensional thermal imaging map in real time, and generates a test report.

2. The online positive and negative pressure micro-leakage detection system according to claim 1, characterized in that: The foam layer thickness of the foaming agent is 1-5 mm, the bursting time is 3-10 seconds, and the residue after the bursting is soluble in water and does not need to be mechanically removed.

3. The online positive and negative pressure micro-leakage detection system according to claim 1, characterized in that: The gas boosting unit of the positive pressure detection device adopts multi-stage compression technology, the boosting rate can be adjusted in the range of 0.1-2.0MPa / min, and is equipped with an explosion-proof shell.

4. The online positive and negative pressure micro-leakage detection system according to claim 1, characterized in that: The vacuum pump of the negative pressure detection device adopts an oil-free vortex structure, has a pumping rate of 10-50L / min, and is equipped with a condenser to separate liquid impurities in the gas.

5. The online positive and negative pressure micro-leakage detection system according to claim 1, characterized in that: The A / B side switching unit of the online operation module includes a two-way solenoid valve group and a redundant sealing component. The sealing component is made of fluororubber, with a temperature tolerance range of -40°C to 150°C and a leakage rate of ≤1×10 -6 Pa·m 3 / s.

6. The online positive and negative pressure micro-leakage detection system according to claim 1, characterized in that: The high-frequency acoustic wave sensor of the micro-leakage positioning module is associated with the infrared imaging unit through a multi-source data fusion algorithm, specifically: The acoustic signal is subjected to Fourier transform to extract the characteristic frequency; Thermal distribution data uses the grayscale gradient method to identify abnormal temperature areas; The two data are combined to generate a leakage point probability distribution map, and the final coordinates are determined through weighted optimization.

7. The online positive and negative pressure micro-leakage detection system according to claim 6, characterized in that: The multi-source data fusion algorithm of the micro-leakage positioning module specifically includes the following steps: Acoustic signal processing: The original signal collected by the high-frequency acoustic wave sensor is subjected to noise suppression through a bandpass filter; Perform fast Fourier transform on the filtered signal to extract the peak frequency and harmonic components, and calculate the sound intensity level; Generate a sound source intensity distribution heat map based on the mapping relationship between sound intensity level and leak point size; Infrared thermal imaging processing: The infrared imaging unit collects thermal distribution data of the target area at a rate of 30 frames per second; Grayscale gradient method is used to enhance the edge of thermal images and identify abnormal temperature areas; Morphological filtering is used to eliminate environmental thermal noise interference and generate a temperature anomaly probability map; Data fusion and optimization: The sound source intensity distribution heat map is spatially registered with the temperature anomaly probability map, and the Kalman filter algorithm is used for time series synchronization. Assign weight coefficients to the registered data and optimize the coordinates of the leakage points using the least squares method; Output the three-dimensional coordinates (X, Y, Z) and confidence level, and mark the leak location in the visual interaction module.

8. The online positive and negative pressure micro-leakage detection system according to claim 1, characterized in that: The preparation method of the foaming reagent module comprises: Acrylate polymer and deionized water were mixed in a ratio of 1:3, with a stirring rate of 300 rpm and a temperature of 40°C; Add the surfactant sodium lauryl sulfate and the thickener sodium carboxymethyl cellulose and stir until the viscosity stabilizes; The mixture was processed by a high-pressure homogenizer at a pressure of 50 MPa to form a homogeneous dispersion system.

9. The online positive and negative pressure micro-leakage detection system according to claim 8, characterized in that: The preparation method of the foaming reagent module specifically comprises the following steps: Raw material mixing stage: Add acrylic polymer and deionized water in a mass ratio of 1:3 to the reactor, control the stirring rate to 300±10rpm, raise the temperature to 40±2℃, and continue stirring for 30 minutes until completely dissolved; add the surfactant sodium lauryl sulfate dropwise at an amount of 1.5% of the total mass, and simultaneously add sodium carboxymethyl cellulose as a thickener at an amount of 0.8% of the total mass, and continue stirring for 20 minutes until the system is uniform; Homogenization stage: transfer the mixed liquid to a high-pressure homogenizer, set the homogenization pressure to 50±5MPa, and cycle the process three times with an interval of 5 minutes each time to ensure that the particle size distribution D90 ≤ 10μm; after homogenization, filter the reagent through a 0.22μm microporous filter membrane to remove undispersed particles.

10. The online positive and negative pressure micro-leakage detection system according to claim 1, characterized in that: The foaming agent has a viscosity range of 500-1500 mPa·s, a pH value of 6.5-8.0, and an applicable temperature range of -10°C to 85°C. It can form a uniform foam layer on a wet surface containing seawater calcium and magnesium ion concentrations of ≤5000 mg / L without the need for pre-drying or polishing.

Images