Thermal power plant pipeline expansion joint dynamic stress distribution optimization design method and system

Abstract

The invention relates to the technical field of thermal power plant pipeline design, and discloses a thermal power plant pipeline expansion joint dynamic stress distribution optimization design method and system, and the method comprises the steps: monitoring the real-time data of an expansion joint through a sensor network; based on the real-time data, constructing a finite element analysis model, and generating stress analysis results of the expansion joint under different working conditions; the stress analysis result is compared with a preset safety threshold value, and whether design parameters need to be adjusted or not is judged according to the comparison result; according to a comparison result, a calculation feedback control algorithm is adopted to dynamically adjust the design parameters; and the real-time data and the dynamically adjusted data are subjected to a self-learning intelligent diagnosis technology and are combined with historical data to predict potential faults. According to the method, the learning intelligent diagnosis technology is adopted, potential faults can be predicted according to historical data, the effect of making a maintenance plan in advance is achieved, compared with lagging maintenance in the prior art, the maintenance cost can be reduced, meanwhile, the downtime is shortened, and guarantee is provided for long-term stable operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power plant pipeline design, in particular to a power plant pipeline expansion joint dynamic stress distribution optimization design method and system. BACKGROUND

[0002] As the main energy production facility, power plants convert chemical energy into electrical energy through coal, oil or gas burning, and its operation efficiency and safety directly affect the stability of power supply. The overall design and management of power plants involve multiple systems, among which the pipeline system plays a crucial role in transmitting fluids such as steam and cooling water. The pipeline system needs to withstand high temperature and high pressure working conditions, so its design, material selection and maintenance are crucial to ensure safety and efficiency. In the pipeline system of power plants, the pipeline expansion joint is a key component. Due to thermal expansion and contraction caused by temperature changes, the pipeline expansion joint is used to absorb displacement changes in the pipeline system, effectively preventing pipeline damage and failure caused by thermal stress. The design of the expansion joint must take into account various operating conditions and load cases to maintain the stability and reliability of the pipeline.

[0003] The stress distribution of the expansion joint is an important indicator for evaluating its performance and safety. Stress concentration can lead to material fatigue, cracking and even rupture, which poses a threat to the safe operation of power plants. Therefore, accurately identifying and analyzing the stress state of the expansion joint under different operating conditions is crucial for optimizing its design and extending its service life. Through reasonable design and analysis methods, the failure risk of the expansion joint can be effectively reduced to ensure the safe and stable operation of the power plant.

[0004] In the usual use process, maintenance plans usually rely on the lagging response after equipment failure, resulting in increased maintenance costs and downtime after equipment failure. SUMMARY

[0005] In view of the shortcomings of the prior art, the present application provides a power plant pipeline expansion joint dynamic stress distribution optimization design method and system, which solves the problem that maintenance plans usually rely on the lagging response after equipment failure, resulting in increased maintenance costs and downtime after equipment failure.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme:

[0007] The power plant pipeline expansion joint dynamic stress distribution optimization design method comprises the following steps:

[0008] The real-time data of the expansion joint is monitored by a sensor network;

[0009] Based on the real-time data, a finite element analysis model is constructed, and stress analysis results of the expansion joint under different operating conditions are generated;

[0010] The stress analysis result is compared with a preset safety threshold value, and whether the design parameter needs to be adjusted is judged according to the comparison result;

[0011] According to the comparison result, a calculation feedback control algorithm is used to dynamically adjust the design parameter;

[0012] Real-time data and dynamically adjusted data are used to predict potential faults by using self-learning intelligent diagnosis technology combined with historical data, and maintenance suggestions are generated.

[0013] The further improvement of the present application is that the real-time data includes temperature, pressure and strain data of the expansion joint, and the strain data includes radial strain, axial strain and tangential strain.

[0014] The further improvement of the present application is that the finite element analysis model is established by using CAD software to establish a three-dimensional model of the expansion joint, and temperature boundary and pressure boundary are defined, and the stress analysis result includes the numerical value of the radial stress, axial stress and tangential stress of the expansion joint under the conditions of high temperature, high pressure and instantaneous load change, and also includes the stress concentration area under the changing working condition and the fatigue stress evaluation.

