A high-heat-resistant steel water immersion type harmonic nonlinear high-temperature aging test method and system

Abstract

This invention relates to a water immersion harmonic nonlinear high-temperature aging test method and system for high-heat resistant steel, comprising: acquiring the dynamic response signal of a high-heat resistant steel sample in a high-temperature medium to generate a multi-dimensional harmonic feature dataset; extracting the harmonic distortion parameter set of the high-heat resistant steel sample based on the multi-dimensional harmonic feature dataset to construct a harmonic state evolution matrix; establishing a high-temperature aging state space based on the harmonic state evolution matrix, calculating the distribution density of historical failure events of the high-heat resistant steel sample in the high-temperature aging state space, and determining the aging early warning feature index set; obtaining the real-time aging state vector of the high-heat resistant steel sample based on the dynamic response signal of the high-heat resistant steel sample, matching the similarity of the real-time aging state vector with the aging early warning feature index set in the high-temperature aging state space, and outputting the real-time aging correlation degree; and determining the aging failure probability and test parameters of the high-heat resistant steel sample based on the real-time aging correlation degree, the harmonic distortion parameter set, and the harmonic state evolution matrix.

Description

Technical Field

[0001] This invention relates to the field of high-heat resistant steel testing technology, and in particular to a water immersion harmonic nonlinear high-temperature aging test method and system for high-heat resistant steel. Background Technology

[0002] Current testing methods for high-temperature aging of heat-resistant steels mostly focus on static performance testing. Examples include determining the fracture time of a sample under constant load through high-temperature creep stabilization tests, observing microstructural changes after aging through metallographic analysis, or detecting hardness changes during aging using a hardness tester. While these methods can obtain some performance parameters of the sample after aging, they cannot capture the dynamic response characteristics during the aging process in real time, making it difficult to reflect the aging evolution under the dynamic interaction between the high-temperature liquid medium and the sample. Furthermore, traditional testing methods often ignore the nonlinear characteristics of the aging process, such as the non-uniform change in the degree of harmonic signal distortion over time and the nonlinear correlation of parameter abrupt changes at different aging stages. This results in the judgment of the aging state being limited to a single dimension and failing to comprehensively characterize the actual aging degree of the sample.

[0003] Existing aging early warning mechanisms mostly rely on preset performance thresholds, issuing an alert only when a certain performance parameter (such as hardness or elongation) falls below the threshold. This approach has a significant lag, failing to identify risks in the early stages of aging or during periods of slow performance degradation. Furthermore, it does not incorporate statistical patterns from historical failure events, resulting in a lack of specificity and scientific rigor in setting early warning indicators. Simultaneously, most testing systems can only output qualitative assessments of the aging state, failing to quantify the probability of failure or provide adjustment schemes for test parameters. This limits the guiding value of test results for practical engineering applications, making it difficult to optimize test conditions based on dynamic changes during the testing process, and also failing to provide specific parameter references for subsequent maintenance and replacement of equipment components. Consequently, this impacts the safety and economic efficiency of using high-heat-resistant steel in critical equipment.

[0004] In water immersion testing environments, the fluidity and corrosiveness of the high-temperature liquid medium further exacerbate the aging process of high-heat resistant steel, making traditional testing methods less adaptable to the dynamic monitoring requirements under these complex conditions. For example, uneven temperature distribution in the water immersion environment can lead to localized differences in aging rates within the sample, which static testing methods cannot capture. Simultaneously, harmonic signals in water immersion environments are easily affected by medium disturbances, making it difficult for traditional linear analysis methods to effectively extract useful features, resulting in a significant decrease in testing accuracy. These problems make it difficult for existing testing technologies to meet the requirements for accurate, real-time, and comprehensive aging testing of high-heat resistant steel in high-temperature liquid media environments. A testing method that integrates dynamic response monitoring, nonlinear characteristic analysis, and failure probability prediction is needed. Summary of the Invention

[0005] This invention provides a water immersion harmonic nonlinear high-temperature aging test method and system for high-heat resistant steel, to solve the problem that existing testing technologies for high-temperature aging of high-heat resistant steel are unable to meet the requirements for accurate, real-time and comprehensive aging tests of high-heat resistant steel in high-temperature liquid media environments.

[0006] A first aspect of this invention provides a water immersion harmonic nonlinear high-temperature aging test method for high-heat resistant steel, comprising the following steps: acquiring real-time dynamic response signals of a high-heat resistant steel sample in a high-temperature liquid medium, and generating a multi-dimensional harmonic feature dataset based on the real-time dynamic response signals; extracting a harmonic distortion parameter set of the high-heat resistant steel sample based on the multi-dimensional harmonic feature dataset, and constructing a harmonic state evolution matrix based on the harmonic distortion parameter set; establishing a high-temperature aging state space based on the harmonic state evolution matrix, calculating the distribution density of historical failure events of the high-heat resistant steel sample in the high-temperature aging state space, and determining an aging warning feature index set based on the distribution density; obtaining a real-time aging state vector of the high-heat resistant steel sample based on the dynamic response signals of the high-heat resistant steel sample, and performing spatial similarity matching between the real-time aging state vector and the aging warning feature index set in the high-temperature aging state space, and outputting a real-time aging correlation degree; and determining the aging failure probability and test parameters of the high-heat resistant steel sample based on the real-time aging correlation degree, the harmonic distortion parameter set, and the harmonic state evolution matrix.

[0007] A second aspect of this invention provides a water immersion harmonic nonlinear high-temperature aging test system for high-heat resistant steel, comprising: a data acquisition module for acquiring real-time dynamic response signals of a high-heat resistant steel sample in a high-temperature liquid medium, and generating a multi-dimensional harmonic feature dataset based on the real-time dynamic response signals; a feature processing module for extracting a set of harmonic distortion parameters of the high-heat resistant steel sample based on the multi-dimensional harmonic feature dataset, and constructing a harmonic state evolution matrix based on the harmonic distortion parameter set; and a construction module for establishing a high-temperature aging state space based on the harmonic state evolution matrix, and calculating the historical failure events of the high-heat resistant steel sample in high-temperature aging conditions. The system includes a distribution density in the high-temperature aging state space, and a set of aging early warning feature indicators based on the distribution density; a similarity matching module, used to obtain the real-time aging state vector of the high-temperature resistant steel sample based on the dynamic response signal of the high-temperature resistant steel sample, and to perform spatial similarity matching between the real-time aging state vector and the set of aging early warning feature indicators in the high-temperature aging state space, outputting the real-time aging correlation degree; and a probability prediction and test parameter generation module, used to determine the aging failure probability and test parameters of the high-temperature resistant steel sample based on the real-time aging correlation degree, the harmonic distortion parameter set, and the harmonic state evolution matrix.

[0008] A third aspect of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the high-temperature aging test method for high-heat resistant steel by water immersion harmonic nonlinearity as described in the above embodiments.

