Pipeline flange connection leakage detection method and system

Abstract

The invention provides a pipeline flange connection leakage detection method and system, and the method comprises the steps: obtaining a strain signal of a flange bolt through a multi-channel strain sensing device, and converting the strain signal to obtain the axial tension data of the bolt; the distributed gas concentration detection device detects leakage gas concentration data on the outer side of the flange sealing surface; the environment compensation device obtains interference parameter data of the environment where the flange is located; the central data processing device corrects the axial tension data and the leakage gas concentration data and performs multi-source information fusion processing on the corrected axial tension data and the corrected leakage gas concentration data to obtain a sealing state comprehensive score; and the remote monitoring terminal receives the sealing state comprehensive score, the corrected axial tension data and the corrected leakage gas concentration data, judges the sealing grade of the flange based on a preset sealing grade standard, and outputs corresponding state prompt and early warning information according to the sealing grade, so that the reliability and the real-time performance of leakage detection can be improved.

Description

Technical Field

[0001] This invention relates to the field of industrial equipment sealing condition monitoring technology, and in particular to a method and system for detecting leaks in pipeline flange connections. Background Technology

[0002] Flange joint systems are widely used in petrochemical, energy, and pharmaceutical industries due to their simple structure, convenient assembly, and repeated disassembly. However, industrial equipment leakage accidents occur frequently, with approximately 30%-40% of leakage accidents originating from flange connection failures. In nuclear power plant critical equipment sealing failures, flange interface leakage is also one of the main causes, the core mechanical reason being the uneven distribution of bolt preload.

[0003] Current engineering practices primarily employ torque control for bolt pre-tightening, supplemented by local strain monitoring methods such as smart bolts. However, this approach suffers from significant technical drawbacks: First, sequential torque loading can trigger a redistribution of prestress in adjacent fasteners, creating a "seesaw effect." Second, the relationship between torque and axial pre-tightening force is nonlinear, and due to the discreteness of the thread friction coefficient, even with consistent torque, a pre-tightening force deviation of approximately ±30% can still occur. Third, existing monitoring methods cannot achieve synchronous dynamic acquisition of strain across multiple bolts, making real-time assessment of the sealing status difficult. Furthermore, complex disturbances such as vibration, temperature, and pressure in the flange's service environment further affect the accuracy of detection and analysis results. These issues make it difficult to effectively monitor the sealing status of flange connections, failing to meet industrial safety requirements and severely restricting the safe and stable operation of industrial plants. Summary of the Invention

[0004] This invention provides a method and system for detecting leaks in pipeline flange connections, which improves the reliability and real-time performance of leak detection and enhances the safe and stable operation of industrial plants.

[0005] On the one hand, the present invention provides a detection system for pipeline flange connection leakage, which includes a multi-channel strain sensor, a distributed gas concentration detection device, a central data processing device, an environmental compensation device, and a remote monitoring terminal.

[0006] The multi-channel strain sensor is used to acquire the strain signal of the flange bolt and convert the strain signal to obtain the axial tensile force data of the bolt.

[0007] The distributed gas concentration detection device is configured to detect leaked gas concentration data on the outside of the flange sealing surface;

[0008] The environmental compensation device is configured to acquire interference parameter data of the environment in which the flange is located, including temperature data, vibration data, and pressure data.

[0009] The central data processing device is configured to correct the axial tensile force data and the leakage gas concentration data, and to perform multi-source information fusion processing on the corrected axial tensile force data and the corrected leakage gas concentration data to obtain a comprehensive sealing status score.

[0010] The remote monitoring terminal is configured to receive the comprehensive sealing status score, corrected axial tensile force data, and corrected leak gas concentration data, determine the sealing level of the flange based on a preset sealing level standard, and output corresponding status prompts and warning information according to the sealing level.

[0011] On the other hand, the present invention also provides a method for detecting leakage in pipeline flange connections, comprising:

[0012] Obtain the strain signal of the flange bolt, and convert the strain signal to obtain the axial tensile force data of the bolt;

[0013] Detect the concentration data of leaked gas on the outer side of the flange sealing surface;

[0014] Obtain interference parameter data of the environment in which the flange is located, including temperature data, vibration data, and pressure data;

[0015] The axial tensile force data and leakage gas concentration data are corrected, and the corrected axial tensile force data and leakage gas concentration data are fused from multiple sources to obtain a comprehensive sealing status score.

[0016] The system receives the comprehensive sealing status score, corrected axial tensile force data, and corrected leakage gas concentration data. Based on the preset sealing level standard, it determines the sealing level of the flange and outputs corresponding status prompts and warning information according to the sealing level.

[0017] The pipeline flange connection leakage detection method and system provided by this invention achieves synchronous acquisition of bolt strain signals through a multi-channel strain sensing device, eliminates the influence of interference factors such as temperature, vibration and pressure using an environmental compensation device, and performs multi-source information fusion through a central data processing device to accurately assess the flange sealing status. This enables synchronous multi-channel acquisition of flange bolt strain signals, effectively compensates for the influence of environmental interference such as temperature, vibration and pressure on the measurement data, and accurately assesses the flange sealing status through multi-source information fusion technology, thereby improving the reliability and real-time performance of leakage detection. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the structure of a pipeline flange connection leakage detection system provided in an embodiment of the present invention;

[0020] Figure 2 This is a schematic flowchart of a pipeline flange connection leakage detection method provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] Figure 1 This is a schematic diagram of the structure of a pipeline flange connection leakage detection system provided in an embodiment of the present invention.