[0015] The further improvement of the present application is that the comparison of the stress analysis result with the preset safety threshold value is achieved by comparing the stress analysis result of the expansion joint calculated under different working conditions with the preset safety threshold value item by item, including checking whether the radial stress, axial stress and tangential stress exceed the corresponding safety limit value, and if any stress value exceeds the safety threshold value, it is judged that the design parameter needs to be adjusted.

[0016] The further improvement of the present application is that the specific steps of the calculation feedback control algorithm are as follows:

[0017] The difference between the current stress analysis result and the preset safety threshold value is calculated by the calculation feedback control algorithm, and the specific stress exceeding range area is identified;

[0018] The design parameter is adjusted by using an optimization algorithm combined with the actual working condition and material characteristics;

[0019] The material characteristics include the shape and material thickness of the expansion joint.

[0020] The further improvement of the present application is that the adjustment of the design parameter includes increasing or decreasing the wall thickness of the expansion joint and changing the geometric shape or support scheme thereof.

[0021] The further improvement of the present application is that the specific steps of using self-learning intelligent diagnosis technology combined with historical data to predict potential faults and generate maintenance suggestions are as follows:

[0022] Collect historical data to analyze potential failure modes and build a dataset;

[0023] Train the dataset using machine learning algorithms to build a failure prediction model;

[0024] Predict potential failures through the failure prediction model and generate maintenance recommendations.

[0025] Further improvements of the present application are that the historical data includes historical stress data, failure records and maintenance logs, and the failure modes include excessive strain and material fatigue.

[0026] Further improvements of the present application are that the maintenance recommendations include regular inspection, replacement, lubrication and cleaning of vulnerable components.

[0027] The power plant pipe expansion joint dynamic stress distribution optimization design system is based on the power plant pipe expansion joint dynamic stress distribution optimization design method, and includes the following modules:

[0028] A real-time data monitoring module for monitoring real-time data of the expansion joint;

[0029] A finite element analysis model construction module for generating stress analysis results of the expansion joint under different working conditions;

[0030] A stress analysis result comparison module for comparing the stress analysis results with a preset safety threshold and determining whether the design parameters need to be adjusted according to the comparison results;

[0031] A calculation feedback control algorithm module for calculating the difference between the current stress analysis results and the preset safety threshold and adjusting the design parameters according to the actual working conditions and material characteristics;

[0032] A dynamic adjustment module for dynamically adjusting the design parameters according to the optimization algorithm;

[0033] A failure prediction and maintenance recommendation module for analyzing potential failure modes and generating maintenance recommendations.

[0034] Compared with the prior art, the present application has at least the following beneficial technical effects:

[0035] By using learning intelligent diagnosis technology, the present application can predict potential failures according to historical data, achieve the effect of formulating maintenance plans in advance, reduce maintenance costs compared with the lagging maintenance in the prior art, reduce downtime, and provide protection for long-term stable operation.

[0036] The present application can make engineers deeply understand stress distribution by using simulation and finite element analysis technology to analyze stress of the expansion joint under different working conditions, which not only provides the basis for design optimization, but also effectively improves the toughness and durability of the expansion joint structure, and ensures safe operation under various environmental conditions.

[0037] The present application realizes comprehensive evaluation of the expansion joint performance by multi-condition simulation combined with real-time monitoring data, improves design reliability and durability, ensures stable operation of the equipment under various working conditions, and reduces the risk of failure.

[0038] The present application realizes adaptive design of the expansion joint by calculating feedback control combined with dynamic design parameter optimization and safety evaluation, achieves the effect of improving its adaptability and overall performance, and ensures the stability of the equipment under various complex operating environments. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the specific embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0040] Figure 1 The method flow chart of the dynamic stress distribution optimization design method for the expansion joint of the pipeline of the thermal power plant of the present application is shown in the figure.

[0041] Figure 2 The structural framework diagram of the dynamic stress distribution optimization design system for the expansion joint of the pipeline of the thermal power plant of the present application is shown in the figure. DETAILED DESCRIPTION

[0042] In the following, only some exemplary embodiments are simply described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present application. Therefore, the drawings and the description are considered to be exemplary in nature rather than limiting.