[0009] A fourth aspect of the present invention provides a computer program product having a computer program stored thereon, which is executed by a processor to implement the water immersion harmonic nonlinear high-temperature aging test method for high-heat resistant steel as described in the above embodiments.

[0010] In the above embodiments, real-time dynamic response signals of high-heat resistant steel samples in high-temperature liquid media are collected, and a multi-dimensional harmonic feature dataset is generated based on the real-time dynamic response signals. The harmonic distortion parameter set of the high-heat resistant steel samples is extracted based on the multi-dimensional harmonic feature dataset, and a harmonic state evolution matrix is ​​constructed based on the harmonic distortion parameter set. A high-temperature aging state space is established based on the harmonic state evolution matrix, and the distribution density of historical failure events of the high-heat resistant steel samples in the high-temperature aging state space is calculated. An aging early warning feature index set is determined based on the distribution density. The real-time aging state vector of the high-heat resistant steel samples is obtained based on the dynamic response signals of the samples, and spatial similarity matching is performed between the real-time aging state vector and the aging early warning feature index set in the high-temperature aging state space to output the real-time aging correlation. The aging failure probability and test parameters of the high-heat resistant steel samples are determined based on the real-time aging correlation, the harmonic distortion parameter set, and the harmonic state evolution matrix. This solves the problem that existing testing technologies for high-temperature aging of heat-resistant steel cannot meet the needs of accurate, real-time, and comprehensive aging tests in high-temperature liquid media environments. The quantified aging failure probability provides users with more specific risk assessment data, facilitating the development of targeted maintenance or replacement plans based on the failure probability. Simultaneously, the output of test parameter adjustment schemes can optimize test conditions based on real-time changes in aging status, ensuring the accuracy and effectiveness of subsequent tests. It also provides a reference for adjusting the operating parameters of heat-resistant steel components in practical engineering, further expanding the application value of the testing system.

[0011] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0012] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart of a water immersion harmonic nonlinear high-temperature aging test method for high-heat resistant steel according to an embodiment of the present invention; Figure 2This is a flowchart illustrating the dynamic response data acquisition and multi-dimensional harmonic feature dataset generation according to an embodiment of the present invention; Figure 3 A flowchart illustrating the establishment of the temperature aging state space and the determination of the aging early warning feature index set according to an embodiment of the present invention; Figure 4 This is a comparison diagram of the calibration effect of the reference sample according to an embodiment of the present invention; Figure 5 This is a graph showing the variation of harmonic components with time under multi-field coupling of high-heat resistant steel according to an embodiment of the present invention. Figure 6 This is an example diagram of a water immersion harmonic nonlinear high-temperature aging test system for high-heat resistant steel according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0013] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0014] The following describes, with reference to the accompanying drawings, a water immersion harmonic nonlinear high-temperature aging test method and system for high-heat resistant steel according to embodiments of the present invention. Addressing the problem mentioned in the background art that existing testing techniques for high-temperature aging of high-heat resistant steel are insufficient to meet the requirements for accurate, real-time, and comprehensive aging tests of high-heat resistant steel in a high-temperature liquid medium environment, the present invention provides a water immersion harmonic nonlinear high-temperature aging test method for high-heat resistant steel. In this method, the real-time dynamic response signal of the high-heat resistant steel sample in a high-temperature liquid medium is acquired, and a multi-dimensional harmonic feature dataset is generated based on the real-time dynamic response signal; the harmonic distortion parameter set of the high-heat resistant steel sample is extracted based on the multi-dimensional harmonic feature dataset, and a harmonic state evolution matrix is ​​constructed based on the harmonic distortion parameter set; A high-temperature aging state space is established based on the harmonic state evolution matrix. The distribution density of historical failure events of high-heat resistant steel samples in the high-temperature aging state space is calculated, and the aging early warning feature index set is determined based on the distribution density. The real-time aging state vector of the high-heat resistant steel sample is obtained based on the dynamic response signal of the sample. Spatial similarity matching is performed between the real-time aging state vector and the aging early warning feature index set in the high-temperature aging state space to output the real-time aging correlation. The aging failure probability and test parameters of the high-heat resistant steel sample are determined based on the real-time aging correlation, the harmonic distortion parameter set, and the harmonic state evolution matrix. This solves the problem that existing testing technologies for high-temperature aging of high-heat resistant steel cannot meet the needs of accurate, real-time, and comprehensive aging testing of high-heat resistant steel in high-temperature liquid media environments. The quantified aging failure probability can provide users with more specific risk assessment basis, facilitating the development of targeted maintenance or replacement plans based on the failure probability. At the same time, the output of the test parameter adjustment scheme can optimize test conditions according to real-time aging state changes, ensuring the accuracy and effectiveness of subsequent tests, and also providing a reference for adjusting the operating parameters of high-heat resistant steel components in actual engineering, further expanding the application value of the testing system.

[0015] Specifically, Figure 1 This is a schematic flowchart of a water immersion harmonic nonlinear high-temperature aging test method for high-heat resistant steel provided in an embodiment of the present invention.

[0016] like Figure 1 As shown, the water immersion harmonic nonlinear high-temperature aging test method for high-heat resistant steel includes the following steps: In step S101, the real-time dynamic response signal of the high-heat resistant steel sample in the high-temperature liquid medium is collected, and a multi-dimensional harmonic feature dataset is generated based on the real-time dynamic response signal. Optionally, in some embodiments, the real-time dynamic response signal of the high-heat resistant steel sample in a high-temperature liquid medium is acquired, and a multi-dimensional harmonic feature dataset is generated based on the real-time dynamic response signal. This includes: synchronously acquiring the real-time dynamic response signal of the high-heat resistant steel sample through a multi-channel sensor array in a high-temperature liquid medium environment; filtering the real-time dynamic response signal by amplitude threshold to remove abnormal data segments that exceed a preset reasonable fluctuation range; and classifying and integrating the filtered dynamic response signals according to the type of physical quantity to generate a multi-dimensional harmonic feature dataset.

[0017] Specifically, such as Figure 2 As shown, a multi-channel sensor array is directly installed in a high-temperature liquid medium environment. The array contains various types of sensors to capture different physical phenomena. For example, a high-frequency accelerometer is used to monitor the mechanical vibration of the sample itself, a dynamic pressure sensor is used to sense the hydrodynamic effect of the liquid medium on the sample surface, and distributed fiber optic sensors are deployed along the sample surface to measure the thermal strain distribution. All sensor elements are encapsulated in high-temperature resistant and corrosion-resistant materials to ensure the stability and reliability of the signals under long-term immersion in the high-temperature liquid medium. The multi-channel data acquisition unit is connected to the sensor array. The acquisition unit synchronously records the raw voltage or current signals of all channels at an extremely high sampling rate. This synchronization is crucial for the phase relationship between different physical quantities in subsequent analysis. The precise alignment of the timestamps is controlled by a unified clock signal.

