A method and system for life health management of HR3C materials

Abstract

This invention discloses a method and system for life health management of HR3C materials, relating to the field of data processing technology. The method includes: collecting operational condition data of HR3C materials under service conditions; conducting creep, fatigue, and creep-fatigue interaction performance tests on welds and heat-affected zones to obtain real-time service condition data; constructing a microstructure-performance coupling model based on the results of high-temperature accelerated aging tests; and performing micro-damage calculations and remaining life prediction based on the microstructure-performance coupling model and real-time service condition data, generating a health assessment index to perform health management. This invention solves the technical problems of existing technologies being unable to accurately and in real-time assess the damage evolution of HR3C materials under complex high-temperature conditions, and the insufficient accuracy of life prediction and health management. It achieves the technical effect of accurately assessing micro-damage and dynamically predicting remaining life through performance coupling analysis under multiple operating conditions and feedback from the aging process, thereby improving the accuracy of health management.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method and system for life health management of HR3C materials. Background Technology

[0002] With the increasing demand for high-temperature materials in the industrial sector, HR3C materials are widely used in high-temperature and high-pressure environments, such as boiler pipes, superheaters, and reheaters. Although HR3C materials possess excellent high-temperature resistance and mechanical properties, their performance degrades during long-term service due to factors such as creep, fatigue, and creep-fatigue interaction damage, affecting the stability and safety of equipment. However, current technologies still rely on periodic inspections and static damage prediction models, which are insufficient to address the complex dynamic operating conditions and damage mechanisms encountered by materials in actual service. Summary of the Invention

[0003] This application provides a life health management method and system for HR3C materials, which solves the technical problem of existing technologies in accurately assessing micro-damage and dynamically predicting remaining life through performance coupling analysis under multiple working conditions and feedback of aging processes, thereby improving the accuracy of health management.

[0004] The first aspect of this application provides a method for life health management of HR3C materials. The method includes: collecting operational condition data of the HR3C material under service conditions, the operational condition data including temperature data, stress data, strain data, and time history; based on the operational condition data, performing creep, fatigue, and creep-fatigue interaction performance tests on the weld and heat-affected zone of the HR3C material to obtain real-time service condition data, the service condition data including creep life parameters, fatigue life parameters, and interaction damage parameters; combining the results of high-temperature accelerated aging tests to construct a mapping relationship between microstructure evolution characteristic parameters and mechanical properties, obtaining a microstructure-performance coupling model; based on the microstructure-performance coupling model and the real-time service condition data, performing microscopic damage calculation and remaining life prediction of the HR3C material at the current moment, generating a remaining life estimate; and generating a health assessment index based on the remaining life estimate, and performing health management according to the health assessment index.

[0005] A second aspect of this application provides a life health management system for HR3C materials. The system includes: a running condition data acquisition module for acquiring running condition data of the HR3C material under service conditions, including temperature data, stress data, strain data, and time history; and a performance testing module for performing creep, fatigue, and creep-fatigue interaction performance tests on the weld and heat-affected zone of the HR3C material based on the running condition data, obtaining real-time service status data, including creep life parameters, fatigue life parameters, and interaction parameters. The system employs the following modules: a damage parameter module; a tissue-performance coupling module, which combines the results of high-temperature accelerated aging tests to construct a mapping relationship between tissue evolution characteristic parameters and mechanical properties, thereby obtaining a tissue-performance coupling model; a remaining lifetime prediction module, which calculates the microscopic damage and predicts the remaining lifetime of the HR3C material at the current moment based on the tissue-performance coupling model and the real-time service status data, generating an estimated remaining lifetime value; and a health assessment and management module, which generates a health assessment index based on the estimated remaining lifetime value and performs health management according to the health assessment index.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] This application provides a life health management method and system for HR3C materials, relating to the field of data processing technology. It collects operational data of HR3C materials, performs creep, fatigue, and interaction performance tests to obtain real-time service status data, and combines this with high-temperature accelerated aging test results to construct a tissue-performance coupling model to predict the material's remaining life. Based on the estimated remaining life, a health assessment index is generated, and health management is performed according to this index. This solves the technical problem of existing technologies that, through performance coupling analysis under multiple operating conditions and aging process feedback, accurately assess microscopic damage and dynamically predict remaining life, thereby improving the accuracy of health management. It achieves the technical effect of accurately assessing microscopic damage and dynamically predicting remaining life through performance coupling analysis under multiple operating conditions and aging process feedback, thus improving the accuracy of health management. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1A schematic diagram of a life health management method for HR3C materials provided in this application embodiment;

[0010] Figure 2 This is a schematic diagram of a life health management system for HR3C materials provided in an embodiment of this application.

[0011] Figure labeling: 11 Operational condition data acquisition module, 12 Performance testing module, 13 Organization-performance coupling module, 14 Remaining life prediction module, 15 Health assessment and management module. Detailed Implementation

[0012] This application provides a life health management method and system for HR3C materials, which solves the technical problem of existing technologies in accurately assessing micro-damage and dynamically predicting remaining life through performance coupling analysis under multiple working conditions and feedback of aging processes, thereby improving the accuracy of health management.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, this application provides a method for life health management of HR3C materials, the method comprising:

[0016] P10: Collect operating condition data of HR3C material under service conditions, including temperature data, stress data, strain data and time history.