[0043] In the description of the present application, it should be understood that when used in the specification and the appended claims, the terms "comprise" and "include" indicate the presence of described features, whole, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0044] It should also be understood that the terms used in the specification of the present application are only for the purpose of describing particular embodiments and are not intended to limit the present application. As used in the specification and the appended claims of the present application, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0045] It should be further understood that the term "and / or" used in the specification and the appended claims of the present application means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0046] Various structural diagrams according to the disclosed embodiments of the present application are shown in the accompanying drawings. These drawings are not drawn to scale, in which certain details are exaggerated for the purpose of clarity and certain details can be omitted. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are only exemplary, and in actuality can deviate due to manufacturing tolerances or technical limitations, and regions / layers with different shapes, sizes and relative positions can be additionally designed by those skilled in the art according to actual needs.

[0047] The embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0048] Embodiment 1

[0049] Please refer to the accompanying drawings Figure 1 The power plant pipeline expansion joint dynamic stress distribution optimization design method provided by the present application comprises the following steps:

[0050] The real-time data of the expansion joint is monitored through a sensor network;

[0051] Based on the real-time data, a finite element analysis model is constructed, and stress analysis results of the expansion joint under different working conditions are generated;

[0052] The stress analysis results are compared with the preset safety threshold, and whether the design parameters need to be adjusted is determined according to the comparison results;

[0053] According to the comparison results, a calculation feedback control algorithm is used to dynamically adjust the design parameters;

[0054] The real-time data and the dynamically adjusted data are used to predict potential failures by using self-learning intelligent diagnosis technology and combining historical data, and maintenance suggestions are generated.

[0055] Specifically, by monitoring the real-time data of the expansion joint, the temperature, pressure and strain of the expansion joint and other key parameters can be comprehensively understood, so that potential operation problems can be identified in a timely manner, and the safety and reliability of the expansion joint can be improved;

[0056] Based on real-time data, a finite element analysis model is constructed, and stress analysis results of the expansion joint under different working conditions are generated, enabling engineers to deeply analyze the stress distribution of the expansion joint under various operating conditions, helping to make reasonable design decisions, thereby enhancing the strength and durability of the structure;

[0057] The stress analysis results are compared with the preset safety threshold, and whether the design parameters need to be adjusted is determined according to the comparison results, ensuring the safety during the design process, keeping the equipment running within a safe range, reducing the risk of failure and accidents, and helping to protect the safety of the equipment and its surrounding environment;

[0058] According to the comparison results, a calculation feedback control algorithm is used to dynamically adjust the design parameters, which can timely respond to changes in operating conditions through real-time adjustment, thereby optimizing the design and improving the performance and service life of the equipment;

[0059] Real-time data and dynamically adjusted data are combined with self-learning intelligent diagnosis technology to predict potential failures using historical data and generate maintenance recommendations, which can effectively identify fault risks and take maintenance measures in advance to ensure long-term stable operation of the expansion joint while reducing maintenance costs and downtime.

[0060] Real-time data includes temperature, pressure and strain data of the expansion joint, and strain data includes radial strain, axial strain and tangential strain.

[0061] Specifically, real-time data includes temperature, pressure and strain data of the expansion joint, and strain data is subdivided into radial strain, axial strain and tangential strain. The monitoring of these data helps to comprehensively evaluate the working state of the expansion joint. Real-time monitoring of temperature and pressure can provide basic information for safe operation. At the same time, the detailed collection of radial strain, axial strain and tangential strain enables engineers to accurately analyze the stress state of the expansion joint under different loads. Real-time monitoring data is also crucial for subsequent fault diagnosis and maintenance recommendations, helping to ensure effective operation of the expansion joint and reduce the risk of failure.

[0062] The finite element analysis model is constructed by using CAD software to establish a three-dimensional model of the expansion joint and defining temperature boundaries and pressure boundaries. The stress analysis results include the numerical values of the radial stress, axial stress and tangential stress of the expansion joint under high temperature, high pressure and transient load changes, as well as the stress concentration areas and fatigue stress evaluation under varying working conditions.