[0018] The acquired raw dynamic response data stream first enters the preprocessing stage for amplitude threshold filtering. The system presets a reasonable fluctuation range based on the material's mechanical properties, liquid medium characteristics, and test conditions. If the instantaneous amplitude of any data point exceeds the upper or lower limit of this range, the system will automatically identify it and mark that data point and the continuous data segments within a specific time window before and after it as abnormal. The marked abnormal data segments are temporarily removed, and the time point of their occurrence and possible causes are recorded in the data log to ensure that the dynamic response data used for analysis is within the expected physically reasonable range.

[0019] The clean data, after screening, were classified and integrated according to their physical source and characterization significance. The frequency components related to structural vibration extracted from the acceleration sensor signals were classified as material deformation harmonic parameters, the fluid fluctuation characteristics reflected by the dynamic pressure sensor signals were classified as liquid medium corrosion harmonic parameters, and the periodic thermal stress components derived from strain and temperature fluctuation data were classified as thermal stress harmonic parameters, thus obtaining a multi-dimensional harmonic feature dataset.

[0020] In step S102, the harmonic distortion parameter set of the high-heat resistant steel sample is extracted based on the multi-dimensional harmonic feature dataset, and the harmonic state evolution matrix is ​​constructed based on the harmonic distortion parameter set.

[0021] Optionally, in some embodiments, a harmonic distortion parameter set of the high-heat resistant steel sample is extracted based on a multi-dimensional harmonic feature dataset, and a harmonic state evolution matrix is ​​constructed based on the harmonic distortion parameter set. This includes: performing nonlinear mode decomposition on the multi-dimensional harmonic feature dataset to obtain the decomposed fundamental component and harmonic components of each order; arranging the decomposed fundamental component and harmonic components of each order according to the frequency dimension to form a harmonic distortion parameter set; calculating the nonlinear evolution rate of the parameters in the harmonic distortion parameter set within a continuous test period; and arranging the nonlinear evolution rate according to the test time series to construct a harmonic state evolution matrix representing different harmonic parameters and column-based time series data.

[0022] The multidimensional harmonic feature dataset then undergoes nonlinear eigenvalue decomposition to extract the harmonic components characterizing the nonlinear damage behavior of the material. An adaptive signal decomposition algorithm is used to analyze each type of harmonic time series in the dataset. The algorithm can decompose complex signals into a series of intrinsic mode components based on the characteristic scale of the data itself.

[0023] The decomposition process identifies the most prominent low-frequency fluctuations in the signal as the fundamental component. The fundamental component represents the dominant dynamic characteristics of the system under ideal linear response. Components with frequencies higher than the fundamental frequency are identified as second, third, and even higher-order harmonic components.

[0024] Each decomposition operation is performed on a continuous time period, thereby obtaining the trajectory of the fundamental wave and each harmonic component changing over time. The amplitude and phase information of the fundamental wave component and the amplitude and phase information of each harmonic component obtained at each time point are arranged and combined in order of frequency from low to high to form a comprehensive set of harmonic distortion parameters. The set of harmonic distortion parameters quantifies the degree of deviation of the actual response signal from the ideal linear response.

[0025] The harmonic distortion parameter set contains information about the aging state of materials, but its dynamic evolution requires further processing to reveal. The instantaneous rate of change of each parameter in the harmonic distortion parameter set over consecutive, equally spaced test periods is calculated. Due to the nonlinear nature of the material aging process, parameter changes are often not uniform; their rate of change itself evolves over time, and this rate of change is called the nonlinear evolution rate. The nonlinear evolution rate is calculated by numerical differentiation of the parameter's time series or by finding the derivative through local curve fitting.

[0026] After obtaining the nonlinear evolution rates of all parameters at different time points, they are organized according to a structure: each row of the matrix corresponds to a fixed harmonic distortion parameter, and each column of the matrix corresponds to a continuous test time point. The resulting two-dimensional data table is the harmonic state evolution matrix. The harmonic state evolution matrix maps the aging process in the time dimension to a high-dimensional feature space composed of the change rates of harmonic parameters. Each element in the matrix represents the rate of change of a certain parameter at a specific moment, thus fully describing the dynamic characteristics of the aging process.

[0027] The construction of the harmonic state evolution matrix is ​​an ongoing process, accompanying data acquisition and analysis throughout the entire testing cycle. The matrix data is continuously updated, with new time-point data added to the matrix columns, ensuring that the harmonic state evolution matrix reflects the entire evolution history from the start of the test to the current moment. The completeness and accuracy of the harmonic state evolution matrix directly affect the subsequent construction of the high-temperature aging state space and the reliability of aging state assessment, thus occupying a central position in the entire system. All algorithm parameters involved in data processing, such as the stopping criteria for the decomposition algorithm and the window size used for differentiation, need to be pre-calibrated and optimized according to the specific material-medium system and testing conditions to ensure the consistency of the analysis results and the clarity of their physical meaning.

[0028] Specifically, the completeness and accuracy of the harmonic state evolution matrix are the foundation for constructing the high-temperature aging state space and conducting reliable aging state assessments, because the matrix fully records the nonlinear relationship of the harmonic distortion parameters of the high-heat resistant steel sample evolving over time during the testing process.

[0029] To ensure the integrity of the matrix, the system synchronously acquires dynamic response data through a multi-channel sensor array in a high-temperature liquid medium environment during the data acquisition phase. Abnormal data segments that exceed the preset reasonable fluctuation range are filtered out by amplitude threshold, thereby ensuring the integrity and quality of the input data.

[0030] After generating a multi-dimensional harmonic feature dataset, the stopping criteria for the nonlinear feature decomposition algorithm need to be pre-calibrated based on the specific material-medium system and test conditions. For example, the sufficiency standard for harmonic component separation can be determined by analyzing historical test data to avoid insufficient or excessive decomposition and ensure effective separation of the fundamental wave from each order of harmonic components. The window size used for differentiation needs to be optimized based on the test cycle and signal variation characteristics. For example, the window size can be adjusted according to the sampling frequency and the characteristic time scale of the material aging process to ensure that the calculation of the nonlinear evolution rate can accurately capture the aging trend without introducing noise. The calibration and optimization of these algorithm parameters are achieved through calibration experiments for specific material-medium combinations. The parameter settings are adjusted using historical data with known aging behavior to adapt the data processing flow to the actual test environment, thereby ensuring that the harmonic state evolution matrix can accurately reflect the aging dynamics and provide a reliable basis for the subsequent construction of the high-temperature aging state space and aging early warning.

[0031] In step S103, a high-temperature aging state space is established based on the harmonic state evolution matrix, the distribution density of historical failure events of high-heat resistant steel samples in the high-temperature aging state space is calculated, and the aging early warning characteristic index set is determined based on the distribution density.

