Flange Connection Tightness Calculation Modeling Method, Calculation Method, System, Equipment and Medium

Abstract

The present invention discloses a flange connection tightness calculation modeling method, calculation method, system, equipment, and medium. The modeling method includes obtaining historical gaps between flange joints under different temperatures and loads; constructing a basic gap physical model based on the historical gaps between flange joints under different temperatures and loads; calculating gap deviations under different temperatures and loads based on the basic gap physical model and the historical gaps between flange joints under different temperatures and loads; constructing a sample data set based on the gap deviations under different temperatures and loads; and deploying a deep learning model, training the deep learning model using the sample data set, and obtaining a gap deviation prediction model. The present invention improves the calculation accuracy of flange connection tightness and enables real-time and continuous calculation of flange connection tightness.

Description

Technical Field

[0001] The present invention belongs to the technical field of flange connection safety, and in particular relates to a flange connection tightness calculation modeling method, a calculation method, a system, a device and a medium. Background Art

[0002] Flange connections are one of the most common connections in industrial equipment, widely used in critical applications such as wind power equipment, aerospace equipment, and petrochemical pipelines. The tightness of flange connections is directly related to the safety, reliability, and service life of the equipment. However, in actual operation, flange connections can lose tightness due to factors such as temperature fluctuations, load fluctuations, and bolt loosening, potentially leading to serious problems such as leakage, vibration, and structural damage.

[0003] At present, flange connection tightness monitoring mainly relies on the following methods:

[0004] First, regular offline inspections, such as using a torque wrench to detect bolt preload and an ultrasonic thickness gauge to detect bolt length changes, etc.

[0005] The second is simple sensor monitoring, such as pressure sensor monitoring of medium pressure changes, bolt strain monitoring, etc.

[0006] The third is the theoretical calculation method, which predicts the flange connection status based on elasticity theory and finite element analysis.

[0007] However, these methods have obvious shortcomings:

[0008] Offline inspection cannot achieve real-time monitoring, and equipment shutdown inspection increases operation and maintenance costs; simple sensor monitoring cannot fully reflect the actual status of flange connections under complex working conditions; theoretical calculation methods are usually based on ideal assumptions, ignoring actual influencing factors such as temperature changes and dynamic loads, and the calculation accuracy is limited.

[0009] Therefore, it is urgent to propose a method that can accurately, efficiently and in real time evaluate the tightness status of flange connections. Summary of the Invention

[0010] The purpose of the present invention is to provide a flange connection tightness calculation modeling method, calculation method, system, equipment and medium to solve the problems of insufficient monitoring accuracy and poor real-time performance of traditional methods.

[0011] The present invention solves the above technical problems through the following technical solutions: A flange connection tightness calculation modeling method, comprising:

[0012] Obtain historical clearance of flange joints under different temperatures and loads;

[0013] Construct a basic gap physical model based on the historical gap of flange joint surfaces under different temperatures and loads;

[0014] Calculating the gap deviation under different temperatures and loads based on the basic gap physical model and the historical gaps of the flange joint surfaces under different temperatures and loads;

[0015] Construct a sample data set based on gap deviations at different temperatures and loads;

[0016] Deploy a deep learning model and train the deep learning model using the sample data set to obtain a gap deviation prediction model.

[0017] Furthermore, a basic gap physical model is constructed based on the historical gap of the flange joint surface under different temperatures and loads, specifically including:

[0018] Constructing a flange finite element model; wherein the flange finite element model includes an upper flange, a lower flange, and bolts connecting the upper flange and the lower flange, and the gap between the upper flange and the lower flange is the flange joint surface gap;

[0019] Based on the flange finite element model, simulation calculations are performed at different temperatures to obtain simulated gaps of the flange joint surface at different temperatures; the simulated gaps of the flange joint surface at different temperatures are fitted to obtain a temperature-joint surface gap fitting function;

[0020] Based on the flange finite element model, simulation calculations are performed under different loads to obtain simulated clearances of the flange joint surface under different loads; the simulated clearances of the flange joint surface under different loads are fitted to obtain a load-joint surface clearance fitting function;

[0021] The flange joint surface gap caused by temperature and the flange joint surface gap caused by load are calculated based on the historical gap of the flange joint surface under different temperatures and loads, as well as the temperature-joint surface gap fitting function and the load-joint surface gap fitting function;

[0022] According to the flange joint surface gap caused by temperature, the flange joint surface gap caused by load and the flange joint surface historical gap, the basic gap physical model is obtained.

[0023] Furthermore, a sample data set is constructed based on the gap deviation under different temperatures and loads, specifically including:

[0024] Constructing a characteristic vector according to each set of temperature and load; wherein the characteristic vector includes the temperature, the load, and the interaction value between the temperature and the load;

[0025] performing normalization processing on the feature vector;

[0026] The standardized feature vector and its gap deviation constitute a sample, and then a sample data set is obtained.