[0024] like Figure 1 As shown, the pipeline flange connection leakage detection system provided in this embodiment of the invention may include a multi-channel strain sensor 11, a distributed gas concentration detection device 12, a central data processing device 13, an environmental compensation device 14, and a remote monitoring terminal 15.

[0025] The multi-channel strain sensor 11 is used to acquire the strain signal of the flange bolt and convert the strain signal to obtain the axial tensile force data of the bolt.

[0026] The distributed gas concentration detection device 12 is configured to detect the leakage gas concentration data on the outside of the flange sealing surface;

[0027] The environmental compensation device 14 is configured to acquire interference parameter data of the environment in which the flange is located, including temperature data, vibration data and pressure data.

[0028] The central data processing device 13 is configured to correct the axial tensile force data and the leakage gas concentration data, and to perform multi-source information fusion processing on the corrected axial tensile force data and the corrected leakage gas concentration data to obtain a comprehensive sealing status score.

[0029] The remote monitoring terminal 15 is configured to receive the comprehensive sealing status score, corrected axial tensile force data, and corrected leakage gas concentration data, determine the sealing level of the flange based on a preset sealing level standard, and output corresponding status prompts and warning information according to the sealing level.

[0030] In practical applications, the multi-channel strain sensing device 11 can acquire signals using strain gauges arranged on the flange bolts, such as resistive strain gauges or fiber optic strain sensors. Furthermore, the distributed gas concentration detection device 12 can be implemented in various ways, such as detecting leaked gas concentration using a gas sensor array arranged around the flange sealing surface, or using infrared imaging technology to analyze the gas absorption characteristics at specific wavelengths to determine the gas concentration distribution. The environmental compensation device 14 can be implemented by integrating multiple sensor modules, such as temperature sensors, vibration accelerometers, and pressure sensors. The data from these sensors can directly reflect the interference parameters of the environment in which the flange is located. Specifically, the correction function of the central data processing device 13 can be implemented by calling preset mathematical models or algorithms, such as using linear regression models or neural network models to correct the original data. As a preferred embodiment, the remote monitoring terminal 15 can determine the sealing level based on a rule engine, for example, by setting a series of logical judgment conditions to map the comprehensive score to different sealing levels.

[0031] In detail, the strain signal of the flange bolts is acquired by the multi-channel strain sensor 11 and converted into axial tensile force data. A distributed gas concentration detection device 12 detects the leakage gas concentration data on the outer side of the flange sealing surface, providing direct evidence of seal failure. An environmental compensation device 14 acquires interference parameter data such as temperature, vibration, and pressure, providing an environmental basis for subsequent data correction. A central data processing device 13 corrects the axial tensile force data and leakage gas concentration data based on the interference parameter data and performs multi-source information fusion processing on the corrected data to obtain a comprehensive sealing status score. Environmental compensation eliminates the influence of interference factors, thus comprehensively and accurately reflecting the sealing status of the flange. The comprehensive sealing status score and related correction data are received by the remote monitoring terminal 15. Based on preset standards, the sealing level is determined, and status prompts and warning information are output, realizing closed-loop management from data acquisition to decision support. This facilitates timely intervention by operators and prevents the escalation of leakage accidents.

[0032] Therefore, by transforming strain signals into axial tensile force, compensating for environmental interference, and fusing multi-source data, the system collaboratively solves problems such as inaccurate preload monitoring, environmental interference, and difficulties in real-time assessment, thereby improving the reliability and real-time performance of leak detection.

[0033] In some embodiments, the present invention further proposes a central data processing device 13 comprising the following modules: a first calling module configured to call a preset environmental interference transmission matrix, wherein the environmental interference transmission matrix pre-stores correction coefficients corresponding to the interference parameter data; a first correction module configured to use the correction coefficients in the environmental interference transmission matrix to calculate the total correction amount of axial tensile force and the total correction amount of gas concentration respectively; a second correction module configured to subtract the total correction amount of axial tensile force from the axial tensile force data to obtain the initially corrected axial tensile force data; a third correction module configured to subtract the total correction amount of gas concentration from the leaked gas concentration data to obtain the initially corrected leaked gas concentration data; a construction module configured to input the initially corrected axial tensile force data, the initially corrected leaked gas concentration data, and the interference parameter data into an improved Kalman filter algorithm to construct a state equation including a bolt preload attenuation factor and a gas diffusion coefficient, and to offset data fluctuations through iterative calculation; and an output module configured to output the filtered and corrected axial tensile force data and the filtered and corrected leaked gas concentration data to complete the correction of the axial tensile force data and the leaked gas concentration data.

[0034] The environmental disturbance transfer matrix stores a data structure relating disturbance parameters such as temperature, vibration, and pressure to their corresponding correction coefficients. This structure can be implemented using a multidimensional array or an associative table. The core purpose of this matrix is ​​to quantify the specific impact of environmental disturbances on axial tensile force and gas concentration through experimental calibration. The correction coefficients are obtained by measuring the impact of unit changes in disturbance parameters on the target data through multiple sets of experimental tests under various operating conditions, ensuring that the matrix accurately reflects the disturbance characteristics under complex environments.