[0032] Optionally, in some embodiments, a high-temperature aging state space is established based on the harmonic state evolution matrix, the distribution density of historical failure events of high-heat resistant steel samples in the high-temperature aging state space is calculated, and an aging warning feature index set is determined based on the distribution density. This includes: determining the dimension of the high-temperature aging state space based on the number of harmonic parameters in the harmonic state evolution matrix; statistically analyzing the spatial aggregation degree of feature points corresponding to historical failure events in the high-temperature aging state space to obtain the aging state distribution density value; and determining the harmonic parameters corresponding to feature points in the aging state distribution density value that are greater than the warning boundary to obtain the aging warning feature index set.

[0033] Specifically, such as Figure 3 As shown, the number of columns in the harmonic state evolution matrix corresponds to the number of completed test time points, while the number of rows corresponds to the number of harmonic distortion parameters being analyzed. The dimension of the high-temperature aging state space is strictly equal to the number of harmonic distortion parameters. Each harmonic distortion parameter, such as the amplitude nonlinear evolution rate of the fundamental component, the amplitude nonlinear evolution rate of the second harmonic component, and the phase nonlinear evolution rate of the third harmonic component, is independently defined as a coordinate axis in the high-temperature aging state space. Assuming that the harmonic distortion parameter set contains n independent parameters, then the high-temperature aging state space is an n-dimensional vector space. Any point in the space can be uniquely represented by a vector containing n components. Each component value of this vector represents the specific numerical value of the nonlinear evolution rate of the corresponding harmonic distortion parameter at a specific moment.

[0034] The coordinate axes of the high-temperature aging state space need to have clear physical meaning and dimensions. The harmonic distortion parameters represented by each coordinate axis must be clearly defined and calculated in the previous data processing stage. The scale range of the coordinate axes needs to be set based on historical test data or theoretical estimates to cover all possible nonlinear evolution rate values. In the high-temperature aging state space, the entire aging process from the start of the test to the current moment can be depicted as a trajectory line. Each point on this trajectory line corresponds to a column of data in the harmonic state evolution matrix, that is, the instantaneous mapping of the material's aging state in n-dimensional space at the corresponding moment.

[0035] The introduction of historical failure events provides a basis for judging the high-temperature aging state space. Historical failure events refer to test cases where, under the same or similar test conditions in the past, high-heat resistant steel specimens eventually experienced functional failure or reached a predetermined damage threshold. For each historical failure event, complete test process data is required, including dynamic response data from the initial state to the moment of failure. This historical data needs to undergo a processing procedure completely consistent with the current test: dynamic response data acquisition and screening, generation of a multi-dimensional harmonic feature dataset, nonlinear eigenvalue decomposition to obtain the harmonic distortion parameter set of the historical specimen, and finally, construction of the harmonic state evolution matrix of the historical specimen from start to failure. From the harmonic state evolution matrix of each historical failure event, a sequence of state vectors within a specific time window before failure is extracted. The length of this time window needs to be determined based on the aging characteristics of the material and the sampling frequency; its purpose is to capture the critical state characteristics before failure. All state vectors corresponding to all historical failure events within the critical time window are mapped to the established n-dimensional high-temperature aging state space. Each historical failure event will form a dense point cloud in the high-temperature aging state space. The position and shape of the point cloud reflect the common state characteristics before the occurrence of this type of failure mode.

[0036] Calculating the distribution density of feature points corresponding to historical failure events in the high-temperature aging state space requires multivariate statistical analysis. Kernel density estimation is a commonly used nonparametric method. It places a smooth kernel function at each historical data point and, by superimposing all kernel functions, obtains a probability density function that continuously varies across the entire high-temperature aging state space. The value of this probability density function shows a peak in regions where historical failure points are clustered, while the density value is lower in sparse data regions. The aging state distribution density obtained through kernel density estimation quantitatively describes the relative probability of a historical failure state occurring at any location in the high-temperature aging state space.

[0037] The determination of the aging warning feature index set directly depends on the distribution characteristics of the aging state distribution density values. In the high-temperature aging state space, a dynamic warning boundary is defined, which is essentially an isosurface of the aging state distribution density values. The density threshold corresponding to this isosurface can be determined statistically, for example, by selecting a density level that ensures a certain proportion (e.g., 95%) of historical failure event data points fall outside the area enclosed by this isosurface. The dynamic warning boundary delineates a high-risk region in n-dimensional space; the region inside this boundary represents a spatial domain highly similar to historical failure states.

[0038] The aging warning feature set consists of combinations of harmonic distortion parameters that play a decisive role in defining the dynamic warning boundary. Analyzing the main contributing dimensions of the dynamic warning boundary involves identifying which harmonic distortion parameters exhibit the most significant differences in nonlinear evolution rates when distinguishing between high-risk and safe states. The aging warning feature set can be defined as a collection of these key harmonic distortion parameters, or it can further include the typical value ranges or trend patterns of these parameters under critical states. Once determined, the aging warning feature set serves as a benchmark template for similarity matching in subsequent real-time monitoring. Maintaining the high-temperature aging state space is a dynamic process. As new historical failure event data accumulates, the aging state distribution density values ​​in the high-temperature aging state space need to be updated periodically, and the dynamic warning boundary and aging warning feature set adjusted accordingly. This iterative optimization mechanism enables the system's warning capability to continuously improve with data accumulation, more accurately reflecting the actual aging patterns of high-heat resistant steel under specific working conditions. The dimensions of the high-temperature aging state space are generally kept fixed after the initial system setup to ensure the consistency of the state vectors of test data from different periods, facilitating longitudinal comparison and analysis. Comparing the state vector obtained from real-time monitoring with the aging early warning feature index set in the high-temperature aging state space is essentially a pattern recognition process.

[0039] The calculation of real-time aging correlation relies on spatial distance metrics. The Euclidean distance between the real-time state vector and each feature point in the aging early warning feature index set is calculated. The shorter the distance, the more similar the real-time state is to a certain historical critical failure state. By taking a weighted average of all these distances or finding the minimum distance, the overall risk level of the real-time state can be comprehensively assessed.

[0040] Optionally, in some embodiments, before establishing the high-temperature aging state space based on the harmonic state evolution matrix, the process includes: setting up multiple sets of reference sample groups in a high-temperature liquid medium environment, and synchronously collecting the harmonic characteristic parameters of the reference sample groups as reference data; calibrating the deviation of the real-time harmonic characteristic parameters of the high-heat resistant steel sample based on the reference data, so as to establish the high-temperature aging state space according to the calibrated real-time harmonic characteristic parameters.

[0041] The testing accuracy of the water immersion harmonic nonlinear high-temperature aging test system for high-heat resistant steel is affected by the background fluctuations of the test environment and system noise. In order to effectively isolate and eliminate these common-mode interferences, multiple sets of reference samples are placed simultaneously with the high-heat resistant steel sample to be tested in a high-temperature liquid medium environment.