[0017] Specifically, the first step is to collect operational condition data of the HR3C material under service conditions as the basic input for assessing its microscopic damage evolution and life degradation process. Operational condition data refers to the evolution record of the thermo-mechanical load combination information experienced by the material in the actual service environment over time, including temperature data, stress data, strain data, and time history. To ensure the engineering adaptability and physical accuracy of the data sources, this step adopts a zoned deployment, sensor acquisition, and data archiving execution scheme, as detailed below:

[0018] First, in areas where HR3C material is used (such as boiler superheater pipes, steam pipes, or reheater weld joints), typical representative locations including the base material area, weld area, and heat-affected zone are selected, and sensor nodes are deployed. Temperature data acquisition uses K-type or S-type thermocouple arrays, which are deployed on the key hot areas of the pipe outer wall by welding. The acquisition range is 300℃ to 750℃, with a temperature measurement accuracy of not less than ±1.5℃, and the signal is filtered and calibrated using a PID conditioning circuit.

[0019] Secondly, different strategies were used for acquiring stress and strain data. Stress data was obtained through indirect modeling and stress calculation. Specifically, by combining parameters such as steam pressure, pipe diameter, wall thickness, and arrangement angle, the principal stress history of the wall surface was calculated using a thin-walled circular pipe mechanical model or finite element simulation method, and the direction and magnitude of the principal stress were calculated in real time. At the same time, strain data was acquired using high-temperature resistance strain gauges or fiber Bragg grating (FBG) arrays. Longitudinal and circumferential strain monitoring points were set up in the weld, base material, and heat-affected zone to collect plastic and creep coupled strain values ​​with a resolution better than 5 με.

[0020] Furthermore, to ensure the integrity of the loading process required for creep and low-cycle fatigue life analysis, this step involves data acquisition with periodic time steps, with a recommended cycle of 10 seconds to 1 minute, adjusted according to the application scenario. All data is transmitted in real time to the edge computing unit via the Data Acquisition System (DAQ) and undergoes local buffering and outlier removal.

[0021] Finally, the temperature, stress, and strain data are bound to their corresponding time labels to form a unified format of multi-parameter time-series dataset for operational conditions. This dataset serves as the foundational data source for subsequent creep life modeling, tissue evolution prediction, and health assessment models. It possesses temporal continuity, spatial representativeness, and physical consistency, and its integrity is verified by a data consistency check algorithm. By executing step P10 through the above technical path, a comprehensive and reliable database of operational conditions for HR3C materials under in-service conditions can be constructed, achieving high-quality data support from physical perception to engineering modeling.

[0022] P20: Based on the operating condition data, creep, fatigue, and creep-fatigue interaction performance tests are performed on the weld and heat-affected zone of the HR3C material to obtain real-time service status data, which includes creep life parameters, fatigue life parameters, and interaction damage parameters.

[0023] Furthermore, step P20 in this embodiment of the application also includes:

[0024] P21: Construct a creep damage model, a fatigue life model, and a creep-fatigue interactive damage coupling model; P22: Based on the operating condition data, assess the microstructure stability of the weld fusion line, heat-affected zone, and base material, and generate stability assessment coefficients; P23: Use the stability assessment coefficients to perform nonlinear correction on the creep damage model, fatigue life model, and creep-fatigue interactive damage coupling model, and based on the corrected model and the operating condition data, assess the service status and generate the creep life parameter, fatigue life parameter, and interactive damage parameter.

[0025] It should be understood that, based on the aforementioned collected operating condition data, performance tests are conducted on the weld and heat-affected zone of HR3C material to obtain key performance parameters of the material under actual service conditions, including creep life parameters, fatigue life parameters, and creep-fatigue interaction damage parameters. To achieve this goal, it is first necessary to construct appropriate creep damage models, fatigue life models, and creep-fatigue interaction damage coupling models.

[0026] When constructing a creep damage model, several important factors need to be considered, including temperature, stress, and time. Combining the properties of HR3C material with relevant standards, the behavior of creep damage is fitted using tensile and creep test data. Next, a fatigue life model is established using the Manson-Coffin or Basquin model, deriving the corresponding parameters of fatigue damage based on experimental data. These models provide independent prediction capabilities for creep damage and fatigue damage, respectively, and lay the foundation for subsequent creep-fatigue interactive damage coupling models.

[0027] To accurately describe the interaction between creep and fatigue, a creep-fatigue interactive damage coupling model needs to be constructed. This model needs to be based on the independent damage mechanisms of creep and fatigue, taking into account their actual interaction during service. In particular, during the load-holding phase, the effects of creep and fatigue on the material coexist, and this interaction affects the material's damage accumulation. Therefore, when establishing the interactive damage model, stress path tracing and nonlinear damage accumulation algorithms are used to combine the effects of creep damage and fatigue damage, further improving the predictive accuracy of the damage model.

[0028] Next, based on operational data, the microstructure stability of the weld fusion line, heat-affected zone, and base metal is assessed. Specifically, the stability of the weld and heat-affected zone needs to be evaluated based on real-time temperature, stress, and strain data, combined with the microstructure evolution characteristics of HR3C material. Stability assessment coefficients are generated by analyzing the microstructure characteristics of these regions (such as precipitates and grain size). These coefficients reflect the performance degradation of different regions under actual operating conditions, especially the impact of microstructure changes in the weld and heat-affected zone on overall performance.

[0029] After obtaining the stability assessment coefficients, the constructed creep damage model, fatigue life model, and creep-fatigue interactive damage coupling model can be nonlinearly modified. The parameters in these models are dynamically adjusted based on the stability assessment coefficients. For example, when the weld fusion line has poor stability, the accumulation rate of creep damage can be increased or the stress amplitude coefficient in the fatigue life model can be improved to ensure that the model better reflects the damage behavior under actual service conditions.

[0030] Finally, the service status is assessed by combining the revised model with real-time service status data. By inputting real-time service status data into the revised creep damage model, fatigue life model, and creep-fatigue interactive damage coupling model, creep life parameters, fatigue life parameters, and creep-fatigue interaction damage parameters are calculated to accurately describe the current health status of the material, providing reliable basic data for subsequent life prediction and health management.