[0027] Furthermore, there are multiple deep learning models, each of which has the same structure but different initial weights, and each of the multiple deep learning models is trained using the sample data set;

[0028] When each gap deviation prediction model is applied to perform real-time calculation, the final real-time gap deviation is equal to the weighted average of the real-time gap deviations output by each gap deviation prediction model.

[0029] Furthermore, the deep learning model is trained using the sample data set, specifically including:

[0030] Inputting the feature vectors in the sample data set into a deep learning model;

[0031] The deep learning model extracts and predicts the feature vector to obtain a predicted gap deviation;

[0032] The improved LASSO regression loss function is used to calculate the loss error between the gap deviation corresponding to the feature vector and the predicted gap deviation. The parameters of the deep learning model are adjusted according to the loss error to realize the training of the deep learning model.

[0033] Among them, the improved LASSO regression loss function introduces L2 regularization on the basis of the original LASSO regression loss function.

[0034] Based on the same concept, the present invention also provides a method for calculating the tightness of flange connection, including:

[0035] Calling a basic gap physical model and a gap deviation prediction model; wherein the basic gap physical model and the gap deviation prediction model are constructed using the flange connection tightness calculation modeling method described above;

[0036] Get the real-time temperature and load of the flange;

[0037] Inputting the real-time temperature and real-time load into a foundation gap physical model to obtain a real-time foundation gap;

[0038] Preprocessing the real-time temperature and real-time load to obtain a real-time feature vector;

[0039] Inputting the real-time feature vector into a gap deviation prediction model to obtain a real-time gap deviation;

[0040] The real-time gap of the flange joint surface is calculated according to the real-time basic gap and the real-time gap deviation.

[0041] Furthermore, the calculation method also includes providing an early warning based on the calculated real-time clearance of the flange joint surface, specifically including:

[0042] When the real-time gap between the flange joint surfaces is less than the first gap threshold, the flange connection tightness is normal;

[0043] When the first gap threshold ≤ the real-time gap of the flange joint surface < the second gap threshold, a flange connection tightness attention prompt is issued;

[0044] When the second gap threshold ≤ the real-time gap of the flange joint surface < the third gap threshold, a flange connection tightness warning prompt is issued;

[0045] When the real-time gap between the flange joint surfaces is ≥ the third gap threshold, a flange connection tightness danger warning is issued.

[0046] Based on the same concept, the present invention also provides a flange connection tightness calculation system, including a data access layer, a calculation layer and a result output layer;

[0047] The data access layer is used to obtain the real-time temperature and real-time load of the flange and transmit the real-time temperature and real-time load to the calculation layer;

[0048] The computing layer is deployed with a basic gap physical model and a gap deviation prediction model, and is used to input the real-time temperature and real-time load into the basic gap physical model to obtain a real-time basic gap; pre-process the real-time temperature and real-time load to obtain a real-time feature vector; input the real-time feature vector into the gap deviation prediction model to obtain a real-time gap deviation; calculate the real-time gap of the flange joint surface based on the real-time basic gap and the real-time gap deviation; wherein the basic gap physical model and the gap deviation prediction model are constructed using the flange connection tightness calculation modeling method described above;

[0049] The result output layer is used to display the real-time temperature and real-time load of the flange and the calculated real-time gap of the flange joint surface.

[0050] Based on the same concept, the present invention also provides an electronic device, including a memory, a processor, and a computer program / instructions stored on the memory, wherein the processor executes the computer program / instructions to implement the flange connection tightness calculation modeling method or the flange connection tightness calculation method as described above.

[0051] Based on the same concept, the present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the flange connection tightness calculation modeling method or flange connection tightness calculation method as described above.

[0052] Compared with the prior art, the advantages of the present invention are:

[0053] The present invention constructs a basic clearance physical model according to the historical clearance of the flange joint surface under different temperatures and loads, takes into account the influence of temperature and load changes on the flange joint surface clearance, and improves the calculation accuracy of the flange connection tightness through the coupling analysis of temperature field and load field; at the same time, a gap deviation prediction model is constructed based on the deep learning model, and the gap deviation prediction model is used to predict the deviation between the basic clearance and the actual clearance, and the predicted gap deviation is used to correct the basic clearance, thereby further improving the calculation accuracy of the flange connection tightness.