[0042] The reference sample set is made of materials with stable chemical composition, uniform microstructure, and known extremely slow or even negligible aging behavior under specific test conditions. Commonly used materials for reference sample sets include high-purity sintered alumina ceramics or nickel-based superalloys with specific compositions. The geometry, surface roughness, and contact with the liquid medium of the reference sample set should be as consistent as possible with the high-heat resistant steel sample to be tested. The installation position of the reference sample set within the test container must also be carefully selected to ensure that the thermodynamic environment it experiences is highly similar to that of the high-heat resistant steel sample. The reference sample set is also connected to a multi-channel sensor array with specifications completely identical to that of the high-heat resistant steel sample, and the data acquisition system acquires the dynamic response signals of the reference sample set in a completely synchronous manner.

[0043] The dynamic response signals acquired from the reference sample group undergo the same processing procedure as the high-heat resistant steel sample data. After preprocessing, the harmonic characteristic parameters of the reference sample group are extracted, and these parameters serve as benchmark reference data. The benchmark reference data essentially characterizes the signal characteristics that an ideal, stable sample should exhibit under the current testing environment. The benchmark reference data includes harmonic components caused by environmental factors such as furnace vibration, periodic fluctuations in the overall temperature of the medium, and power supply ripple.

[0044] Before calculating the real-time aging state vector of the high-heat resistant steel sample, a deviation calibration operation is required. This calibration compares the real-time harmonic characteristic parameters of the high-heat resistant steel sample with the baseline reference data of the reference sample group. A specific calibration method is to calculate the relative deviation of each harmonic characteristic parameter, for example, using the formula: Calibrated parameter = (High-heat resistant steel sample parameter - Mean of reference sample group parameters) / Standard deviation of reference sample group parameters. This baseline reference data is continuously updated as the test progresses, forming a dynamically changing background signal baseline.

[0045] The harmonic characteristic parameters of the high-heat resistant steel sample, after deviation calibration, are used to construct a real-time aging state vector. This calibrated real-time aging state vector more purely reflects the changes in the material state of the high-heat resistant steel sample itself, minimizing interference from environmental background noise. Subsequently, the calibrated real-time aging state vector is mapped to the high-temperature aging state space, and its real-time aging correlation with the aging early warning characteristic index set is calculated. This calibration mechanism significantly improves the sensitivity of the real-time aging correlation to the material's own aging state, reduces the risk of false alarms and false negatives, and enables the system to detect minor degradation of material properties earlier.

[0046] In step S104, the real-time aging state vector of the high-heat resistant steel sample is obtained based on the dynamic response signal of the high-heat resistant steel sample, and spatial similarity matching is performed between the real-time aging state vector and the aging warning feature index set in the high-temperature aging state space to output the real-time aging correlation.

[0047] Optionally, in some embodiments, a real-time aging state vector of the high-heat resistant steel sample is obtained based on the dynamic response signal of the high-temperature resistant steel sample, and spatial similarity matching is performed between the real-time aging state vector and the aging warning feature index set in the high-temperature aging state space to output the real-time aging correlation degree. This includes: extracting real-time harmonic feature parameters of the same dimension as the harmonic distortion parameter set from the high-temperature dynamic response signal to construct the real-time aging state vector; mapping the real-time aging state vector to the high-temperature aging state space; calculating the weighted average of the spatial distances between the real-time aging state vector and the feature points in the aging warning feature index set in the high-temperature aging state space; and outputting the real-time aging correlation degree.

[0048] The sensor probe of the high-frequency sampling module maintains close contact with the surface of the high-heat resistant steel sample or is at a predetermined optimal sensing distance. The high-frequency sampling module continuously captures the dynamic response signal of the high-heat resistant steel sample in a high-temperature liquid medium at a sampling rate several times higher than the highest harmonic frequency of interest. The dynamic response signal includes high-frequency vibration components excited by thermal shock, medium flow corrosion, and dislocation motion within the material.

[0049] The acquired raw analog signal is converted into a discrete-time digital signal by a high-precision analog-to-digital converter after passing through an anti-aliasing filter. The digital signal is then transmitted in real-time to the signal processor's buffer via a high-speed data bus. The real-time dynamic response signal enters the preprocessing stage. The preprocessing algorithm applies digital filtering to the input signal sequence, identical to that used in offline analysis, to suppress background noise and performs outlier detection and removal based on a sliding window. The preprocessed signal segment is then fed into the feature extraction engine, which uses lock-in amplification or a fast Fourier transform algorithm to perform spectral analysis on the signal within the current time window. The spectral analysis results are used to accurately identify and extract the predefined fundamental component amplitude, harmonic component amplitude, and key phase angle information. The extracted real-time harmonic feature parameters must be completely consistent with the parameter definitions used when constructing the harmonic distortion parameter set in terms of type, quantity, and physical meaning. These real-time parameters are organized into a multi-dimensional vector, called the real-time aging state vector, according to a fixed index order. The dimension of the real-time aging state vector strictly corresponds to the dimension of the high-temperature aging state space.

[0050] Placing the real-time aging state vector in the high-temperature aging state space requires coordinate mapping. The coordinate axes of the high-temperature aging state space represent the nonlinear evolution rate of harmonic distortion parameters rather than the instantaneous values ​​of the parameters. The mapping process requires converting the instantaneous observations contained in the real-time aging state vector into rate of change information. The system uses the real-time harmonic characteristic parameter values ​​of the current moment and the previous few sampling moments to numerically calculate the instantaneous rate of change of each parameter at the current moment through the first-order difference method or the central difference method. Only this newly generated vector composed of instantaneous rate of change is compatible with the coordinate definition of the high-temperature aging state space, thus uniquely determining the position of the real-time state point in the high-temperature aging state space.

[0051] The spatial similarity matching process calculates the proximity between real-time state points and the risk areas represented by the aging warning feature index set. The aging warning feature index set consists of a set of feature points in the high-temperature aging state space, with each feature point corresponding to a critical state of a historical failure event. The core of the matching is calculating the generalized distance between the real-time state point and each feature point in the aging warning feature index set. Since parameters in different dimensions of the high-temperature aging state space may have different physical dimensions, directly calculating the Euclidean distance will lead to a dimensional mismatch problem. To solve this problem, a standardized weighted Euclidean distance formula is used:

[0052] Among them, symbols This represents the real-time status point obtained after standardization and the aging early warning feature index set. The dimensionless generalized distance between feature points, denoted by _ ... Represents the total number of dimensions in the high-temperature aging state space, symbol It is to give the first Each dimension has a weighting coefficient, which can be adjusted based on the sensitivity of that dimension to failure. (Symbol) Indicates the real-time state point at the 1st The coordinate value in the i-th dimension, i.e., the i-th The nonlinear evolution rate of each harmonic distortion parameter at the current moment, sign The first in the set of aging early warning characteristic indicators The feature point at the th ... Coordinate values ​​in each dimension, symbol It is the first The standardization factor for each dimension parameter is typically the standard deviation of that parameter over a large amount of historical data (including normal and pre-failure states). The difference term in the formula is divided by the standard deviation. The operation achieves dimensionless scaling, ensuring that each term within the summation and the final distance are dimensionless. All of them are dimensionless values.