[0035] Specifically, the function of the first correction module is to independently calculate the correction amount of each interference parameter on the target data based on the correction coefficients in the environmental interference transfer matrix, and then accumulate them to form the total correction amount. For example, for axial tensile force data, the correction amount of temperature on axial tensile force can be obtained by multiplying the temperature data by the first correction coefficient, and the correction amount of vibration on axial tensile force can be obtained by multiplying the vibration data by the second correction coefficient. Finally, the two are superimposed to form the total correction amount of axial tensile force.

[0036] Furthermore, the module's role is to combine the initially corrected data with interference parameters and perform dynamic optimization using an improved Kalman filter algorithm. Specifically, this module first defines a state vector, constructing a state equation that includes the bolt preload attenuation factor and gas diffusion coefficient, based on the initially corrected axial tension, gas concentration, and interference parameters. Subsequently, by combining process noise and observation noise terms, a dynamic prediction and update mechanism is established, gradually converging to the optimal estimate through iterative calculations. This process not only incorporates the physical laws of the sealing state but also effectively suppresses data fluctuations by adaptively adjusting the noise variance.

[0037] Specifically, the above scheme addresses data fluctuations caused by environmental interference through a phased correction strategy. First, the first calling module invokes the environmental interference transfer matrix, providing precise quantification for subsequent corrections. Next, the first correction module calculates the total correction using correction coefficients in the matrix, achieving independent quantification and superposition of multi-source interference. Based on this, the second and third correction modules perform preliminary corrections on the original data, effectively eliminating systematic biases. Subsequently, the construction module further optimizes the data using an improved Kalman filter algorithm, dynamically tracking trends using a physical model and real-time data. Finally, the output module provides highly reliable correction results.

[0038] In some embodiments, the present invention further proposes specific steps for the construction module to execute the improved Kalman filter algorithm, including:

[0039] Define the state vector: Based on the initially corrected axial tensile force, gas concentration and disturbance parameters, construct a state vector containing axial tensile force parameters, gas concentration parameters, bolt preload attenuation factor and gas diffusion coefficient.

[0040] Equation construction: Combining the preload decay law and the gas diffusion law, a state equation containing process noise term is constructed; using the preliminary corrected data as the observation value, and combining the influence of interference parameters, an observation equation containing observation noise term is constructed.

[0041] Initialization parameters: Input the initial service data of the flange, determine the initial values ​​of each parameter of the state vector, and preset the initial state covariance matrix.

[0042] Prediction and Update: First, calculate the current predicted state vector and covariance matrix using the state vector and state equation from the previous time step; then calculate the Kalman gain, combine it with the observation values ​​to correct the prediction results, obtain the optimal state vector and update the covariance matrix.

[0043] Iterative convergence: Repeat the prediction and update steps and dynamically adjust the variance of the noise term; when the difference between the axial tension and gas concentration parameters in two consecutive iterations is less than the preset threshold, the iteration stops and the filtered and corrected data is output.

[0044] Specifically, the state vector can be implemented using a multi-dimensional vector containing axial tensile force parameters, gas concentration parameters, bolt preload attenuation factors, and gas diffusion coefficients. The purpose of this design is to closely couple the physical laws of bolt preload attenuation and gas diffusion, thereby accurately reflecting the evolution of the sealing state. The state equation is a mathematical expression describing the change of the system state over time. It can be implemented by combining the preload attenuation law and the gas diffusion law to generate a model containing process noise terms, aiming to accurately predict system behavior and accommodate the randomness of environmental disturbances. The observation equation can be implemented by using preliminary corrected data as observations and incorporating the influence of disturbance parameters to construct a model containing observation noise terms, aiming to dynamically correct measurement errors and improve adaptability to disturbances such as temperature and vibration. The initial state covariance matrix is ​​a matrix used to characterize the uncertainty of the initial estimation of the state vector, which can be implemented by presetting a reasonable numerical range based on the initial service data of the flange. The Kalman gain can be dynamically adjusted through optimization algorithms to ensure that a smooth and reliable estimate is still output even when data fluctuations are severe.

[0045] In detail, the above technical solution, through a systematic definition of the execution flow of the improved Kalman filter algorithm, enables stable convergence of data fluctuations under dynamic environmental disturbances. When defining the state vector, it is constructed based on the initially corrected axial tension, gas concentration, and disturbance parameters. This tightly couples the physical laws of bolt preload decay and gas diffusion, incorporating key dynamic factors such as preload decay factor and gas diffusion coefficient into the model. In the equation construction stage, a state equation containing process noise terms is generated by combining the preload decay law and the gas diffusion law. This accurately predicts system behavior and accommodates the randomness of environmental disturbances. Simultaneously, an observation equation containing observation noise terms is constructed using the initially corrected data as observation values ​​and incorporating the influence of disturbance parameters. This allows the model to dynamically correct measurement errors and significantly improve its adaptability to disturbances such as temperature and vibration. In the parameter initialization step, the initial service data of the flange is input to determine the initial values ​​of the state vector and the covariance matrix, ensuring that the filtering starting point is consistent with the actual working conditions, laying the foundation for algorithm stability. In the prediction and update phase, the prediction result is calculated using the state vector and state equation from the previous time step, and then corrected by fusing new observations using Kalman gain. This mechanism dynamically balances the weights of historical data and real-time measurements, ensuring smooth and reliable estimates even when data fluctuations are severe. In the iterative convergence process, predictions are repeatedly updated and noise variance is dynamically adjusted. A threshold difference between axial tension and gas concentration parameters is used as a stopping condition to ensure adaptive convergence of the algorithm under changing environmental disturbances, ultimately outputting stable corrected data.