[0054] By deploying the basic gap physical model and gap deviation prediction model constructed by the present invention in actual monitoring conditions, real-time and continuous calculation of flange connection tightness can be achieved, ensuring the safety, reliability and service life of the equipment. It is suitable for flange connection status monitoring in many fields such as wind power, aerospace, and petrochemicals. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only one embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 This is a flow chart of a flange connection tightness calculation modeling method according to an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of a flange connection model in an embodiment of the present invention;

[0058] Figure 3 It is a flow chart of the flange connection tightness calculation method in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following is a clear and complete description of the technical solutions of the present invention in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0060] The following specific embodiments are used to describe the technical solution of the present application in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0061] Example 1

[0062] Figure 1The flow chart of the flange connection tightness calculation modeling method provided by the present invention is shown. Figure 1 As shown, the flange connection tightness calculation modeling method includes the following steps:

[0063] Step A1: Obtain the historical clearance of the flange joint surface under different temperatures and loads.

[0064] This example uses the flange connection of a MW-class wind turbine tower at a wind farm as the research object. We collected 500 sets of historical flange joint clearances under different temperatures and loads. The flange joint clearance refers to the gap between the upper and lower flanges. We used the quartile method to detect anomalies in these 500 sets of flange joint clearances under different temperatures and loads, removing outliers. We then used a sliding window to smooth the detected flange joint clearances, reducing data noise and improving the signal-to-noise ratio by approximately 35%. In this example, the sliding window length was 5.

[0065] Step A2: Construct a basic gap physical model based on the historical gap of the flange joint surface under different temperatures and loads.

[0066] In a specific embodiment of the present invention, a basic gap physical model is constructed according to the historical gaps of the flange joint surfaces under different temperatures and loads, specifically including:

[0067] Step A2.1: Construct the flange finite element model.

[0068] The research object is the flange connection of a MW-class wind turbine tower at a wind farm. The flange structural parameters are: outer diameter 2600mm, inner diameter 2400mm, thickness 80mm, number of bolts 128, and bolt size M36. A 3D flange model is constructed based on these structural parameters and meshed to obtain a finite element model of the flange. In this example, the mesh element type is C3D8R, with approximately 150,000 cells.

[0069] Among them, the flange connection model includes the upper flange, the lower flange and the bolts connecting the upper flange and the lower flange, such as Figure 2 As shown in the figure, a flange finite element model is constructed based on the flange connection model.

[0070] Step A2.2: Based on the flange finite element model, simulation calculations are performed at different temperatures to obtain the simulated clearances of the flange joint surfaces at different temperatures; the simulated clearances of the flange joint surfaces at different temperatures are fitted to obtain a temperature-joint clearance fitting function.

[0071] Based on the wind farm's climatic conditions, the temperature range was set between -40°C and 80°C. Material parameters were based on the nonlinear properties of Q345 steel. For example, the thermal conductivity was 52 W / (m·K) at 20°C and 45 W / (m·K) at -40°C, and the specific heat capacity was 465 J / (kg·K) at 20°C, decreasing to 420 J / (kg·K) at -40°C. Four temperature gradients were set: 5°C / h, 10°C / h, 15°C / h, and 20°C / h, to simulate different cooling rates.

[0072] Finite element simulations were performed on the flange finite element model under multiple temperature conditions to obtain a temperature field distribution cloud map. The simulated gaps of the flange joints at different temperatures were extracted from the temperature field distribution cloud map, and then a temperature-joint gap fitting function was constructed. The specific expression is:

[0073] (1)

[0074] in, Indicates the flange joint surface clearance caused by temperature, unit is mm; T indicates temperature, unit is ℃; 、 、 In this embodiment, the fitting coefficient 、 、 They are 0.00072, -0.0015, and 0.025 respectively.

[0075] The simulation results show that when the temperature drops from 20°C to -20°C, the maximum change in the simulated gap of the flange joint surface is 0.028mm; when the temperature gradient reaches 20°C / h, the local temperature difference of the flange can reach 15°C, resulting in uneven thermal stress, causing the simulated gap of the flange joint surface to increase to 0.035mm.

[0076] Step A2.3: Based on the flange finite element model, simulation calculations are performed under different loads to obtain the simulated clearance of the flange joint surface under different loads; the simulated clearance of the flange joint surface under different loads is fitted to obtain a load-joint surface clearance fitting function.

[0077] The load in this embodiment includes transverse load and axial load. Simulation calculation under transverse load:

[0078] The lateral load range is 0-100kN, and 10 lateral load conditions are set at intervals of 10kN. Considering the elastic-plastic deformation characteristics of the flange material and the contact surface friction coefficient, the contact surface friction coefficient is set to 0.15. The finite element method is used to calculate the deformation and gap distribution of the flange joint surface under each lateral load condition, and the simulated gap of the flange joint surface under different lateral loads is obtained. Then, a lateral load-joint surface gap fitting function is constructed. Its specific expression is:

[0079] (2)

[0080] in, Indicates the gap between flange joints caused by lateral load, in mm; Indicates the lateral load, unit is kN; 、 、 In this embodiment, the fitting coefficient 、 、 They are 0.0000023, 0.00085 and 0.002 respectively.