[0053] Calculate the generalized distance between the real-time status point and each feature point in the aging early warning feature index set. Next, these distance values ​​need to be aggregated into a single real-time aging correlation index. The aggregation function adopts the form of an exponential kernel function: ,in It is the total number of characteristic points in the set of aging early warning characteristic indicators. Represents the minimum generalized distance from the real-time status point to all warning feature points, with the symbol... It is a positive proportionality coefficient used to control the rate at which distance affects the decay of correlation. Real-time aging correlation. It is a dimensionless value between 0 and 1, representing the real-time aging correlation. The closer the value is to 1, the more similar the real-time aging state is to a certain historical critical failure state, and the higher the current aging risk level of the material.

[0054] The entire real-time monitoring and correlation calculation process runs periodically at a fixed pace. The high-frequency sampling module continuously acquires new dynamic response data, and the real-time aging state vector is continuously updated, resulting in real-time aging correlation. It also changes dynamically. The system will monitor the aging correlation in real time. The value is compared with multiple preset threshold levels, and the real-time aging correlation is determined. Exceeding the primary threshold may trigger log enhancement, and real-time aging correlation may also be affected. Exceeding a high threshold may activate an audible and visual alarm and initiate a high-temperature aging probability prediction model. This design enables continuous, online, and quantitative assessment of the aging state of high-heat-resistant steel samples, providing direct data support for predictive maintenance and testing process optimization. The computational workflow has been optimized to ensure a high degree of correlation between data acquisition and real-time aging. The output delay is within the time limit required by the system.

[0055] In step S105, the aging failure probability and test parameters of the high-heat resistant steel sample are determined based on the real-time aging correlation, harmonic distortion parameter set, and harmonic state evolution matrix.

[0056] Optionally, in some embodiments, the aging failure probability and test parameters of the high-heat resistant steel sample are determined based on the real-time aging correlation, harmonic distortion parameter set, and harmonic state evolution matrix. This includes: inputting the real-time aging correlation and harmonic distortion parameter set into a preset high-temperature aging probability prediction model; calculating the nonlinear evolution acceleration of harmonic parameters in the current test stage based on the harmonic state evolution matrix; predicting the aging failure probability of the high-heat resistant steel sample in the remaining test cycle based on the nonlinear evolution acceleration; and generating test parameters including temperature gradient adjustment, liquid medium flow rate correction, and mechanical load compensation when the aging failure probability is greater than a preset critical value.

[0057] Real-time aging correlation is a scalar value between zero and one. The value of the real-time aging correlation directly reflects the similarity between the current state of the high-heat resistant steel sample and the critical state recorded in the historical failure database. The harmonic distortion parameter set provides more detailed information about the current damage mode of the material. For example, an abnormal increase in the amplitude of the second harmonic may indicate the initiation of microcracks, while the drift of the fundamental phase angle may be related to the change in the dislocation density inside the material.

[0058] The high-temperature aging probability prediction model is a machine learning-based regression model trained on a large amount of historical test data. This data includes the complete harmonic evolution sequence from the initial state of the sample to its final failure, along with the corresponding real-time aging correlation curves. The training process uses a supervised learning algorithm. Each sample in the training dataset consists of an input feature vector and a label. The input feature vector contains the real-time aging correlation calculated at a specific historical time point, and all harmonic distortion parameter values ​​extracted at that time point. The label represents the remaining service life of the sample after that moment, or a binary identifier. Through training, the high-temperature aging probability prediction model learns a nonlinear mapping relationship from complex input features to future failure risk or remaining service life. When new real-time data is input, the model outputs an aging failure probability based on the learned pattern. This probability is a value between zero and one, quantifying the likelihood of functional failure of the high-heat resistant steel sample within a predetermined testing period.

[0059] The harmonic state evolution matrix plays a role in providing trend information in the prediction. The system calculates the nonlinear evolution acceleration of each harmonic distortion parameter from the recent harmonic state evolution matrix. The nonlinear evolution acceleration is the rate of change of the evolution rate. The nonlinear evolution acceleration is approximated by calculating the difference of the nonlinear evolution rate at several consecutive time points. A parameter showing a positive nonlinear evolution acceleration means that its rate of change is accelerating, which often indicates that damage is accumulating at an accelerated rate. This accelerating trend will be captured by the high-temperature aging probability prediction model and regarded as a strong signal of increased risk.

[0060] For example, assuming the real-time aging correlation is at a moderate level, but if calculations reveal a sharp increase in the nonlinear evolution acceleration of a specific harmonic parameter characterizing grain boundary corrosion sensitivity, the high-temperature aging probability prediction model might adjust the final predicted aging failure probability upwards accordingly. The predicted aging failure probability is the direct basis for triggering the test parameter adjustment scheme. The system internally presets one or more probability thresholds, for example, setting 0.7 as a warning threshold and 0.9 as a critical threshold. When the aging failure probability output by the high-temperature aging probability prediction model exceeds the set warning threshold, the system begins generating a test parameter adjustment scheme. The test parameter adjustment scheme is a set of specific control instructions designed to intervene in the aging process by changing the test environment. The purpose of this intervention might be to delay failure to complete long-term data acquisition, or to enhance a specific damage mechanism for research.

[0061] As shown in Table 1, the test parameter adjustment scheme usually includes the adjustment amount of several core parameters: the adjustment amount of ambient temperature gradient, the correction value of liquid medium flow rate, and the compensation amount of external mechanical load.

[0062] Table 1

[0063] The table shows a set of adjustment parameters corresponding to different aging failure probability ranges. For example, when the aging failure probability falls within the range of 0.8 to 0.9, the system may generate instructions to reduce the temperature gradient in the test chamber by 20 degrees Celsius, increase the circulation velocity of the liquid medium by 0.1 m / s, and reduce the mechanical load applied to the sample by 10 MPa. Reducing the temperature gradient and mechanical load aims to reduce the driving forces of thermal stress and fatigue damage, while increasing the medium flow rate can enhance the mass transfer process, potentially helping to mitigate localized corrosion or making corrosion products easier to remove, thereby delaying the damage process.