[0031] Furthermore, in constructing the creep-fatigue interactive damage coupling model, step P21 of this application embodiment also includes:

[0032] P21-1: Collect load spectrum data of deep peak-shaving units and analyze the stress-strain history for multiple time periods; P21-2: Perform spectral clustering processing on the stress-strain history to divide it into typical load mode sets; P21-3: Match the corresponding interactive test parameter set according to the typical load mode set; P21-4: Based on the interactive test parameter set, dynamically adjust the temporal structure of the fatigue loading stage and the load holding stage, perform spectral adaptive optimization of the creep-fatigue loading path, and generate the creep-fatigue interactive damage coupling model.

[0033] Optionally, the process of constructing the creep-fatigue interactive damage coupling model can be further refined to accurately simulate the damage evolution of HR3C materials during actual service, especially when the material is subjected to complex load fluctuations under conditions such as deep peak shaving units.

[0034] First, load spectrum data of the deep peak-shaving unit is collected. This load spectrum data includes all load histories experienced by the unit during operation, including load amplitude, load fluctuation period, and load type. The collected data should cover the complete operating cycle of the unit, especially the high-temperature and high-pressure fluctuating load portion. Through real-time monitoring systems and high-frequency data acquisition equipment (such as stress sensors and temperature sensors), the stress-strain history under different operating stages can be obtained as the basic parameters for subsequent damage assessment. The stress-strain history reflects the deformation of the material under different loading modes during actual service, including both periodic and sudden loads.

[0035] Next, spectral clustering is performed on the obtained stress-strain history. Spectral clustering is a clustering algorithm based on stress-strain data that classifies stress-strain history according to their similarity, grouping stress-strain patterns with similar load fluctuation characteristics into one category. By analyzing the stress-strain history over different time periods, a set of typical load patterns is identified. These patterns typically reflect the typical operating conditions encountered by the unit during its service life. For example, in peak-shaving units, there may be regular load fluctuations, sudden load increases, or overload operation. Spectral clustering quantifies and classifies these situations.

[0036] Next, based on the obtained set of typical load modes, a corresponding set of interactive test parameters is matched for each mode. These interactive test parameter sets include the temperature and stress conditions for creep testing, and the stress amplitude and loading frequency for fatigue testing. The interactive test parameter sets for each typical load mode are obtained through laboratory tests. These tests simulate the material's behavior under the corresponding load modes, providing an important basis for establishing the creep-fatigue interactive damage coupling model. By matching the corresponding interactive test parameter sets, the reliability of the model and its adaptability to actual working conditions can be ensured.

[0037] Finally, based on the matched set of interactive test parameters, the temporal structure of the fatigue loading stage and the holding stage is dynamically adjusted to optimize the spectral adaptability of the creep-fatigue loading path. In this process, the order and duration of the fatigue loading stage and the creep holding stage are adjusted by combining the stress-strain history and interactive test parameters. For example, after fatigue loading under high stress, the material may undergo a brief creep process. In this case, the duration of the holding stage needs to be adjusted based on experimental data to better simulate the material's response under actual service conditions. Through this dynamic adjustment, the spectral adaptability of the creep-fatigue loading path is optimized, thereby ensuring that the creep-fatigue interactive damage coupling model better conforms to the damage evolution law under actual working conditions.

[0038] Finally, through the above steps, a creep-fatigue interactive damage coupling model is generated. This model can comprehensively reflect the interaction between creep and fatigue and its impact on the material damage evolution during the actual service of HR3C materials, and accurately assess the performance degradation of materials under complex load conditions.

[0039] P30: Combining the results of high-temperature accelerated aging tests, a mapping relationship between tissue evolution characteristic parameters and mechanical properties is constructed to obtain a tissue-property coupling model.

[0040] Furthermore, step P30 in this embodiment of the application also includes:

[0041] P31: Acquire microstructure images of the HR3C base material region, weld region, and heat-affected zone after high-temperature aging treatment, and extract microstructure evolution characteristic parameters based on the microstructure images; P32: Obtain mechanical property parameters at each time point through tensile, creep, and fatigue tests on the corresponding aging specimens; P33: Perform coupling analysis based on multi-scale modeling, integrate microstructure characteristics and macroscopic mechanical properties, construct a mapping function set between microstructure evolution characteristic parameters and mechanical property parameters, and form the microstructure-property coupling model.

[0042] It should be understood that by combining the results of high-temperature accelerated aging tests, a mapping relationship between tissue evolution characteristic parameters and mechanical properties is constructed to obtain an accurate tissue-property coupling model. This step aims to evaluate the tissue changes of HR3C materials under long-term high-temperature environments and their impact on mechanical properties through high-temperature aging tests, thereby establishing an efficient predictive model and providing a scientific basis for material life assessment and health management.

[0043] First, microstructure images of HR3C material after high-temperature aging treatment were acquired in the base metal region, weld zone, and heat-affected zone. High-temperature aging tests are typically conducted at temperatures above 700°C to simulate microstructural changes in the material under high-temperature service conditions. Fine-grained microstructure images were obtained using techniques such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), and electron backscatter diffraction (EBSD). These images were then analyzed to extract microstructure evolution parameters such as grain size, the number and distribution of precipitates, secondary phase grain size, and twin density. These parameters are crucial for describing the microstructural changes of HR3C material during high-temperature aging and directly affect the material's mechanical properties.