[0063] Specifically, in constructing the finite element analysis model of the expansion joint, first, according to the design data and material performance parameters, the data usually includes the geometric dimensions of the expansion joint, such as length, diameter, wall thickness and the geometric shape of the connecting parts, these geometric data are input through engineering drawings or CAD model to ensure that the three-dimensional model truly reflects the actual structure, at the same time, the physical and mechanical properties of the materials used in the expansion joint need to be input, such as elastic modulus, Poisson's ratio, yield strength and thermal expansion coefficient, etc., and the temperature boundary and pressure boundary conditions of the model need to be defined to accurately simulate the actual working environment, for example, the temperature boundary can be set according to the temperature of the working medium, and the pressure boundary should reflect the maximum working pressure of the expansion joint, after determining the geometry and material properties of the model, a three-dimensional model is established through CAD software and imported into the finite element analysis software for further analysis, during the stress analysis process, engineers can understand the performance of the expansion joint under high temperature, high pressure and instantaneous load change conditions according to the stress calculation, such as axial stress, radial stress and tangential stress, so that engineers can understand the stress distribution of the expansion joint under different working conditions, including the numerical values of radial stress, axial stress and tangential stress, the stress analysis results also reveal the stress concentration area and fatigue stress evaluation under varying working conditions, providing information for engineers to make targeted design optimization decisions, thereby improving the safety and durability of the overall structure and ensuring the stability and reliability of the expansion joint in actual operation.

[0064] The stress analysis results are compared with the preset safety threshold by comparing the stress analysis results of the expansion joint calculated under different working conditions with the preset safety threshold item by item, including checking whether the radial stress, axial stress and tangential stress exceed the corresponding safety limit, if any stress value is found to exceed the safety threshold, it is judged that the design parameters need to be adjusted.

[0065] Specifically, the stress analysis results of the expansion joint calculated under different working conditions are compared with the preset safety threshold item by item to ensure the safety of the expansion joint, the radial stress, axial stress and tangential stress obtained from the analysis results are compared with the defined safety limit one by one, for example, assuming that the preset safety limit is: the radial stress does not exceed 15 MPa, the axial stress does not exceed 20 MPa, and the tangential stress does not exceed 10 MPa, during the comparison process, if the calculation result shows that a certain stress, for example, the tangential stress reaches 12 MPa, will exceed the safety threshold, this potential risk can be identified in time, if any stress value is found to exceed the safety threshold, the engineer will judge that the design parameters need to be adjusted, so that not only can the damage caused by stress overlimit during operation of the equipment be prevented, but also the overall structural safety of the equipment can be effectively maintained to ensure that all stresses are within the safety range, thereby improving the stability and reliability of the equipment under various working conditions.

[0066] The specific steps of the calculation feedback control algorithm are as follows:

[0067] The difference between the current stress analysis result and the preset safety threshold is calculated by a computational feedback control algorithm to identify specific areas where the stress exceeds the range;

[0068] By combining actual working conditions and material properties, the design parameters are adjusted through algorithm optimization;

[0069] Material properties include the shape of the expansion joint and the material thickness.

[0070] Specifically, the difference between the current stress analysis result and the preset safety threshold is calculated. The stress difference is calculated using the following formula:

[0071] Δσ=σ current -σ threshold ;

[0072] Where Δσ represents the stress difference, σ current The stress value obtained from the current calculation, σ threshold The preset safety threshold is used to identify specific stress values ​​that exceed the safety range. For example, the currently calculated tangential stress is σ. current =18MPa, preset safety threshold is σ threshold =15MPa, then the stress difference is 18MPa - 15MPa = 3MPa. By analyzing the calculated stress difference, areas where the stress exceeds the range are identified. In this case, Δσ>0, and these areas are marked as areas requiring adjustment. This process ensures that potential structural safety hazards can be quickly located. Then, combined with the actual working conditions and the material properties of the expansion joint, such as shape and material thickness, the design parameters are optimized. The material properties must be obtained in the design stage, including the tensile strength and yield strength of the material. Based on the identified out-of-range areas, the design parameters are adjusted through optimization algorithms, modifying the material thickness t and geometry so that the stress no longer exceeds the preset safety threshold. The new design parameters are calculated using the following formula:

[0073] t new =t current +k·Δσ;

[0074] Among them, t ncw t represents the thickness of the new material. current Let t be the current material thickness, and k be an adjustment coefficient related to material properties. Assume the current material thickness is t. current =10mm, adjustment coefficient k=0.2, based on the stress difference calculated by the formula, Δσ>0, then the new material thickness is calculated as follows: t new =10mm + 0.2×3 = 10 + 0.6 = 10.6mm, ensuring that stress is reduced by increasing the material thickness and keeping the expansion joint operating within a safe range.