[0081] The simulation results show that when the lateral load reaches 50kN, the flange joint surface begins to separate locally; when the load reaches 90kN, the maximum simulated gap of the flange joint surface reaches 0.12mm.

[0082] Simulation calculation under axial load:

[0083] The axial load range is 0-200kN, and 10 axial load conditions are set at intervals of 20kN. The flange stress conditions under different preloads (set at 60%, 80%, 100%, and 120% of the standard preload, that is, simulating different bolt connection states) are simulated. The finite element method is used to calculate the deformation of the flange joint surface and the change in the leakage path under each axial load condition, and the simulated gap of the flange joint surface under different axial loads is obtained. Then, the axial load-joint gap fitting function is constructed. Its specific expression is:

[0084] (3)

[0085] in, Indicates the flange joint surface clearance caused by axial load, unit: mm; Indicates axial load, unit kN; 、 、 In this embodiment, the fitting coefficient 、 、 They are 0.000015, 0.00063 and 0.001 respectively.

[0086] Simulation results show that under standard preload, a significant leakage path begins to appear when the axial load reaches 120 kN; when the preload is reduced by 20%, this critical value drops to 90 kN.

[0087] Step A2.4: Calculate the flange joint surface gap caused by temperature and the flange joint surface gap caused by load based on the historical flange joint surface gaps under different temperatures and loads, as well as the temperature-joint surface gap fitting function and the load-joint surface gap fitting function.

[0088] Substitute the temperature and load of the flange joint surface historical gap under different temperatures and loads obtained in step A1 into the temperature-joint surface gap fitting function and the load-joint surface gap fitting function (including the transverse load-joint surface gap fitting function and the axial load-joint surface gap fitting function) to obtain the flange joint surface gap caused by temperature and the flange joint surface gap caused by load. In this way, multiple sets of temperature and the flange joint surface gap caused by temperature, load and the flange joint surface gap caused by load can be obtained, which can be expressed as .

[0089] Step A2.5: Obtain a basic clearance physical model based on the flange joint surface clearance caused by temperature, the flange joint surface clearance caused by load, and the flange joint surface historical clearance.

[0090] The least squares method is used to fit multiple sets of temperature and the flange joint surface gap caused by it, and the flange joint surface gap caused by it, to obtain the basic gap physical model. In this embodiment, the specific expression of the basic gap physical model is:

[0091] (4)

[0092] in, Indicates the foundation gap; 、 、 Both represent weight coefficients, that is, the contribution weights of temperature, lateral load and axial load to the flange joint clearance. In this embodiment, the weight coefficients 、 、 The root mean square error of the physical model of the basic gap tested alone is 0.0125 mm.

[0093] Step A3: Calculate the gap deviation under different temperatures and loads based on the basic gap physical model and the historical gaps of the flange joint surfaces under different temperatures and loads.

[0094] Substitute the temperature and load of the historical gap of the flange joint surface under different temperatures and loads obtained in step A1 into the temperature-joint surface gap fitting function and the load-joint surface gap fitting function, respectively, to obtain the flange joint surface gap caused by temperature and the flange joint surface gap caused by load; then substitute the flange joint surface gap caused by temperature and the flange joint surface gap caused by load into the basic gap physical model to obtain the basic gap; the difference between the historical gap of the flange joint surface and the basic gap is the gap deviation under this set of temperatures and loads.

[0095] Step A4: Construct a sample data set based on the gap deviation under different temperatures and loads.

[0096] In a specific embodiment of the present invention, a sample data set is constructed based on the gap deviation under different temperatures and loads, specifically including:

[0097] Step A4.1: Construct a feature vector for each set of temperature and load.

[0098] This embodiment not only considers the influence of temperature and load, but also the interaction between temperature and load. Therefore, the eigenvector includes the temperature, load and the interaction between temperature and load, which can be expressed as the eigenvector .

[0099] Step A4.2: To eliminate the dimension effect, the eigenvector X is normalized.

[0100] Step A4.3: The normalized feature vector and its gap deviation constitute a sample, thereby obtaining a sample data set.

[0101] Principal component analysis was used to evaluate the contribution of the eigenvectors, and the results showed that the cumulative contribution of the first three principal components reached 97.8%. Although the dimensionality was reduced to three principal components, the original physical characteristics were retained to ensure model interpretability. The standardized 6-dimensional eigenvector was retained as the input to the deep learning model, and the corresponding gap deviation was used as the expected label of the deep learning model.