[0046] In some embodiments, the environmental interference transmission matrix stores a first correction coefficient for temperature data on axial tensile force data and a second correction coefficient for vibration data on axial tensile force data. The first correction module is specifically configured to: multiply the temperature data by the first correction coefficient for temperature-corresponding axial tensile force in the environmental interference transmission matrix to obtain a first correction amount for temperature on axial tensile force; multiply the vibration data by the second correction coefficient for vibration-corresponding axial tensile force in the environmental interference transmission matrix to obtain a second correction amount for vibration on axial tensile force; and calculate the sum of the first correction amount and the second correction amount to obtain the total correction amount for axial tensile force.

[0047] In detail, this scheme establishes a standardized interference mapping relationship by pre-storing a first correction coefficient for axial tensile force based on temperature data and a second correction coefficient based on vibration data within an environmental interference transmission matrix. During actual operation, the first correction module first multiplies the real-time acquired temperature data by the first correction coefficient to generate a first correction amount for the temperature-induced axial tensile force. This process utilizes the linear proportional relationship between temperature change and axial tensile force deviation, dynamically reflecting the strain signal drift caused by the thermal expansion effect of the material. Subsequently, the first correction module multiplies the vibration data by the second correction coefficient to generate a second correction amount for the vibration-induced axial tensile force. This operation converts vibration intensity or acceleration into an equivalent change in axial tensile force, effectively quantifying the instantaneous impact characteristics of high-frequency noise on bolt strain. Finally, by summing the first and second correction amounts, the influence of both temperature and vibration—the two main interference sources—is comprehensively considered, ensuring that the total correction amount can fully offset the superposition effect of multi-source interference. The first and second correction coefficients are quantitative parameters obtained based on experimental calibration, reflecting the degree of linear influence of temperature change and vibration intensity on the axial tensile force data, respectively.

[0048] In some embodiments, the present invention further proposes that the environmental interference transmission matrix also pre-stores a third correction coefficient for the temperature data on the leaked gas concentration data and a fourth correction coefficient for the pressure data on the leaked gas concentration data. The first correction module is further configured to multiply the temperature data by the third correction coefficient for the temperature-corresponding gas concentration in the environmental interference transmission matrix to obtain a third correction amount for the temperature on the gas concentration; multiply the pressure data by the fourth correction coefficient for the pressure-corresponding gas concentration in the environmental interference transmission matrix to obtain a fourth correction amount for the pressure on the gas concentration; and calculate the sum of the third correction amount and the fourth correction amount to obtain the total correction amount for the gas concentration.

[0049] In detail, to address the issue of gas concentration data being easily distorted by interference in complex environments, precise environmental compensation for gas concentration data is achieved by expanding the coverage of the environmental interference transmission matrix and designing dynamic correction logic. A third correction coefficient for leaked gas concentration data based on pre-stored temperature data is used. This is based on the physical characteristic that temperature changes significantly alter the diffusion rate of gas molecules and sensor sensitivity, enabling the system to quantify the specific impact of temperature fluctuations on concentration readings. A fourth correction coefficient for leaked gas concentration data based on pre-stored pressure data is used. Considering that pressure changes directly affect gas density and leakage diffusion paths, the pre-stored coefficient accurately captures the interference patterns of pressure factors on concentration data, ensuring the correction process conforms to actual operating conditions. Multiplying the temperature data by the third correction coefficient yields the third correction amount. Using real-time acquired temperature data combined with the pre-stored coefficient, the deviation value of gas concentration under the current temperature condition is dynamically calculated, ensuring the correction amount is closely related to the on-site environmental conditions. Multiplying the pressure data by the fourth correction coefficient yields the fourth correction amount. Based on real-time pressure data, the concentration deviation caused by pressure is generated, enabling the correction process to respond to the instantaneous characteristics of pressure fluctuations. The sum of the third and fourth corrections is calculated to obtain the total correction for gas concentration. By integrating the correction effects of multi-source interference, the combined effects of temperature and pressure on gas concentration data are fully offset, providing a reliable data foundation for subsequent sealing status fusion analysis.

[0050] The third correction factor can be understood as a numerical parameter that quantifies the impact of temperature changes on gas concentration detection data; it can be obtained through experimental calibration or numerical simulation. The fourth correction factor is a correction parameter that reflects the degree of interference of pressure changes on gas concentration readings; it can be determined through actual operating condition testing or theoretical derivation.

[0051] In some embodiments, the present invention further proposes a first calling module, which is also configured to: calibrate the corresponding correction coefficient in the environmental interference transmission matrix by: conducting comparative experiments under multiple temperature, vibration, and pressure conditions based on flange material, bolt type, and sealing medium type, measuring the influence of unit interference parameter change on axial tensile force data and leaked gas concentration data, and storing the influence as a correction coefficient in the environmental interference transmission matrix.

[0052] Specifically, the combination design of flange material, bolt type, and sealing medium type is to ensure a high degree of consistency between experimental conditions and actual application scenarios, as different materials and media exhibit significant differences in sensitivity to interference parameters. Conducting comparative experiments under multiple temperature, vibration, and pressure conditions aims to cover the combined variation range of interference parameters and reveal the comprehensive impact of single or combined interference on monitoring data. The impact of unit interference parameter changes on axial tensile force data and leaked gas concentration data refers to the direct quantification of the causal relationship between interference parameters and key monitoring data through experiments. This can be achieved through high-precision sensor acquisition combined with data analysis algorithms.