[0044] Next, tensile tests, creep tests, and fatigue tests were conducted on the corresponding aged specimens to obtain the mechanical property parameters at each time point. These mechanical property tests were used to determine parameters such as yield strength, tensile strength, ductility, creep rate, and fatigue life of the material at different time periods (e.g., every 1000 hours). By acquiring the experimental data, we can accurately assess the changes in the mechanical properties of HR3C material during high-temperature aging and provide necessary mechanical data support for the construction of subsequent models.

[0045] Furthermore, based on multi-scale modeling methods, coupled analysis is performed to integrate microstructural features and macroscopic mechanical properties, constructing a set of mapping functions between microstructure evolution parameters and mechanical property parameters, thus forming the microstructure-property coupled model. For example, the multi-scale modeling method combines analysis at different levels from micro to macro. First, microscale modeling (e.g., based on crystallography, phase transition dynamics, and damage accumulation) analyzes the microstructure evolution characteristics of the material. Then, macroscale modeling (e.g., finite element analysis of macroscopic stress-strain behavior) evaluates the mechanical properties of the material. The combination of these two approaches yields a set of mapping functions that describe the changes in the material's mechanical properties under specific microstructure evolution conditions. These mapping functions will provide a comprehensive theoretical framework for the lifetime assessment and damage prediction of HR3C materials.

[0046] By performing the above steps, the constructed tissue-performance coupling model can accurately reflect the changes in mechanical properties of HR3C materials due to tissue evolution during high-temperature service, thus providing a reliable basis for subsequent health assessment and remaining life prediction.

[0047] Furthermore, the organization-performance coupling model is modified based on a time-sensitive process feedback mechanism, including:

[0048] P33-1a: Acquire transmission electron microscopy images and backscattered electron diffraction patterns at different time periods to identify the actual tissue state; P33-2a: Compare and analyze the differences between the actual tissue state and the predicted tissue state to construct an error vector set; P33-3a: Dynamically adjust the time constant and evolution rate function of the tissue evolution model based on the error vector set to perform real-time correction of the tissue-performance coupling model.

[0049] In one possible embodiment of this application, the microstructure-property coupling model can be corrected based on an aging process feedback mechanism to ensure that the model accurately reflects the actual evolution of the material during long-term high-temperature service. By comparing and correcting the deviation between the microstructure state and the predicted state in real time, the prediction accuracy of the model is improved.

[0050] First, transmission electron microscopy (TEM) images and backscattered electron diffraction (EBSD) patterns were acquired at different aging stages to identify the actual microstructure of the HR3C material. TEM and EBSD are commonly used techniques for analyzing the microstructure of materials. TEM provides high-resolution images of the internal microstructure of materials, particularly suitable for analyzing details such as grain morphology, precipitates, and grain boundaries; while EBSD patterns help analyze the crystal orientation, grain boundary structure, and stress distribution of the material. During the aging process, HR3C materials undergo different microstructural evolution processes at different time points, including grain growth and changes in precipitates, which significantly affect the mechanical properties of the material.

[0051] Next, a comparative analysis is performed between the actual microstructure collected and the microstructure predicted by the model. The predicted microstructure is generated based on a microstructure evolution model, which typically estimates the changes in material microstructure based on certain assumptions (such as temperature and stress conditions) during aging. However, the actual microstructure is often affected by complex working conditions and uncertainties, resulting in differences from the predicted results. By comparing and analyzing these differences, the prediction errors of the model at different time periods can be identified. By constructing a set of error vectors, we can quantify the difference between the actual and predicted microstructure, providing a basis for model correction.

[0052] Next, based on the obtained error vector set, the time constant and evolution rate function of the microstructure evolution model are dynamically adjusted to achieve real-time correction of the microstructure-property coupling model. Specifically, the error vector set provides quantitative information about the prediction error of microstructure state. These errors can be passed to the microstructure evolution model through a backpropagation algorithm to adjust the parameters in the model, such as the time constant (a factor that determines the microstructure evolution rate) and the evolution rate function (which determines the evolution law of material microstructure during aging). By dynamically adjusting these parameters, we can make the microstructure-property coupling model more closely reflect the microstructure evolution process of materials under actual working conditions, thereby improving the accuracy and predictive ability of the model.

[0053] By implementing these steps, the microstructure-property coupling model can be continuously revised, ensuring its high accuracy and timeliness in reflecting changes in the mechanical properties of HR3C materials during high-temperature aging. Therefore, this feedback-based dynamic correction method can effectively address the complex evolution of materials during service, thus providing a more precise basis for remaining service life prediction and health assessment.

[0054] P40: Based on the tissue-performance coupling model and the real-time service status data, perform microscopic damage calculation and remaining life prediction of the HR3C material at the current moment, and generate an estimated remaining life value.

[0055] Furthermore, step P40 in this embodiment of the application also includes:

[0056] P41: Based on the real-time service status data, extract the creep life parameter, fatigue life parameter, and interaction damage parameter for the current service stage; P42: Input the creep life parameter, fatigue life parameter, and interaction damage parameter into the tissue-performance coupling model to calculate the damage evolution state at the current moment; P43: Based on the damage evolution state and combined with historical damage evolution data, construct a time evolution function to predict the time evolution of the cumulative effect of micro-damage and output the estimated remaining life value at the current moment.

[0057] It should be understood that, based on the aforementioned organization-performance coupling model and real-time service status data, the microscopic damage of the HR3C material at the current moment is calculated and the remaining service life is predicted, generating an estimated remaining service life value. In other words, by comprehensively assessing the current damage state of the material, its future remaining service life is predicted, providing a basis for subsequent health management decisions.