[0102] The number of samples in the sample dataset constructed based on the historical gaps of the flange joints under different temperatures and loads obtained in step A1 is 500. The sample dataset containing 500 samples is divided into a training set (350), a validation set (100), and a test set (50) in a ratio of 7:2:1. In order to enhance the robustness of the deep learning model, random perturbations (i.e., gap deviations) that conform to engineering practice are added to each basic gap in the training set, thereby expanding the training set to 700 samples.

[0103] Step A5: Deploy a deep learning model and train the deep learning model using the sample dataset to obtain a gap deviation prediction model.

[0104] In this embodiment, a neural network model is used as the deep learning model. The neural network model includes an input layer, a hidden layer, a dropout layer, and an output layer, which are connected in sequence. The number of nodes in the input layer is set according to the dimension of the input feature vector. In this embodiment, the input layer contains 6 nodes. The hidden layer adopts a 2-layer structure, each containing 8 nodes, using the ReLU activation function. The output layer contains 1 node, which is used to output the gap deviation, that is, the correction amount to the basic gap. A dropout layer is added between the hidden layer and the output layer to improve generalization capability.

[0105] In a specific embodiment of the present invention, the neural network model is trained using a training set, specifically including:

[0106] Step A5.1: Input the feature vectors in the training set into the neural network model;

[0107] Step A5.2: The neural network model extracts and predicts the feature vector to obtain the predicted gap deviation;

[0108] Step A5.3: Use the improved LASSO regression loss function to calculate the loss error between the gap deviation corresponding to the feature vector and the predicted gap deviation, adjust the parameters of the neural network model according to the loss error, and implement the training of the neural network model.

[0109] In this example, the neural network model was trained using the Adam optimizer (learning rate 0.001, batch size 64). An early stopping strategy was used; training was terminated after 15 consecutive training rounds without improving the performance of the neural network model. Loss calculation was performed using a modified LASSO regression loss function. This function introduces L2 regularization to the original LASSO regression loss function, better capturing the interactions between influencing factors.

[0110] In a specific embodiment of the present invention, multiple deep learning models (e.g., five) are used. These models have the same structure but different initial weights. The multiple deep learning models are trained, validated, and tested using a sample dataset to generate multiple gap deviation prediction models. In this embodiment, the weights of each deep learning model are initialized using a random seed.

[0111] When there are multiple gap deviation prediction models, the final predicted gap deviation is equal to the weighted average of the output results of multiple gap deviation prediction models. The weight of the output result of each gap deviation prediction model in the weighted average calculation is determined according to the performance of the gap deviation prediction model on the validation set. The better the performance on the validation set, the greater the weight, and the sum of all weights is equal to 1.

[0112] The basic gap physical model (i.e., formula (1) to formula (4)) and the gap deviation prediction model are programmed and packaged to form a software tool for real-time flange connection tightness calculation and solution.

[0113] Example 2

[0114] Figure 3 The flow chart of the flange connection tightness calculation method provided by the present invention is shown. Figure 3 As shown, the flange connection tightness calculation method includes the following steps:

[0115] Step B1: Call the basic gap physical model and gap deviation prediction model.

[0116] Among them, the basic gap physical model (i.e., formula (1) to formula (4)) and the gap deviation prediction model are constructed using the flange connection tightness calculation modeling method of an embodiment of the present application.

[0117] Step B2: Obtain the real-time temperature and real-time load of the flange.

[0118] Taking the wind turbine tower flange as an example, sensors are installed at the flange connection to collect the flange's real-time temperature and load. These sensors include temperature sensors, lateral load sensors, and axial load sensors. Specifically, eight PT100 platinum resistance temperature sensors (accuracy ±0.1°C) are evenly installed around the flange to monitor the flange's temperature distribution; four strain gauge sensors (range 0-120kN, accuracy ±0.5%FS) are evenly installed around the flange to monitor lateral load; and four piezoelectric sensors (range 0-250kN, accuracy ±0.3%FS) are evenly installed around the flange to monitor axial load. The sampling frequency of all sensors is no less than 200Hz, meeting real-time monitoring requirements.

[0119] The real-time temperature is collected by 8 PT100 platinum resistance temperature sensors, and the final real-time temperature is equal to the average value of each real-time temperature; the real-time lateral load is collected by 4 strain gauge sensors, and the final real-time lateral load is equal to the average value of each real-time lateral load; the real-time axial load is collected by 4 piezoelectric sensors, and the final real-time axial load is equal to the average value of each real-time axial load.

[0120] Step B3: Input the real-time temperature and real-time load into the foundation gap physical model to obtain the real-time foundation gap.