[0064] Optionally, in some embodiments, when generating test parameters including temperature gradient adjustment, liquid medium flow rate correction, and mechanical load compensation, the following steps are taken: establishing a mapping relationship library between the microstructure changes of the high-heat resistant steel sample and harmonic characteristic parameters; when the real-time aging correlation is greater than or equal to a preset threshold, matching the corresponding material lattice distortion type from the mapping relationship library; and optimizing the liquid medium composition ratio in the test parameters according to the material lattice distortion type.

[0065] The system's decision-making capability is further optimized by introducing a mapping relationship library between changes in material microstructure and harmonic characteristic parameters. The mapping relationship library is established through correlation analysis of offline material characterization and online harmonic monitoring data. The mapping relationship library records the correspondence between harmonic characteristic modes and specific material micro-damage mechanisms. For example, a significant increase in harmonic energy within a specific frequency range may be associated with coarsening of precipitates inside the material, while a phase anomaly in another frequency band may point to the initiation of grain boundary oxidation cracks.

[0066] When the real-time aging correlation reaches a certain threshold, the system not only calculates the overall aging failure probability but also analyzes which parameter(s) in the current harmonic distortion parameter set deviate most significantly from the normal range. It then searches the mapping database for the material lattice distortion type or damage mechanism most commonly associated with these abnormal parameters. Based on the identified dominant damage mechanism, the test parameter adjustment scheme can be precisely customized. For example, if the system identifies sulfide stress corrosion cracking as the dominant damage mechanism, which is highly sensitive to the sulfur ion concentration and tensile stress level in the liquid medium, the generated test parameter adjustment scheme will further optimize the composition ratio of the liquid medium based on general adjustments. For instance, it might instruct the auxiliary system to add a trace amount of sulfur fixative to the liquid medium to reduce the concentration of active sulfur ions. Simultaneously, the mechanical load compensation may focus more on reducing the tensile stress component. This precise adjustment based on mechanism understanding, compared to simple parameter threshold control, can more effectively intervene in the specific aging process, improving the scientific rigor of the test and the interpretability of the results.

[0067] Optionally, in some embodiments, when determining the aging failure probability and test parameters of the high-heat resistant steel sample based on the real-time aging correlation, harmonic distortion parameter set, and harmonic state evolution matrix, the method includes: constructing a test environment steady-state index; and stopping the prediction of the aging failure probability of the high-heat resistant steel sample when the test environment steady-state index is less than a preset standard.

[0068] The stability of the test environment is another crucial prerequisite for obtaining reliable aging assessment results. The system quantifies the thermodynamic stability of the high-temperature liquid medium environment by constructing a comprehensive test environment steady-state index. The test environment steady-state index is not a single physical quantity measurement; rather, it is a dimensionless index calculated by integrating multiple key environmental parameters. Parameters used to calculate the test environment steady-state index typically include the temperature fluctuation variance in the core region of the liquid medium, the stability coefficient of the medium circulation velocity, the monitoring range of key corrosive ion concentrations (such as chloride and oxygen ions), and the fluctuation of heating power. These parameters are measured independently by their respective sensors and fused into a single test environment steady-state index using a multivariate statistical process control model or a weighted geometric mean model. A higher test environment steady-state index value indicates a more stable test environment.

[0069] The system sets a preset standard value for the steady-state index of the test environment, which is a threshold determined based on a large amount of historical stable test data. During the test, the system calculates the steady-state index of the test environment in real time and compares it with the preset standard value. When the steady-state index of the test environment is lower than the preset standard, it indicates that the current test environment is in an unstable transient state, such as power fluctuations in the heating element, abnormal operation of the medium circulation pump, or decreased efficiency of the cooling system. The dynamic response signal collected under such unstable conditions contains a large number of distortions caused by non-material factors, and the harmonic characteristic parameters and real-time aging correlation calculated based on these signals will lose reliability. Once the steady-state index of the test environment is detected to be lower than the preset standard, the system will immediately suspend the operation of the high-temperature aging probability prediction model and data recording, and simultaneously initiate an automatic environmental parameter rebalancing process.

[0070] The environmental parameter rebalancing process triggers a diagnostic procedure. This procedure analyzes which environmental parameters (or parameters) caused the decrease in the test environment's steady-state index and executes pre-set corrective measures. These measures may include activating a backup heater to stabilize the temperature, adjusting the circulation pump speed to stabilize the flow rate, or adding chemical reagents to the media replenishment tank to stabilize the composition. After the environmental parameter rebalancing process is complete, the system continuously monitors the test environment's steady-state index until it is confirmed to have stably recovered to above the pre-set standard and maintained for a period of time. At this point, the system does not simply continue testing from the point of interruption but executes a state recovery sequence. This sequence includes reloading the harmonic state evolution matrix from before the interruption, evaluating the system's transient response using data collected during the interruption but marked as invalid, smoothly connecting the latest valid data with the historical data sequence, and reinitializing the high-temperature aging probability prediction model. This mechanism maximizes the continuity of test data and the consistency of aging state assessments, ensuring the scientific validity and comparability of the final experimental results even in the face of unavoidable environmental disturbances.

[0071] like Figure 4 As shown in the figure, this diagram revolves around optimizing test accuracy, with the core being the isolation of environmental interference by using a reference sample group. The upper and lower subplots respectively present the changes in harmonic parameters over test time for uncalibrated and calibrated samples: the uncalibrated curve has a higher baseline due to environmental noise from furnace vibration, medium temperature fluctuations, etc., and its fluctuations are affected by non-material factors; after calibration, the system calculates the deviation between the harmonic parameters of the high-heat resistant steel sample under test and the baseline data of the reference sample group, effectively eliminating environmental noise. The curve baseline more closely matches the actual aging response of the material, and the fluctuations more accurately reflect the performance degradation law of the high-heat resistant steel itself. This calibration mechanism significantly improves the accuracy of aging state assessment, providing more reliable basic data for subsequent real-time aging correlation calculations and failure probability predictions.

[0072] This invention provides a water immersion harmonic nonlinear high-temperature aging test method for high-heat resistant steel. It utilizes a multi-channel sensor array deployed in a high-temperature liquid medium environment to simultaneously acquire dynamic response data of high-heat resistant steel samples under the coupling of multiple fields of heat, fluid and chemical. After preprocessing, these data are used to generate a multi-dimensional harmonic feature dataset containing multiple physical quantities such as thermal stress, medium corrosion and material deformation.

[0073] Nonlinear eigenvalue decomposition was performed on the dataset to separate the fundamental frequency and harmonic components of each order. The harmonic amplitudes of each harmonic component are as follows: Figure 5 As shown, a set of harmonic distortion parameters is formed, and based on the nonlinear relationship of the evolution of this parameter set over time, a harmonic state evolution matrix with time as the sequence is constructed. This matrix defines the trajectory of the material aging process in a high-dimensional feature space.

[0074] Based on this harmonic state evolution matrix, a high-temperature aging state space can be established. By calculating the distribution density of historical failure events in this space, the critical state characteristics that indicate the material is close to failure can be determined, i.e., the aging early warning characteristic index set.