[0075] Adjusting the design parameters includes increasing or decreasing the wall thickness of the expansion joint and changing its geometry or support scheme.

[0076] Specifically, the step of adjusting the geometry or support scheme analyzes the geometry of the expansion joint according to the identified stress problem, determines whether there is a design defect, appropriately changes the geometry to improve the uniformity of stress distribution, such as adding rib beams or optimizing the connection structure, confirms whether the new design meets the stress analysis requirements, and verifies, for example, if the stress at a specific location is found to be too high in stress analysis, the stress can be dispersed by introducing rib beams as a support scheme to reduce the load at that location, and by increasing or decreasing the wall thickness and changing the geometry, the stress distribution should be effectively optimized to ensure that the expansion joint meets the safety and stability requirements in actual operation. This process is carried out during the design stage, which helps to improve the fatigue resistance and service life of the overall structure.

[0077] The specific steps of using self-learning intelligent diagnosis technology to combine real-time data with dynamically adjusted data and historical data to predict potential failures and generate maintenance recommendations are as follows:

[0078] Collect historical data to analyze potential failure modes and build a data set;

[0079] Use machine learning algorithms to train the data set to build a failure prediction model;

[0080] Predict potential failures through the failure prediction model and generate maintenance recommendations.

[0081] Specifically, first, collect historical operating data of the expansion joint under different working conditions, including temperature, pressure, strain and other parameters, to build a data set. During the process of building the data set, data preprocessing is required to ensure data quality and accuracy. Preprocessing includes missing value filling, noise removal and data standardization. Missing values are processed by linear interpolation method, using the following formula:

[0082]

[0083] Where X filed is the filled value, X prev and X next are the observation values before and after the missing value, n is the number of missing values, and k is the position of the missing value in the time series. For example, if the previous observation value is 80 and the next one is 100, and the missing value is in the middle, the filled value is 90. Noise removal uses the moving average method, using the following formula:

[0084]

[0085] Where X smolohed is the denoised data, N is the size of the moving window, and Xi is the current and previous data points, for example, if five observation values are [10, 12, 14, 13, 11], the mean after denoising is 12, and the standardization is calculated using the following formula:

[0086]

[0087] where μ is the mean of the data, σ is the standard deviation of the data, for example, there is an observation value 14, the data set is [10, 12, 14, 16, 18], the mean is 14, and the standard deviation is 2, so that the standardization result is 0, after completing the data preprocessing, the machine learning algorithm is used to train the data set to build a fault prediction model, and the posterior probability of the model is calculated by the following Bayes formula:

[0088]

[0089] where P(y|X) represents the posterior probability of fault y given the feature X, P(X|y) is the conditional probability of feature X under the condition of fault, P(y) is the prior probability of fault occurrence, and P(X) is the marginal probability of the feature, for example, if the prior probability of fault occurrence is 10%, the probability of feature occurrence under the condition of fault is 80%, and the marginal probability of the feature is 50%, then we can get:

[0090]

[0091] Thus, the fault risk is 16%, after building the fault prediction model, the engineer can develop a corresponding maintenance plan or adjustment strategy for potential fault conditions, when the fault risk exceeds the set threshold, the system will issue an alarm, according to the fault prediction result, the engineer can evaluate the factors that may affect the performance and safety of the equipment, so as to optimize the operating conditions or carry out preventive maintenance.

[0092] Historical data includes historical stress data, fault records and maintenance logs, and fault modes include excessive strain and material fatigue.

[0093] Specifically, the collection of historical data includes historical stress data, fault records and maintenance logs, which provide the basis for potential fault prediction, by analyzing historical stress data, the performance changes of materials under different working conditions can be identified, and the basis for confirming fault modes such as excessive strain and material fatigue can be provided, fault records record the past fault conditions, helping technicians understand the frequency and causes of faults, at the same time, maintenance logs provide specific information of past maintenance operations, which can guide the improvement of maintenance plans and processes.

[0094] Maintenance recommendations include regular inspection, replacement, lubrication and cleaning of vulnerable components.