[0058] First, based on the collected real-time service status data, creep life parameters, fatigue life parameters, and interaction damage parameters for the current service stage are extracted. These parameters reflect the damage state of the material under current operating conditions. Specifically, the creep life parameter describes the damage caused by the creep process of the material under high temperature; the fatigue life parameter quantifies the fatigue damage of the material under repeated loading; and the interaction damage parameter integrates the damage effects of creep and fatigue under simultaneous action. Based on this, the above damage parameters are extracted using real-time monitored data such as temperature, stress, and strain, providing direct input for subsequent damage assessment and remaining service life prediction.

[0059] Next, the extracted creep life parameters, fatigue life parameters, and interaction damage parameters are input into the previously constructed microstructure-property coupling model to calculate the damage evolution state at the current moment. The core of this step is to use the microstructure-property coupling model to combine the real-time extracted damage parameters with the microstructure evolution of the material. Through the damage accumulation mechanism embedded in the model, the current damage evolution state of the material is calculated, including changes in microstructural features such as the initiation and propagation of microcracks, the degree of grain deformation, and phase transformation.

[0060] Furthermore, based on the current damage evolution state and combined with historical damage evolution data, a time evolution function is constructed to predict the cumulative effect of micro-damage over time, outputting an estimate of the remaining lifetime at the current moment. The time evolution function is a mathematical model used to describe the cumulative law of micro-damage over time. By combining the current damage evolution state and historical damage evolution data, the time evolution function can predict the future accumulation trend of micro-damage, thus providing a basis for estimating the remaining lifetime. Specifically, the time evolution function can be constructed based on damage mechanics theory, probabilistic statistical methods, or machine learning algorithms, comprehensively considering the initial properties of the material, service conditions, and dynamic changes in microstructure. Through this function, the current damage state and historical damage trends can be combined to predict damage development over a future period and estimate the remaining lifetime of the material.

[0061] This process provides data support and theoretical basis for the health management and maintenance decisions of HR3C materials, thereby enabling the efficient and reliable use of the materials.

[0062] Furthermore, step P40 in this embodiment of the application also includes:

[0063] The time evolution function is based on nonlinear damage evolution theory, takes into account the coupling effect of creep and fatigue during high-temperature service, and is adjusted according to the ratio of service time to current damage state to gradually update the remaining life estimate.

[0064] Optionally, the time evolution function is designed to consider the coupling effect of creep and fatigue during high-temperature service and is modeled based on nonlinear damage evolution theory. The core objective of this function is to accurately describe the damage accumulation caused by the interaction of creep and fatigue during service, especially under complex conditions of high temperature and high pressure, and its impact on the remaining service life.

[0065] The time evolution function is based on nonlinear damage evolution theory, which considers that the accumulation of creep and fatigue damage is nonlinear when materials undergo complex conditions such as high temperature and high pressure. Specifically, the interaction between creep damage and fatigue damage is not a simple linear superposition, but rather a complex interaction that occurs depending on different load conditions, time, and the evolution of the material's microstructure. Therefore, exponential, power, or logarithmic nonlinear relationships need to be introduced into the damage evolution process to adapt to the changes in damage rate under high temperature and multi-condition environments.

[0066] When establishing the time evolution function, the coupling effect of creep and fatigue is a key consideration. During actual service, creep and fatigue often occur in parallel, especially under peak operating conditions, where materials frequently experience high-stress cycles (fatigue) and static loads at sustained high temperatures (creep). The interaction of these factors accelerates material damage. Therefore, the time evolution function must not only consider the time accumulation effect of creep but also comprehensively account for the cyclic strain effect of fatigue, and adjust the damage rate according to actual operating conditions.

[0067] Specifically, the time evolution function dynamically adjusts the remaining service life estimate based on the ratio of service time to the current damage state. This means that damage progresses slowly in the initial service stages, but as damage accumulates, the damage rate gradually increases, and the parameters in the function change over time. For example, if the material primarily experiences fatigue damage in the early stages and enters a creep-dominated stage later, the time evolution function will adjust the weight of the creep damage component accordingly, making the damage prediction more closely reflect the actual service state.

[0068] Ultimately, through this dynamic adjustment mechanism, the time evolution function can provide an accurate estimate of the remaining life of the material at each specific moment. This estimate takes into account the current damage state of the material and reflects the damage process through real-time updates. By combining the nonlinear accumulation of damage, creep-fatigue interaction effects, and adjustments to the ratio of service time to damage, the generated remaining life estimate is more accurate, providing a scientific basis for the health management and maintenance decisions of HR3C materials.

[0069] Furthermore, the remaining lifetime estimate is generated based on a multi-source model fusion strategy and also includes:

[0070] P43-1a: The output of the physical model is used as the initial prediction benchmark; P43-2a: A machine learning-based neural network model is introduced to train the damage parameters in historical sample data and actual failure data to generate a data-driven prediction model; P43-3a: The physical model and the data-driven model are combined and fused using the combined model to generate the remaining lifetime estimate.

[0071] In one possible embodiment of this application, the remaining lifetime estimate can be generated using a multi-source model fusion strategy, combining the advantages of physical models and data-driven models to improve the accuracy and robustness of the prediction.

[0072] First, the output of the physical model is used as the initial prediction baseline. The physical model is constructed based on known creep damage models, fatigue life models, and creep-fatigue interactive damage coupling models. These models can predict the remaining life of materials based on real-time monitoring data (such as temperature, stress, strain, etc.) and established material properties. The physical model provides a preliminary estimate of the remaining life based on theoretical and experimental data, which reflects the expected life progression of the material under standard operating conditions.

[0073] Next, a machine learning-based neural network model is introduced. This model is trained on damage parameters from historical sample data and actual failure data to generate a data-driven prediction model. In this process, the neural network model learns the complex nonlinear relationship between material damage and lifespan by inputting a large amount of historical data (such as past damage data, changes in operating conditions, and material failure data). Through training, the neural network can capture potential patterns that are difficult for physical models to describe, especially unknown or nonlinear factors that may exist during material service, thus improving the accuracy of remaining life prediction.