[0053] In detail, this scheme solves the problem of inaccurate correction coefficient sources through an experiment-driven calibration method. Experiments based on flange material, bolt type, and sealing medium type accurately reproduce the interference effects under operating conditions, avoiding insufficient adaptability caused by universal coefficients. Comparative experiments under multiple temperature, vibration, and pressure conditions not only overcome the limitations of single-condition experiments but also make the calibration results more universal. Measuring the impact of unit interference parameter changes on axial tensile force and leaked gas concentration data provides objective measurement basis for the correction coefficients, ensuring the scientific nature of the correction process. Storing the impact as correction coefficients in the environmental interference transfer matrix achieves direct conversion from experimental data to compensation parameters, eliminating errors introduced by empirical estimation, thereby improving the overall accuracy of leak detection.

[0054] In a specific implementation, the first calling module is further configured to: The calibration process of the corresponding correction coefficients in the environmental interference transmission matrix includes:

[0055] Select flange samples, bolt samples, and sealing medium samples that are consistent with the actual application scenario. The material, nominal diameter, and nominal pressure of the flange samples are consistent with the actual flanges. The model and strength grade of the bolt samples are consistent with the actual bolts. The sealing medium samples are consistent with the type of medium actually in service.

[0056] Design a combination of multiple sets of temperature, vibration and pressure conditions for experiments;

[0057] Under each working condition, the bolts are pre-tightened to the rated pre-tightening force using a standard torque wrench, and a sealing medium with a preset flow rate is injected through a standard gas generator. Simultaneously, the reference values ​​of axial tension and gas concentration under undisturbed working conditions, as well as the measured values ​​of axial tension and gas concentration under the corresponding disturbed working conditions, are collected.

[0058] Calculate the first influence of unit temperature change on axial tensile force data, and use the first influence as the first correction coefficient for temperature-corresponding axial tensile force in the environmental disturbance transfer matrix.

[0059] Calculate the second influence of unit vibration change on axial tensile force data, and use the second influence as the second correction coefficient for the axial tensile force corresponding to vibration in the environmental disturbance transmission matrix.

[0060] Calculate the third influence of unit temperature change on gas concentration data, and use the third influence as the third correction coefficient for temperature-corresponding gas concentration in the environmental disturbance transfer matrix.

[0061] Calculate the fourth influence of unit pressure change on gas concentration data, and use the fourth influence as the fourth correction coefficient for pressure-corresponding gas concentration in the environmental disturbance transmission matrix.

[0062] The first, second, third, and fourth correction coefficients are stored in the environmental interference transmission matrix according to the correspondence between the interference parameter type and the monitoring data type.

[0063] Among them, the combined experiments with multiple temperature, vibration, and pressure conditions refer to multi-parameter coupled experimental schemes generated through orthogonal experimental design methods. These schemes can be implemented using full-factor or partial-factor experimental design, aiming to cover the multi-factor interactions in complex industrial environments. A standard torque wrench is a tool capable of precisely controlling torque output; it can be an electronic digital torque wrench or a mechanical torque wrench, aiming to ensure consistent bolt preload. A standard gas generator can be a gas proportioning system based on a mass flow controller, designed to provide a stable gas flow output.

[0064] In practical applications, the first influence of unit temperature change on axial tensile force data refers to the linear relationship coefficient between temperature change and axial tensile force change determined experimentally. This coefficient can be calculated using least squares fitting or multi-point interpolation, aiming to quantify the specific interference effect of temperature on axial tensile force. Similarly, the second influence of unit vibration change on axial tensile force data, the third influence of unit temperature change on gas concentration data, and the fourth influence of unit pressure change on gas concentration data are all directly calculated from experimental data. Their purpose is to capture the independent effect of each interference parameter on the monitoring data.

[0065] The above-described scheme, together with the aforementioned axial tensile force data correction mechanism, forms a complete environmental compensation system. By establishing independent yet interrelated correction logics for axial tensile force and gas concentration, it effectively improves the anti-interference capability and detection accuracy of the entire detection system. This two-dimensional correction strategy is particularly suitable for complex industrial scenarios involving multiple coupled factors, such as flange connections, and can significantly improve the reliability of leak detection results.

[0066] In some embodiments, the present invention further proposes specific steps for calculating the first influence of a unit temperature change on axial tensile force data, including:

[0067] Calculate the first difference between the measured value of axial tensile force and the reference value of axial tensile force, and calculate the first ratio of the first difference to the temperature change as the first influencing factor;

[0068] The ratio of the first difference to the second change in vibration acceleration is calculated as the second influencing factor.

[0069] Calculate the second difference between the measured gas concentration and the reference gas concentration, and calculate the third ratio of the second difference to the temperature change as the third influencing factor;

[0070] The fourth ratio of the second difference to the pressure change is calculated as the fourth influencing factor.

[0071] Specifically, the purpose of the first difference is to quantify the change in axial tensile force caused by disturbance. The axial tensile force reference value refers to the ideal state value determined by standard experiments under undisturbed working conditions. It can be obtained using a standard torque wrench and preload measuring device, aiming to provide a reliable reference benchmark. In practical applications, the first ratio can reflect the linear effect of unit temperature change on axial tensile force.