[0121] Substitute the final real-time temperature into formula (1) to obtain the flange joint surface clearance caused by temperature; substitute the final real-time lateral load into formula (2) to obtain the flange joint surface clearance caused by lateral load; substitute the final real-time axial load into formula (3) to obtain the flange joint surface clearance caused by axial load; finally, substitute the flange joint surface clearance caused by temperature, the flange joint surface clearance caused by lateral load, and the flange joint surface clearance caused by axial load into formula (4) to obtain the real-time basic clearance.

[0122] Step B4: Preprocess the real-time temperature and real-time load to obtain a real-time feature vector.

[0123] According to the processing method of step A4.1 and step A4.2 in the first embodiment of the present application, the real-time temperature and real-time load are preprocessed to obtain a real-time feature vector.

[0124] Step B5: Input the real-time feature vector into the gap deviation prediction model to obtain the real-time gap deviation.

[0125] When there are multiple gap deviation prediction models, the real-time feature vector is input into each gap deviation prediction model, and then the output results of each gap deviation prediction model are weighted averaged to obtain the final real-time gap deviation.

[0126] Step B6: Calculate the real-time gap of the flange joint surface based on the real-time basic gap and the real-time gap deviation to achieve flange connection tightness calculation.

[0127] The sum of the real-time basic clearance and the final real-time clearance deviation is the real-time clearance of the flange joint surface.

[0128] Step B7: Issue an early warning based on the calculated real-time clearance of the flange joint surface.

[0129] In a specific embodiment of the present invention, issuing an early warning based on the calculated real-time gap between flange joints specifically includes:

[0130] When the real-time gap between the flange joint surfaces is less than the first gap threshold, the flange connection tightness is normal;

[0131] When the first gap threshold ≤ the real-time gap between the flange joint surfaces < the second gap threshold, attention should be paid to the tightness of the flange connection and a warning message should be issued;

[0132] When the second gap threshold ≤ the real-time gap of the flange joint surface < the third gap threshold, a flange connection tightness warning prompt is issued;

[0133] When the real-time gap between the flange joint surfaces is ≥ the third gap threshold, a flange connection tightness danger warning is issued.

[0134] In this embodiment, the first clearance threshold is 0.05mm, the second clearance threshold is 0.08mm, and the third clearance threshold is 0.10mm. The present invention establishes a multi-level early warning mechanism to implement monitoring and early warning. When the real-time clearance between the flange joints exceeds the third clearance threshold, it is considered insufficient tightness. The real-time clearance between the flange joints is calculated every 0.1s, enabling continuous monitoring and calculation of flange connection tightness.

[0135] The calculation method of the present invention was applied to 10 wind turbines in a wind farm and operated for 12 months, achieving significant results: 7 abnormal flange connection conditions were successfully warned, avoiding unexpected equipment shutdowns; the calculation accuracy reached 91.5%, an increase of approximately 55% over traditional methods; equipment availability increased by 2.3%, and annual maintenance costs decreased by 18.7%.

[0136] Compared to traditional methods, the present invention improves monitoring accuracy by over 50% and reduces response time to milliseconds. The calculated results of the joint clearance match actual operating conditions by over 90%. This calculation method has broad applicability and significant application value. It can be appropriately adjusted to suit the characteristics of different industries and application scenarios, effectively improving the safety, reliability, and service life of flange connections in various types of equipment.

[0137] Example 3

[0138] The flange connection tightness calculation system provided by the present invention comprises a data access layer, a calculation layer and a result output layer.

[0139] The data access layer is used to obtain the real-time temperature and real-time load of the flange and transmit the real-time temperature and real-time load to the calculation layer.

[0140] The computing layer deploys a basic gap physical model and a gap deviation prediction model, and is used to input real-time temperature and real-time load into the basic gap physical model to obtain the real-time basic gap; preprocess the real-time temperature and real-time load to obtain a real-time feature vector; input the real-time feature vector into the gap deviation prediction model to obtain the real-time gap deviation; and calculate the real-time gap of the flange mating surface based on the real-time basic gap and real-time gap deviation. The basic gap physical model and gap deviation prediction model are constructed using the flange connection tightness calculation modeling method described in Example 1 of the present application.

[0141] The result output layer is used to display the real-time temperature and real-time load of the flange and the calculated real-time gap of the flange joint surface.

[0142] The flange tightness calculation system of the present invention is built on a cloud-based architecture and adopts a three-tier design: a data access layer, a computing layer, and a result output layer. The data access layer reads and preprocesses sensor data; the computing layer calculates the flange joint clearance based on the sensor data; and the result output layer visualizes the calculation results. The flange tightness calculation system uses C++ to develop its core algorithm and Python to develop its interface layer, supporting multi-threaded parallel computing.