[0074] Furthermore, the physical model and the data-driven model are combined for fusion prediction. Specifically, the preliminary estimate provided by the physical model serves as one of the inputs to the neural network model, which then adjusts and optimizes this estimate based on historical data. This combination allows the prediction to integrate the fundamental laws of physics with the complexities of data-driven approaches, resulting in a more accurate and reliable remaining lifetime estimate. The fusion strategy can employ methods such as weighted averaging, Bayesian optimization, or Gaussian mixture models, dynamically adjusting the weights based on the prediction errors of the physical model and the neural network. This makes the final remaining lifetime estimate more practically meaningful and highly adaptable to various operating conditions.

[0075] Through this multi-source model fusion strategy, the final estimated remaining lifetime value can not only reflect the current damage state of the material, but also provide a more accurate lifetime prediction by combining historical data and real-time monitoring data. This can make up for the limitations of a single physical model or data-driven model in prediction, and ensure that the health management of HR3C materials under high temperature and complex working conditions has higher accuracy and reliability.

[0076] P50: Based on the estimated remaining life expectancy, generate a health assessment index and manage health according to the health assessment index.

[0077] Furthermore, step P50 in this embodiment of the application also includes:

[0078] P51: Collect the current remaining life estimate, historical damage evolution trend, and operating condition change information of the HR3C material to construct a multi-factor assessment dataset; P52: Based on the multi-factor assessment dataset, calculate the creep damage score, fatigue damage score, and creep-fatigue interaction damage score, and perform weighted fusion according to preset weights to generate a comprehensive health score; P53: Compare the comprehensive health score with a preset grading threshold to generate a health assessment index.

[0079] Specifically, based on the aforementioned remaining lifespan estimate, a health assessment index is generated, and health management is implemented based on this index. The key to this process lies in integrating multiple factors that affect the health status of materials and quantifying the material's health level through a scientific scoring and weighting mechanism.

[0080] First, we collected the current remaining life estimate, historical damage evolution trend, and operational condition changes of HR3C materials, and constructed a multi-factor assessment dataset based on this data. This dataset includes multiple dimensions affecting the material's health status, such as current remaining life, past damage history, changes in operational conditions (e.g., temperature, pressure, load spectrum), and the evolution of damage mechanisms such as creep and fatigue. These factors are intertwined and jointly determine the material's health status. Therefore, by integrating this information, we can obtain a comprehensive dataset reflecting the state of HR3C materials, serving as the basis for subsequent health assessments and life predictions.

[0081] Next, based on the constructed multi-factor evaluation dataset, creep damage scores, fatigue damage scores, and creep-fatigue interaction damage scores were calculated. These scores quantify the health of the material under different damage mechanisms. Specifically, the creep damage score considers the cumulative damage over time under high-temperature conditions, the fatigue damage score reflects the cumulative damage under repeated loading, and the creep-fatigue interaction damage score integrates the damage interaction effects of creep and fatigue. The calculation of each damage score combines the material's damage parameters, operating condition data, and damage model, and obtains the specific score result through numerical calculation.

[0082] These scores are quantified based on the severity of damage; therefore, creep damage scores, fatigue damage scores, and interactive damage scores reflect the impact of different damage mechanisms on the material's health status. Based on this, these scores are weighted and fused according to preset weighting coefficients to generate a comprehensive health score. The weighting coefficients are typically set based on the material's actual working environment, material properties, and the sensitivity of the damage mechanism. For example, if creep has a significant impact on the material under current operating conditions, the creep damage score may have a higher weight. Through weighted fusion, the impacts of different damage mechanisms are combined into a comprehensive health score.

[0083] Furthermore, the generated comprehensive health score is compared with preset grading thresholds to obtain a health assessment index. Grading thresholds are typically determined by empirical data or experimental results, representing the score range for different health states. Based on the comparison between the comprehensive health score and the thresholds, the health assessment index can be divided into multiple levels, such as "normal," "mild damage," "moderate damage," "severe damage," or "failure warning." Each health assessment level represents the current health status of the material and determines whether maintenance, repair, or replacement is necessary.

[0084] Ultimately, by generating a health assessment index, different health management measures can be triggered based on different health levels. For example, if the health assessment index shows "severe damage" or "failure warning," repairs or replacements can be carried out immediately; if the assessment result shows "normal" or "minor damage," the material condition can continue to be monitored, and regular inspections or delayed maintenance can be conducted.

[0085] This multi-factor assessment and weighted fusion health assessment method can more accurately reflect the health status of HR3C materials under actual working conditions, thereby providing reliable data support for material health management, remaining life prediction and operation and maintenance decisions.

[0086] In summary, the embodiments of this application have at least the following technical effects:

[0087] This application collects operational data (including temperature, stress, strain, and time history) of HR3C materials and conducts creep, fatigue, and creep-fatigue interaction performance tests based on this data to obtain real-time service status data. Combining the results of high-temperature accelerated aging tests, a coupled model of tissue evolution characteristics and mechanical properties is constructed. The model calculates the current damage state and predicts the remaining service life. Finally, a health assessment index is generated, and health management is implemented based on this index.

[0088] It achieves the technical effect of accurately assessing microscopic damage and dynamically predicting remaining life through performance coupling analysis under multiple working conditions and feedback of the aging process, thereby improving the accuracy of health management.

[0089] Example 2, based on the same inventive concept as the life health management method for HR3C materials in the foregoing examples, such as... Figure 2 As shown, this application provides a life health management system for HR3C materials. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0090] Operating condition data acquisition module 11 is used to acquire operating condition data of HR3C material under service conditions. The operating condition data includes temperature data, stress data, strain data and time history.