[0072] Meanwhile, the second difference aims to capture the actual disturbance of the leaked gas concentration caused by interference. The third ratio refers to the proportional relationship between the second difference and the temperature change, which can be achieved through experimental data fitting or numerical analysis methods, aiming to accurately express the unit effect of temperature on gas concentration. Furthermore, the fourth ratio aims to quantify the unit effect of pressure fluctuations on gas concentration.

[0073] Specifically, firstly, a first difference is calculated based on the measured value of axial tensile force and a benchmark value. This difference directly quantifies the deviation of axial tensile force caused by disturbance, providing an accurate deviation benchmark for subsequent calculations of influence. Based on this, a first ratio is calculated by correlating the first difference with the temperature change, ensuring that the temperature correction coefficient is consistent with actual physical laws and avoiding subjective estimation. Subsequently, a second ratio is calculated using the same first difference and the change in vibration acceleration, precisely quantifying the unit impact of vibration on axial tensile force, ensuring that the calibration of the vibration correction coefficient is based on measured data rather than empirical assumptions.

[0074] Furthermore, by calculating the second difference between the measured gas concentration and the baseline value, the actual disturbance to the leaked gas concentration caused by interference is captured, providing a reliable input for the gas concentration-related influence. Based on this, a third ratio is calculated by establishing a proportional relationship between the second difference and the temperature change, accurately expressing the unit effect of temperature on gas concentration and ensuring that the temperature correction factor effectively offsets thermal effect interference. Finally, a fourth ratio is calculated by directly correlating the second difference with the pressure change, quantifying the unit effect of pressure fluctuations on gas concentration, and ensuring that the calibration of the pressure correction factor conforms to the physical characteristics of gas diffusion.

[0075] In some embodiments, the central data processing device 13 further includes:

[0076] The first determining module is used to determine the bolt preload state level based on the corrected axial tensile force data, wherein the bolt preload state level is obtained by comparing the corrected axial tensile force data with a preload threshold range, and the preload threshold range includes a normal range, a warning range, and a dangerous range.

[0077] The second determining module is configured to determine a gas leak status level based on the corrected leak gas concentration data, wherein the gas leak status level is obtained by comparing the corrected leak gas concentration data with a concentration threshold range, the concentration threshold range including a no-leak range, a slight-leak range, and a severe-leak range.

[0078] The second calling module is configured to call a pre-stored sealing state mapping table, which stores a first basic score corresponding to different bolt preload state levels and a second basic score corresponding to combinations of gas leakage state levels.

[0079] The third calling module is configured to call a pre-stored environmental adaptive weight matrix based on the interference parameter data. The environmental adaptive weight matrix stores the first weight adjustment coefficient for the bolt preload state level and the second weight adjustment coefficient for the gas leakage state level under different interference parameter data. For example, in a high-temperature environment, the weight of the preload state is automatically reduced while the weight of the leakage concentration is increased, thereby adapting to the differences in the influence of complex working conditions on the sealing state.

[0080] The sealing scoring module is configured to use the first weight adjustment coefficient and the second weight coefficient to perform weighted correction on the first basic score and the second weight adjustment coefficient to obtain a comprehensive sealing status score.

[0081] Specifically, the first determination module can be implemented by setting multi-level threshold ranges. For example, axial tensile force data can be divided into normal, warning, and dangerous ranges. The purpose is to discretize continuously changing data for easier intuitive monitoring and early warning. The second determination module can similarly divide the ranges into no leakage, slight leakage, and severe leakage, aiming to quantify the degree of leakage and provide a basis for risk assessment. The sealing status mapping table in the second call module is a standardized tool that can be stored in the form of a two-dimensional table or database. It is used to map discretized status levels to calculable basic scores, avoiding the influence of subjective experience on the scores. The environmental adaptive weight matrix in the third call module is a dynamic adjustment mechanism that can be implemented in the form of a multi-dimensional array or rule base. It adjusts the weights according to changes in disturbance parameters such as temperature, vibration, and pressure, thereby improving the robustness of the assessment results. The sealing score module can generate a single comprehensive sealing status score through weighted correction. The purpose is to balance the contribution of preload decay and gas leakage, making the final score more consistent with the actual sealing condition.

[0082] Based on the same general inventive concept, this invention also protects a method for detecting leaks in pipeline flange connections. The following describes a method for detecting leaks in pipeline flange connections provided by this invention. The method for detecting leaks in pipeline flange connections described below can be referred to in correspondence with the method for detecting leaks in pipeline flange connections described above.

[0083] Figure 2 This is a schematic flowchart of a method for detecting leaks in pipeline flange connections provided by an embodiment of the present invention. Figure 2 As shown, a method for detecting leakage in a pipeline flange connection according to this embodiment includes the following steps:

[0084] 201. Obtain the strain signal of the flange bolt, and convert the strain signal to obtain the axial tensile force data of the bolt;

[0085] 202. Detect the leakage gas concentration data on the outer side of the flange sealing surface;

[0086] 203. Obtain interference parameter data of the environment in which the flange is located, including temperature data, vibration data, and pressure data;

[0087] 204. Correct the axial tensile force data and leakage gas concentration data, and perform multi-source information fusion processing on the corrected axial tensile force data and corrected leakage gas concentration data to obtain a comprehensive sealing status score.