[0143] An edge computing unit is deployed at the data access layer. Located at the base of the tower, it performs preliminary filtering on sensor data, removing significant outliers. This data is then encrypted and transmitted to the computing layer via a 4G network. The average data transmission latency is 32ms, meeting real-time requirements. A MySQL database is also used to store sensor data. In this embodiment, a Kalman filter is used for preliminary filtering to effectively reduce signal noise. Outliers exceeding three standard deviations are automatically identified and removed to ensure data reliability.

[0144] At the computing layer, a basic gap physics model (i.e., equations (1) to (4)) and a gap deviation prediction model are deployed. The Intel MKL matrix operation library is applied to optimize the calculation process, increasing the speed of large matrix operations by approximately 2.8 times. Tests show that the delay of a single calculation is controlled within 125ms, meeting the needs of real-time monitoring. To ensure computing reliability, a redundancy mechanism is implemented at the computing layer. When the main computing unit fails, the backup computing unit automatically takes over the computing task. A complete exception handling process is designed to automatically identify abnormal input data and take compensatory measures. The monitoring platform performs self-calibration every 24 hours to ensure long-term stable operation.

[0145] In the result output layer, a web interface is designed to display the temperature, load and calculated flange joint surface gap in real time, supporting PC and mobile terminal access.

[0146] The calculation system also incorporates a rationality check mechanism. If the rate of change in a calculation result exceeds three times that of historical data, a secondary verification process is triggered. Verification testing showed that this mechanism successfully identified 98.5% of calculation anomalies, effectively improving system reliability.

[0147] With respect to software architecture, the system of the present invention adopts a modular design, supports multi-threaded parallel computing, and improves processing efficiency; implements a model hot update mechanism and supports online optimization and adjustment; and standardizes the design interface to facilitate integration with other systems. With respect to algorithm optimization and acceleration, the present invention applies a matrix operation library to optimize the calculation process; introduces GPU acceleration features to increase the calculation speed of the neural network model; implements an incremental calculation scheme to only process changing data; and controls the calculation delay to within 200ms to meet real-time requirements. With respect to reliability, the present invention adopts a redundancy mechanism to prevent single point failures; designs an exception handling process to ensure stable operation of the system; establishes regular self-test and self-calibration functions; and supports offline calculation mode to cope with network interruptions.

[0148] Example 4

[0149] An embodiment of the present invention also provides an electronic device, which includes: a memory, a processor, and a computer program / instructions stored on the memory, and the processor executes the computer program / instructions to implement the flange connection tightness calculation modeling method or the flange connection tightness calculation method in the embodiment of the present application.

[0150] Although not shown, the electronic device includes a processor that can perform various appropriate operations and processes based on programs and / or data stored in a read-only memory (ROM) or programs and / or data loaded from a storage portion into a random access memory (RAM). The processor can be a multi-core processor or can include multiple processors. In some embodiments, the processor can include a general-purpose main processor and one or more special coprocessors, such as a central processing unit, a graphics processing unit (GPU), a neural network processor (NPU), a digital signal processor (DSP), etc. Various programs and data required for device operation are also stored in the RAM. The processor, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0151] The processor and memory are used together to execute the program / instructions stored in the memory. When the program / instructions are executed by the computer, the methods, steps or functions described in the above embodiments can be implemented.

[0152] Although not shown, an embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the flange connection tightness calculation modeling method or flange connection tightness calculation method in the embodiment of the present application.

[0153] Computer-readable storage media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0154] Although not shown, an embodiment of the present invention further provides a computer program product, including: a computer program / instruction, which, when executed by a processor, implements the flange connection tightness calculation modeling method or flange connection tightness calculation method in the embodiment of the present application.

[0155] The above disclosure is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or modifications within the technical scope disclosed in the present invention, and they should all be covered by the scope of protection of the present invention.

Claims

1. A flange connection tightness calculation modeling method, characterized in that: The modeling method comprises: Obtain historical clearance of flange joints under different temperatures and loads; Construct a basic gap physical model based on the historical gap of flange joint surfaces under different temperatures and loads; Calculating the gap deviation under different temperatures and loads based on the basic gap physical model and the historical gaps of the flange joint surfaces under different temperatures and loads; Construct a sample data set based on gap deviations at different temperatures and loads; Deploy a deep learning model and train the deep learning model using the sample data set to obtain a gap deviation prediction model.