[0091] The performance testing module 12 is used to perform creep, fatigue, and creep-fatigue interaction performance tests on the weld and heat-affected zone of the HR3C material based on the operating condition data, and obtain real-time service status data, including creep life parameters, fatigue life parameters, and interaction damage parameters.

[0092] The tissue-performance coupling module 13 is used to combine the results of high-temperature accelerated aging test to construct the mapping relationship between tissue evolution characteristic parameters and mechanical properties, and obtain the tissue-performance coupling model.

[0093] The remaining lifetime prediction module 14 is used to perform microscopic damage calculation and remaining lifetime prediction of HR3C material at the current moment based on the tissue-performance coupling model and the real-time service status data, and generate an estimated remaining lifetime value.

[0094] The health assessment management module 15 is used to generate a health assessment index based on the estimated remaining life expectancy and to perform health management according to the health assessment index.

[0095] Furthermore, the performance testing module 12 is also used to perform the following steps:

[0096] A creep damage model, a fatigue life model, and a creep-fatigue interactive damage coupling model are constructed. Based on the operating condition data, the microstructure stability of the weld fusion line, heat-affected zone, and base material is assessed, and stability assessment coefficients are generated. Using the stability assessment coefficients, the creep damage model, fatigue life model, and creep-fatigue interactive damage coupling model are nonlinearly modified. Based on the modified model and the operating condition data, the service status is assessed, and the creep life parameter, fatigue life parameter, and interactive damage parameter are generated.

[0097] Furthermore, the performance testing module 12 is also used to perform the following steps:

[0098] Load spectrum data of deep peak-shaving units are collected and analyzed to obtain stress-strain histories for multiple time periods. Spectral clustering is performed on the stress-strain histories to divide them into typical load mode sets. The corresponding interactive test parameter sets are matched according to the typical load mode sets. Based on the interactive test parameter sets, the temporal structure of the fatigue loading stage and the load holding stage is dynamically adjusted to perform spectral adaptive optimization of the creep-fatigue loading path, generating the creep-fatigue interactive damage coupling model.

[0099] Furthermore, the organization-performance coupling module 13 is also used to perform the following steps:

[0100] Microstructure images of the HR3C base material region, weld region, and heat-affected zone after high-temperature aging treatment were acquired, and microstructure evolution characteristic parameters were extracted based on the microstructure images. Mechanical property parameters at each time point were obtained through tensile, creep, and fatigue tests on the corresponding aged specimens. Based on multi-scale modeling, coupling analysis was performed to integrate microstructural features and macroscopic mechanical properties, and a mapping function set between microstructure evolution characteristic parameters and mechanical property parameters was constructed to form the microstructure-property coupling model.

[0101] Furthermore, the organization-performance coupling module 13 is also used to perform the following steps:

[0102] Transmission electron microscopy (TEM) images and backscattered electron diffraction (BED) patterns were acquired at different time points to identify the actual tissue state. The actual tissue state and the predicted tissue state were compared and analyzed to construct an error vector set. The time constant and evolution rate function of the tissue evolution model were dynamically adjusted based on the error vector set to perform real-time correction of the tissue-performance coupling model.

[0103] Furthermore, the remaining lifetime prediction module 14 is also used to perform the following steps:

[0104] Based on the real-time service status data, creep life parameters, fatigue life parameters, and interaction damage parameters for the current service stage are extracted; the creep life parameters, fatigue life parameters, and interaction damage parameters are input into the tissue-performance coupling model to calculate the damage evolution state at the current moment; based on the damage evolution state and combined with historical damage evolution data, a time evolution function is constructed to predict the time evolution of the cumulative effect of micro-damage and output the estimated remaining life value at the current moment.

[0105] Furthermore, the remaining lifetime prediction module 14 is also used to perform the following steps:

[0106] The time evolution function is based on nonlinear damage evolution theory, takes into account the coupling effect of creep and fatigue during high-temperature service, and is adjusted according to the ratio of service time to current damage state to gradually update the remaining life estimate.

[0107] Furthermore, the remaining lifetime prediction module 14 is also used to perform the following steps:

[0108] The physical model output is used as the initial prediction benchmark; a machine learning-based neural network model is introduced to train damage parameters and actual failure data in historical sample data to generate a data-driven prediction model; the physical model and the data-driven model are combined and fused using the combined model to generate the remaining lifetime estimate.

[0109] Furthermore, the health assessment management module 15 is also used to perform the following steps:

[0110] Collect the current remaining life estimate, historical damage evolution trend and operating condition change information of the HR3C material to construct a multi-factor assessment dataset; based on the multi-factor assessment dataset, calculate creep damage score, fatigue damage score and creep-fatigue interaction damage score, and perform weighted fusion according to preset weights to generate a comprehensive health score; compare the comprehensive health score with a preset grading threshold to generate a health assessment index.