[0095] Specifically, the maintenance recommendations include regular inspection, replacement, lubrication and cleaning of vulnerable parts, aiming to prolong the service life of the equipment and reduce the risk of failure, regular inspection can timely find the abnormality of the equipment operation state, provide the basis for early identification of potential failure, replacement of vulnerable parts can prevent system downtime or safety hazards caused by component failure, ensure normal operation of each component, lubrication can reduce friction between components and reduce wear and tear, thereby prolonging the service life of the components, at the same time, cleaning can remove accumulated dirt and impurities to prevent their impact on equipment performance, the implementation of the maintenance recommendations can be quantified by the following formula:

[0096] R=Cx(1-(MTTR / MTBF));

[0097] Wherein, R represents the reliability of the equipment, C is the initial cost of the equipment, MTTR represents the average repair time, and MTBF is the average failure-free operation time, by maintaining a high equipment reliability, the maintenance cost and downtime can be effectively reduced, for example, the initial cost of a certain equipment is 100,000 yuan, the average repair time is 5 hours, and the average failure-free operation time is 100 hours, then the reliability can be calculated as: Indicating that the equipment reliability can reach the level of 95000 yuan, thereby realizing the normal operation of the equipment, that is, the probability of failure can be reduced, and the stability of the equipment in the running process can be ensured.

[0098] Embodiment 2

[0099] Please refer to the attached Figure 2 The power plant pipeline expansion joint dynamic stress distribution optimization design system provided by the application comprises:

[0100] A real-time data monitoring module is configured to monitor real-time data of the expansion joint.

[0101] A finite element analysis model construction module is configured to generate stress analysis results of the expansion joint under different working conditions.

[0102] A stress analysis result comparison module is configured to compare the stress analysis results with preset safety thresholds and determine whether the design parameters need to be adjusted according to the comparison results.

[0103] A calculation feedback control algorithm module is configured to calculate the difference between the current stress analysis results and the preset safety thresholds and adjust the design parameters according to the actual working conditions and material characteristics.

[0104] A dynamic adjustment module is configured to dynamically adjust the design parameters according to the optimization algorithm.

[0105] A failure prediction and maintenance recommendation module for analyzing potential failure modes and generating maintenance recommendations.

[0106] Specifically, the real-time data monitoring module is responsible for collecting real-time operational state data of the expansion joint, including temperature, pressure, and strain parameters. These real-time data provide basic information for subsequent stress analysis and failure prediction, ensuring that the system can respond to changes in operating conditions in a timely manner.

[0107] The finite element analysis model construction module is used to generate stress analysis results of the expansion joint under different operating conditions based on real-time data and existing structural parameters. Through finite element analysis methods, the complex stress state under actual working conditions is represented by mathematical models, providing quantitative basis for subsequent design optimization and failure analysis.

[0108] The stress analysis result comparison module compares the finite element analysis results with the preset safety threshold to determine the safety of the expansion joint under operating conditions. Based on the comparison results, the system can determine whether to adjust the design parameters to ensure the safe operation of the expansion joint under high stress conditions.

[0109] The calculation feedback control algorithm module calculates the difference between the current stress analysis results and the preset safety threshold, and dynamically adjusts the design parameters using model algorithms based on specific actual operating conditions and material characteristics. The purpose is to timely correct potential safety hazards and ensure that the design continuously meets safety standards.

[0110] The dynamic adjustment module adjusts the design parameters in real time based on the above feedback and optimization algorithms to adapt to changing operating environments. Through dynamic adjustment, the system can effectively optimize the performance of the expansion joint, reduce the risk of failure, and improve the reliability of the equipment.

[0111] The failure prediction and maintenance recommendation module analyzes potential failure modes, combines historical data, stress analysis results, and monitoring data to generate corresponding maintenance recommendations. By guiding maintenance decisions, the system can reduce the probability of failure and provide support for the normal operation of the equipment.

[0112] In summary, through the coordinated action of various modules, the system can achieve multi-level monitoring, analysis, and optimization of the expansion joint, improving the safety and reliability of equipment operation.

[0113] The foregoing merely illustrates the principles of the application and application of its leading features. This application is not limited to the exact details shown above and described herein, and obvious modifications will occur to those skilled in the art upon reading the foregoing description. Therefore, the scope of the application is not to be determined by the specific examples shown above, but only by the claims below. Any reference signs in the claims should not be construed as limiting the scope of the claims.

[0114] Furthermore, it should be understood that although the description above relates to embodiments, not every embodiment contains only one independent technical solution, and the description above is only for the sake of clarity, and those skilled in the art should understand the description as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that those skilled in the art can understand. The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application. Any modification made on the basis of the technical idea of the present application, which is within the scope of the technical solutions, falls within the protection scope of the claims of the present application.