[0088] 205. Receive the comprehensive sealing status score, corrected axial tensile force data, and corrected leakage gas concentration data; determine the sealing level of the flange based on the preset sealing level standard; and output the corresponding status prompts and warning information according to the sealing level.

[0089] It should be noted that all relevant information that may be involved in the various embodiments of the present invention is processed in strict accordance with the requirements of laws and regulations, following the principles of legality, legitimacy, and necessity, based on the reasonable purpose of the business scenario, and is information that users actively provide or generate during the use of the product / service, as well as information obtained with user authorization.

[0090] The information processed by this invention may vary depending on the specific product / service scenario and should be based on the specific scenario in which the user uses the product / service. This may involve user account information, device information, or other related information. This invention will treat the relevant information and its processing with the utmost diligence.

[0091] This invention places great emphasis on the security of relevant information and has adopted reasonable and feasible security protection measures that comply with industry standards to protect user information and prevent unauthorized access, public disclosure, use, modification, damage or loss of relevant information.

[0092] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A leak detection system for pipeline flange connections, characterized in that, It includes a multi-channel strain sensor, a distributed gas concentration detection device, a central data processing device, an environmental compensation device, and a remote monitoring terminal; The multi-channel strain sensor is used to acquire the strain signal of the flange bolt and convert the strain signal to obtain the axial tensile force data of the bolt. The distributed gas concentration detection device is configured to detect leaked gas concentration data on the outside of the flange sealing surface; The environmental compensation device is configured to acquire interference parameter data of the environment in which the flange is located, including temperature data, vibration data, and pressure data. The central data processing device is configured to correct the axial tensile force data and the leakage gas concentration data, and to perform multi-source information fusion processing on the corrected axial tensile force data and the corrected leakage gas concentration data to obtain a comprehensive sealing status score. The remote monitoring terminal is configured to receive the comprehensive sealing status score, corrected axial tensile force data, and corrected leakage gas concentration data, determine the sealing level of the flange based on a preset sealing level standard, and output corresponding status prompts and warning information according to the sealing level.

2. The pipeline flange connection leakage detection system according to claim 1, characterized in that, The central data processing device includes: The first calling module is configured to call a preset environmental interference transmission matrix, wherein the environmental interference transmission matrix stores correction coefficients corresponding to interference parameter data. The first correction module is configured to use the correction coefficients in the environmental interference transmission matrix to calculate the total correction amount of axial tensile force and the total correction amount of gas concentration, respectively. The second correction module is configured to subtract the total correction amount of axial tensile force from the axial tensile force data to obtain the initially corrected axial tensile force data. The third correction module is configured to subtract the total gas concentration correction amount from the leaked gas concentration data to obtain the initially corrected leaked gas concentration data. The construction module is configured to input the pre-corrected axial tensile force data, the pre-corrected leaked gas concentration data, and the interference parameter data into the improved Kalman filter algorithm to construct a state equation that includes the bolt preload attenuation factor and the gas diffusion coefficient, and to offset data fluctuations through iterative calculation. The output module is configured to output filtered and corrected axial tensile force data and filtered and corrected leaked gas concentration data, thereby completing the correction of the axial tensile force data and leaked gas concentration data.

3. The pipeline flange connection leakage detection system according to claim 2, characterized in that, The specific steps for constructing the module and executing the improved Kalman filter algorithm include: Define the state vector: Based on the initially corrected axial tensile force, gas concentration, and disturbance parameters, construct a state vector containing axial tensile force parameters, gas concentration parameters, bolt preload attenuation factor, and gas diffusion coefficient; Equation construction: Combining the preload decay law and the gas diffusion law, a state equation containing process noise term is constructed; using the preliminary corrected data as the observation value, and combining the influence of interference parameters, an observation equation containing observation noise term is constructed. Initialization parameters: Input the initial service data of the flange, determine the initial values ​​of each parameter of the state vector, and preset the initial state covariance matrix. Prediction and Update: First, calculate the current predicted state vector and covariance matrix using the state vector and state equation from the previous time step; then calculate the Kalman gain, combine it with the observation values ​​to correct the prediction results, obtain the optimal state vector and update the covariance matrix. Iterative convergence: Repeat the prediction and update steps and dynamically adjust the variance of the noise term; when the difference between the axial tension and gas concentration parameters in two consecutive iterations is less than the preset threshold, the iteration stops and the filtered and corrected data is output.

4. The pipeline flange connection leakage detection system according to claim 2, characterized in that, The environmental interference transmission matrix contains a first correction coefficient for temperature data on axial tensile force data and a second correction coefficient for vibration data on axial tensile force data. The first correction module is specifically configured as follows: Multiply the temperature data by the first correction coefficient of the axial tensile force corresponding to the temperature in the environmental interference transmission matrix to obtain the first correction amount of temperature to the axial tensile force. Multiply the vibration data by the second correction coefficient of the axial tensile force corresponding to the vibration in the environmental disturbance transfer matrix to obtain the second correction amount of the vibration on the axial tensile force. Calculate the sum of the first correction and the second correction to obtain the total correction for axial tensile force.