[0111] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0112] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0113] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for life health management of HR3C materials, characterized in that, The method includes: The operating condition data of HR3C material under service conditions are collected, including temperature data, stress data, strain data and time history. Based on the operating condition data, creep, fatigue, and creep-fatigue interaction performance tests were conducted on the weld and heat-affected zone of the HR3C material to obtain real-time service status data, which includes creep life parameters, fatigue life parameters, and interaction damage parameters. Based on the results of high-temperature accelerated aging tests, a mapping relationship between tissue evolution characteristic parameters and mechanical properties was constructed to obtain a tissue-property coupling model. Based on the tissue-performance coupling model and the real-time service status data, the micro-damage of the HR3C material at the current moment is calculated and the remaining life is predicted, generating an estimated remaining life value. Based on the estimated remaining life expectancy, a health assessment index is generated, and health management is carried out according to the health assessment index. Based on the operating condition data, creep, fatigue and creep-fatigue interaction performance tests were conducted on the weld and heat-affected zone of the HR3C material to obtain real-time service status data, and to construct creep damage model, fatigue life model and creep-fatigue interaction damage coupling model. Construct a creep-fatigue interactive damage coupling model, including: Load spectrum data of deep peak-shaving units were collected and analyzed to obtain stress-strain histories over multiple time periods; Perform spectral clustering on the stress-strain history to divide it into a set of typical load modes; Match the corresponding set of interactive test parameters according to the set of typical load patterns; Based on the interactive test parameter set, the temporal structure of the fatigue loading segment and the load holding stage is dynamically adjusted to perform spectral adaptive optimization of the creep-fatigue loading path and generate the creep-fatigue interactive damage coupling model. Based on the results of high-temperature accelerated aging tests, a mapping relationship between tissue evolution characteristic parameters and mechanical properties is constructed to obtain a tissue-property coupling model, including: Microstructure images of the HR3C base material area, weld area and heat-affected zone after high-temperature aging treatment were collected, and microstructure evolution characteristic parameters were extracted based on the microstructure images; Mechanical property parameters at each time point were obtained by tensile, creep, and fatigue tests on the corresponding aged specimens. Based on multi-scale modeling, coupling analysis is performed, integrating microstructural features and macroscopic mechanical properties, and constructing a set of mapping functions between tissue evolution characteristic parameters and mechanical performance parameters to form the tissue-performance coupling model. The organization-performance coupling model is modified based on a time-sensitive process feedback mechanism, including: Transmission electron microscopy images and backscattered electron diffraction patterns were acquired at different time points to identify the actual tissue state; The actual organizational state is compared with the predicted organizational state to construct an error vector set. The time constant and evolution rate function of the tissue evolution model are dynamically adjusted based on the error vector set to perform real-time correction of the tissue-performance coupling model.

2. The lifespan health management method for HR3C materials as described in claim 1, characterized in that, Based on the aforementioned operating condition data, creep, fatigue, and creep-fatigue interaction performance tests are performed on the weld and heat-affected zone of the HR3C material to obtain real-time service status data. The test also includes: Based on the aforementioned operating condition data, the microstructure stability of the weld fusion line, heat-affected zone, and base material is assessed, and stability assessment coefficients are generated. Using the stability evaluation coefficients, the creep damage model, fatigue life model, and creep-fatigue interactive damage coupling model are nonlinearly modified. Based on the modified models and the operating condition data, the service status is evaluated, and the creep life parameter, fatigue life parameter, and interactive damage parameter are generated.

3. The lifespan health management method for HR3C materials as described in claim 1, characterized in that, Based on the aforementioned tissue-performance coupling model and the real-time service status data, microscopic damage calculation and remaining lifetime prediction of the HR3C material at the current moment are performed, generating an estimated remaining lifetime value, including: Based on the real-time service status data, the creep life parameter, fatigue life parameter, and interaction damage parameter of the current service stage are extracted. The creep life parameter, fatigue life parameter, and interaction damage parameter are input into the tissue-performance coupling model to calculate the damage evolution state at the current moment. Based on the damage evolution state and combined with historical damage evolution data, a time evolution function is constructed to predict the time evolution of the cumulative effect of micro-damage and output the estimated remaining lifetime at the current moment.

4. The life health management method for HR3C materials as described in claim 3, characterized in that, The time evolution function is based on nonlinear damage evolution theory, takes into account the coupling effect of creep and fatigue during high-temperature service, and is adjusted according to the ratio of service time to current damage state to gradually update the remaining life estimate.

5. A life health management method for HR3C materials as described in claim 4, characterized in that, The remaining lifetime estimate is generated based on a multi-source model fusion strategy and also includes: The output of the physical model is used as the initial prediction baseline; A machine learning-based neural network model is introduced to train damage parameters and actual failure data in historical sample data to generate a data-driven prediction model. The physical model is combined with the data-driven model, and the combined model is used for fusion prediction to generate the remaining lifetime estimate.

6. A life health management method for HR3C materials as described in claim 1, characterized in that, Based on the estimated remaining life expectancy, a health assessment index is generated, including: Collect the current remaining life estimate, historical damage evolution trend and operating condition change information of the HR3C material, and construct a multi-factor evaluation dataset; Based on the multi-factor assessment dataset, creep damage score, fatigue damage score and creep-fatigue interaction damage score are calculated, and weighted and fused according to preset weights to generate a comprehensive health score. The comprehensive health score is compared with the preset grading threshold to generate a health assessment index.

7. A life health management system for HR3C materials, employing a life health management method for HR3C materials as described in any one of claims 1-6, characterized in that, The system includes: The operating condition data acquisition module is used to collect operating condition data of HR3C material under service conditions. The operating condition data includes temperature data, stress data, strain data and time history. The performance testing module is used to perform creep, fatigue, and creep-fatigue interaction performance tests on the weld and heat-affected zone of the HR3C material based on the operating condition data, and obtain real-time service status data, including creep life parameters, fatigue life parameters, and interaction damage parameters. The tissue-performance coupling module is used to combine the results of high-temperature accelerated aging test to construct the mapping relationship between tissue evolution characteristic parameters and mechanical properties, and obtain a tissue-performance coupling model. The remaining lifetime prediction module is used to perform microscopic damage calculation and remaining lifetime prediction of HR3C material at the current moment based on the tissue-performance coupling model and the real-time service status data, and generate an estimated remaining lifetime value. A health assessment management module is used to generate a health assessment index based on the estimated remaining life expectancy and to perform health management according to the health assessment index.

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