2. The flange connection tightness calculation modeling method according to claim 1 is characterized in that: The basic gap physical model is constructed based on the historical gap of the flange joint surface under different temperatures and loads, including: Constructing a flange finite element model; wherein the flange finite element model includes an upper flange, a lower flange, and bolts connecting the upper flange and the lower flange, and the gap between the upper flange and the lower flange is the flange joint surface gap; Based on the flange finite element model, simulation calculations are performed at different temperatures to obtain simulated gaps of the flange joint surface at different temperatures; the simulated gaps of the flange joint surface at different temperatures are fitted to obtain a temperature-joint surface gap fitting function; Based on the flange finite element model, simulation calculations are performed under different loads to obtain simulated clearances of the flange joint surface under different loads; the simulated clearances of the flange joint surface under different loads are fitted to obtain a load-joint surface clearance fitting function; The flange joint surface gap caused by temperature and the flange joint surface gap caused by load are calculated based on the historical gap of the flange joint surface under different temperatures and loads, as well as the temperature-joint surface gap fitting function and the load-joint surface gap fitting function; According to the flange joint surface gap caused by temperature, the flange joint surface gap caused by load and the flange joint surface historical gap, the basic gap physical model is obtained.

3. The flange connection tightness calculation modeling method according to claim 1, characterized in that: A sample data set was constructed based on gap deviations under different temperatures and loads, including: Constructing a characteristic vector according to each set of temperature and load; wherein the characteristic vector includes the temperature, the load, and the interaction value between the temperature and the load; performing normalization processing on the feature vector; The standardized feature vector and its gap deviation constitute a sample, and then a sample data set is obtained.

4. The flange connection tightness calculation modeling method according to claim 1, characterized in that: There are multiple deep learning models, each of which has the same structure but different initial weights, and each of the multiple deep learning models is trained using the sample data set; When each gap deviation prediction model is applied to perform real-time calculation, the final real-time gap deviation is equal to the weighted average of the real-time gap deviations output by each gap deviation prediction model.

5. The flange connection tightness calculation modeling method according to any one of claims 1 to 4, characterized in that: Training the deep learning model using the sample data set specifically includes: Inputting the feature vectors in the sample data set into a deep learning model; The deep learning model extracts and predicts the feature vector to obtain a predicted gap deviation; The improved LASSO regression loss function is used to calculate the loss error between the gap deviation corresponding to the feature vector and the predicted gap deviation. The parameters of the deep learning model are adjusted according to the loss error to realize the training of the deep learning model. Among them, the improved LASSO regression loss function introduces L2 regularization on the basis of the original LASSO regression loss function.

6. A method for calculating the tightness of flange connection, characterized in that: The calculation method includes: Calling a basic gap physical model and a gap deviation prediction model; wherein the basic gap physical model and the gap deviation prediction model are constructed using the flange connection tightness calculation modeling method according to any one of claims 1 to 5; Get the real-time temperature and load of the flange; Inputting the real-time temperature and real-time load into a foundation gap physical model to obtain a real-time foundation gap; Preprocessing the real-time temperature and real-time load to obtain a real-time feature vector; Inputting the real-time feature vector into a gap deviation prediction model to obtain a real-time gap deviation; The real-time gap of the flange joint surface is calculated according to the real-time basic gap and the real-time gap deviation.

7. The flange connection tightness calculation method according to claim 6, characterized in that: The calculation method also includes providing an early warning based on the calculated real-time clearance of the flange joint surface, specifically including: When the real-time gap between the flange joint surfaces is less than the first gap threshold, the flange connection tightness is normal; When the first gap threshold ≤ the real-time gap of the flange joint surface < the second gap threshold, a flange connection tightness attention prompt is issued; When the second gap threshold ≤ the real-time gap of the flange joint surface < the third gap threshold, a flange connection tightness warning prompt is issued; When the real-time gap between the flange joint surfaces is ≥ the third gap threshold, a flange connection tightness danger warning is issued.

8. A flange connection tightness calculation system, characterized in that: The computing system includes a data access layer, a computing layer and a result output layer; The data access layer is used to obtain the real-time temperature and real-time load of the flange and transmit the real-time temperature and real-time load to the calculation layer; The computing layer is deployed with a basic gap physical model and a gap deviation prediction model, and is used to input the real-time temperature and real-time load into the basic gap physical model to obtain a real-time basic gap; preprocess the real-time temperature and real-time load to obtain a real-time feature vector; input the real-time feature vector into the gap deviation prediction model to obtain a real-time gap deviation; calculate the real-time gap of the flange joint surface based on the real-time basic gap and the real-time gap deviation; wherein the basic gap physical model and the gap deviation prediction model are constructed using the flange connection tightness calculation modeling method according to any one of claims 1 to 5; The result output layer is used to display the real-time temperature and real-time load of the flange and the calculated real-time gap of the flange joint surface.

9. An electronic device comprising a memory, a processor, and a computer program / instruction stored in the memory, characterized in that: The processor executes the computer program / instructions to implement the flange connection tightness calculation modeling method according to any one of claims 1 to 5, or the flange connection tightness calculation method according to claim 6 or 7.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the flange connection tightness calculation modeling method according to any one of claims 1 to 5 or the flange connection tightness calculation method according to claim 6 or 7 is implemented.

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