5. The pipeline flange connection leakage detection system according to claim 4, characterized in that, The environmental interference transmission matrix also pre-stores a third correction coefficient for temperature data on leaked gas concentration data and a fourth correction coefficient for pressure data on leaked gas concentration data. The first correction module is also configured as follows: The temperature data is multiplied by the third correction coefficient of the gas concentration corresponding to the temperature in the environmental interference transmission matrix to obtain the third correction amount of temperature to gas concentration. The pressure data is multiplied by the fourth correction coefficient of the gas concentration corresponding to the pressure in the environmental disturbance transmission matrix to obtain the fourth correction amount of pressure to gas concentration. Calculate the sum of the third and fourth corrections to obtain the total gas concentration correction.

6. The pipeline flange connection leakage detection system according to claim 5, characterized in that, The first calling module is further configured to: calibrate the corresponding correction coefficients in the environmental interference transfer matrix in the following manner: Based on flange material, bolt type, and sealing medium type, comparative experiments were conducted under multiple temperature, vibration, and pressure conditions to determine the impact of unit interference parameter changes on axial tensile force data and leakage gas concentration data. The impact was then stored as a correction coefficient in the environmental interference transfer matrix.

7. The pipeline flange connection leakage detection system according to claim 6, characterized in that, The first calling module is further configured to: The calibration process of the corresponding correction coefficients in the environmental interference transfer matrix includes: Select flange samples, bolt samples, and sealing medium samples that are consistent with the actual application scenario. The material, nominal diameter, and nominal pressure of the flange samples are consistent with the actual flanges. The model and strength grade of the bolt samples are consistent with the actual bolts. The sealing medium samples are consistent with the type of medium actually in service. Design a combination of multiple sets of temperature, vibration and pressure conditions for experiments; Under each working condition, the bolts are pre-tightened to the rated pre-tightening force using a standard torque wrench, and a sealing medium with a preset flow rate is injected through a standard gas generator. Simultaneously, the reference values ​​of axial tension and gas concentration under undisturbed working conditions, as well as the measured values ​​of axial tension and gas concentration under the corresponding disturbed working conditions, are collected. Calculate the first influence of unit temperature change on axial tensile force data, and use the first influence as the first correction coefficient for temperature-corresponding axial tensile force in the environmental disturbance transfer matrix. Calculate the second influence of unit vibration change on axial tensile force data, and use the second influence as the second correction coefficient for the axial tensile force corresponding to vibration in the environmental disturbance transmission matrix. Calculate the third influence of unit temperature change on gas concentration data, and use the third influence as the third correction coefficient for temperature-corresponding gas concentration in the environmental disturbance transfer matrix. Calculate the fourth influence of unit pressure change on gas concentration data, and use the fourth influence as the fourth correction coefficient for pressure-corresponding gas concentration in the environmental disturbance transmission matrix. The first, second, third, and fourth correction coefficients are stored in the environmental interference transmission matrix according to the correspondence between the interference parameter type and the monitoring data type.

8. The pipeline flange connection leakage detection system according to claim 7, characterized in that, The first effect of unit temperature change on axial tensile force data is calculated, including: Calculate the first difference between the measured value of axial tensile force and the reference value of axial tensile force, and calculate the first ratio of the first difference to the temperature change as the first influencing factor; The ratio of the first difference to the second change in vibration acceleration is calculated as the second influencing factor. Calculate the second difference between the measured gas concentration and the reference gas concentration, and calculate the third ratio of the second difference to the temperature change as the third influencing factor; The fourth ratio of the second difference to the pressure change is calculated as the fourth influencing factor.

9. The pipeline flange connection leakage detection system according to claim 6, characterized in that, The central data processing unit further includes: The first determining module is used to determine the bolt preload state level based on the corrected axial tensile force data, wherein the bolt preload state level is obtained by comparing the corrected axial tensile force data with a preload threshold range, and the preload threshold range includes a normal range, a warning range, and a dangerous range. The second determining module is configured to determine a gas leak status level based on the corrected leak gas concentration data, wherein the gas leak status level is obtained by comparing the corrected leak gas concentration data with a concentration threshold range, the concentration threshold range including a no-leak range, a slight-leak range, and a severe-leak range. The second calling module is configured to call a pre-stored sealing state mapping table, which stores a first basic score corresponding to different bolt preload state levels and a second basic score corresponding to combinations of gas leakage state levels. The third calling module is configured to call a pre-stored environmental adaptive weight matrix based on the interference parameter data. The environmental adaptive weight matrix stores the first weight adjustment coefficient for the bolt preload state level and the second weight adjustment coefficient for the gas leakage state level under different interference parameter data. The sealing scoring module is configured to use the first weight adjustment coefficient and the second weight coefficient to perform weighted correction on the first basic score and the second weight adjustment coefficient to obtain a comprehensive sealing status score.

10. A method for detecting leakage in a pipeline flange connection, characterized in that, include: Obtain the strain signal of the flange bolt, and convert the strain signal to obtain the axial tensile force data of the bolt; Detect the concentration data of leaked gas on the outer side of the flange sealing surface; Obtain interference parameter data of the environment in which the flange is located, including temperature data, vibration data, and pressure data; The axial tensile force data and leakage gas concentration data are corrected, and the corrected axial tensile force data and leakage gas concentration data are fused from multiple sources to obtain a comprehensive sealing status score. The system receives the comprehensive sealing status score, corrected axial tensile force data, and corrected leakage gas concentration data. Based on the preset sealing level standard, it determines the sealing level of the flange and outputs corresponding status prompts and warning information according to the sealing level.

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