Claims

1. A method for optimizing the dynamic stress distribution of a power plant pipe expansion joint, characterized in that, The method comprises the following steps: monitoring real-time data of the expansion joint through a sensor network; constructing a finite element analysis model based on the real-time data and generating stress analysis results of the expansion joint under different working conditions; comparing the stress analysis results with preset safety thresholds and determining whether the design parameters need to be adjusted according to the comparison results; dynamically adjusting the design parameters by using a calculation feedback control algorithm according to the comparison results; predicting potential faults by using a self-learning intelligent diagnosis technology and combining historical data with the real-time data and the dynamically adjusted data, and generating maintenance suggestions.

2. The method for dynamic stress distribution optimization design of a power plant piping expansion joint according to claim 1, characterized in that, The real-time data includes temperature, pressure and strain data of the expansion joint, and the strain data includes radial strain, axial strain and tangential strain.

3. The method for dynamic stress distribution optimization design of power plant piping expansion joint according to claim 1, characterized in that, The three-dimensional model of the expansion joint is established by using a CAD software, and temperature boundaries and pressure boundaries are defined, and the stress analysis results include numerical values of radial stress, axial stress and tangential stress of the expansion joint under high temperature, high pressure and instantaneous load change conditions, and also include stress concentration areas and fatigue stress evaluation under varying working conditions.

4. The method for dynamic stress distribution optimization design of power plant piping expansion joint according to claim 1, characterized in that, The comparison of the stress analysis results with the preset safety thresholds is performed by comparing the stress analysis results of the expansion joint under different working conditions with the preset safety thresholds item by item, including checking whether the radial stress, axial stress and tangential stress exceed the corresponding safety limits, and if any stress value exceeds the safety threshold, it is determined that the design parameters need to be adjusted.

5. The method for dynamic stress distribution optimization design of power plant piping expansion joint according to claim 1, characterized in that, The specific steps of using the calculation feedback control algorithm are as follows: calculating the difference between the current stress analysis results and the preset safety thresholds by using the calculation feedback control algorithm to identify the specific stress exceeding range; adjusting the design parameters by using an optimization algorithm in combination with actual working conditions and material characteristics. The material characteristics include the shape and material thickness of the expansion joint.

6. The method for dynamic stress distribution optimization design of power plant piping expansion joint according to claim 5, characterized in that, The adjustment of the design parameters includes increasing or decreasing the wall thickness of the expansion joint and changing the geometric shape or support scheme thereof.

7. The method for dynamic stress distribution optimization design of power plant piping expansion joint according to claim 1, characterized in that, The specific steps of using the self-learning intelligent diagnosis technology and combining historical data to predict potential faults and generate maintenance suggestions are as follows: collecting historical data to analyze potential fault modes and constructing a data set; training the data set by using a machine learning algorithm to construct a fault prediction model; predicting potential faults by using the fault prediction model and generating maintenance suggestions.

8. The method for dynamic stress distribution optimization design of power plant piping expansion joint according to claim 7, characterized in that, The historical data include historical stress data, fault records and maintenance logs, and the fault modes include excessive strain and material fatigue.

9. The method for dynamic stress distribution optimization design of power plant piping expansion joint according to claim 6, characterized in that, The maintenance suggestions include regular inspection, replacement, lubrication and cleaning of vulnerable components.

10. A system for dynamic stress distribution optimization design of a power plant piping expansion joint, characterized by, The method for dynamically optimizing stress distribution of a pipeline expansion joint in a thermal power plant according to any one of claims 1-9 comprises the following modules: a real-time data monitoring module for monitoring real-time data of the expansion joint; a finite element analysis model construction module for generating stress analysis results of the expansion joint under different working conditions; a stress analysis result comparison module for comparing the stress analysis results with preset safety thresholds and determining whether the design parameters need to be adjusted according to the comparison results; A computing feedback control algorithm module is configured to calculate the difference between the current stress analysis result and the preset safety threshold, and adjust the design parameters according to the actual working condition and material characteristics. A dynamic adjustment module is configured to dynamically adjust the design parameters according to the optimization algorithm. A fault prediction and maintenance suggestion module is configured to analyze potential failure modes and generate maintenance suggestions.

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