[0075] During the real-time testing phase, the system continuously monitors the dynamic response signal of the sample, extracts real-time harmonic characteristic parameters and forms a real-time aging state vector. By calculating the spatial similarity between this vector and the aging warning characteristic index set in the high-temperature aging state space, a quantified real-time aging correlation is output.

[0076] The system integrates real-time aging correlation, current harmonic distortion parameter set, and harmonic state evolution matrix reflecting historical evolution patterns to construct a high-temperature aging probability prediction model. This model not only outputs the aging failure probability of the sample in future test cycles, but also automatically generates adjustment schemes for key test parameters such as temperature, medium flow rate, and load when the probability exceeds a threshold, so as to achieve optimized control of the test process, extend the effectiveness of the test, or accelerate the study of specific damage modes.

[0077] Compared with the prior art, the beneficial effects of the present invention are: (1) In the data acquisition stage, the system focuses on the real-time dynamic response data of high-heat resistant steel samples in high-temperature liquid media, rather than relying on traditional static performance parameters. In the high-temperature liquid media environment, the aging process of the sample is accompanied by dynamic physicochemical effects, such as the dynamic reaction between medium ions and the sample surface, and the dynamic release of internal stress of the sample. Real-time dynamic response data can completely record the signal changes under these dynamic effects, and can more realistically reflect the evolution trajectory of the aging process than static data, avoiding the information loss caused by static sampling, and providing more comprehensive basic data for subsequent aging state analysis.

[0078] (2) In the feature processing stage, the system extracts the harmonic distortion parameter set through nonlinear feature decomposition and constructs the harmonic state evolution matrix. During the aging process, the changes in the physical properties of high-heat resistant steel will cause nonlinear distortion in the harmonic signals it generates. This distortion is closely related to the degree of aging, and traditional linear analysis methods cannot accurately capture such nonlinear features. The nonlinear feature decomposition method of this system can accurately separate the harmonic distortion components at different aging stages. The extracted harmonic distortion parameter set can quantitatively characterize the nonlinear change law in the aging process. The harmonic state evolution matrix constructed based on the parameter set can transform the dynamic changes of the aging process into a quantifiable matrix form, clearly presenting the aging state correlation at different time points, and providing structured feature support for the subsequent state space construction.

[0079] (3) Regarding the construction of the state space and the determination of early warning indicators, the system establishes a high-temperature aging state space based on the harmonic state evolution matrix and determines the aging early warning characteristic indicator set by combining the distribution density of historical failure events. The establishment of the high-temperature aging state space integrates multi-dimensional harmonic characteristic parameters into a unified spatial model, realizing the visualization and quantifiable analysis of the aging state; at the same time, the introduction of the distribution density of historical failure events allows the determination of the early warning indicator set to fully combine past failure patterns, avoiding the subjectivity and lag of traditional threshold early warning. By analyzing the distribution pattern of historical failure events in the state space, the state areas prone to failure can be accurately located, and the determined early warning indicator set can identify state changes approaching the failure area in the early stage of aging, achieving earlier and more accurate aging early warning.

[0080] (4) In the real-time monitoring and similarity matching stage, the system extracts the dynamic response signal of the sample in real time to form a real-time aging state vector, and performs spatial similarity matching with the warning index set to output the real-time aging correlation. This real-time matching method can dynamically track the changing trend of the aging state. The state vector at each time node can be compared with the warning index set in real time. The correlation between the current aging state and the warning state is quantified by similarity calculation. Users can intuitively grasp the real-time aging progress of the sample and avoid risk omissions caused by delayed monitoring. It is especially suitable for complex working conditions such as high-temperature liquid media that accelerate aging.

[0081] (5) Regarding the output of prediction models and parameter adjustment schemes, the system combines real-time aging correlation, harmonic distortion parameter set, and evolution matrix to construct a high-temperature aging probability prediction model, and outputs the aging failure probability and test parameter adjustment scheme. Compared with traditional methods that can only qualitatively judge the aging state, the quantitative aging failure probability can provide users with more specific risk assessment basis, making it easier to formulate targeted maintenance or replacement plans based on the failure probability; at the same time, the output of test parameter adjustment schemes can optimize test conditions according to real-time changes in aging state. For example, when an abnormal aging rate is found, parameters such as test temperature and medium concentration can be adjusted to ensure the accuracy and effectiveness of subsequent tests, and also provide a reference for adjusting the operating parameters of high-heat resistant steel components in actual engineering, further expanding the application value of the test system.

[0082] Next, referring to the accompanying drawings, a water immersion harmonic nonlinear high-temperature aging test system for high-heat resistant steel according to an embodiment of the present invention is described.

[0083] Figure 6 This is a block diagram of a water immersion harmonic nonlinear high-temperature aging test system for high-heat resistant steel according to an embodiment of the present invention.

[0084] like Figure 6 As shown, the high-temperature aging test system 10 for high-heat resistant steel water immersion harmonic nonlinear high-temperature aging includes: an acquisition module 100, a feature processing module 200, a construction module 300, a similarity matching module 400, and a probability prediction and test parameter generation module 500.

[0085] The system comprises the following modules: Acquisition module 100, which acquires real-time dynamic response signals of high-heat resistant steel samples in a high-temperature liquid medium and generates a multi-dimensional harmonic feature dataset based on these signals; Feature processing module 200, which extracts a set of harmonic distortion parameters of the high-heat resistant steel samples based on the multi-dimensional harmonic feature dataset and constructs a harmonic state evolution matrix based on the harmonic distortion parameter set; Construction module 300, which establishes a high-temperature aging state space based on the harmonic state evolution matrix, calculates the distribution density of historical failure events of the high-heat resistant steel samples in the high-temperature aging state space, and determines an aging warning feature index set based on the distribution density; Similarity matching module 400, which obtains the real-time aging state vector of the high-heat resistant steel samples based on their dynamic response signals, performs spatial similarity matching between the real-time aging state vector and the aging warning feature index set in the high-temperature aging state space, and outputs the real-time aging correlation; and Probability prediction and test parameter generation module 500, which determines the aging failure probability and test parameters of the high-heat resistant steel samples based on the real-time aging correlation, the harmonic distortion parameter set, and the harmonic state evolution matrix.

[0086] Optionally, in some embodiments, the acquisition module 100 is specifically used to: synchronously acquire the real-time dynamic response signal of the high-heat resistant steel sample through a multi-channel sensor array in a high-temperature liquid medium environment; perform amplitude threshold screening on the real-time dynamic response signal to remove abnormal data segments that exceed the preset reasonable fluctuation range; and classify and integrate the screened dynamic response signals according to the type of physical quantity to generate a multi-dimensional harmonic feature dataset.

[0087] Optionally, in some embodiments, the feature processing module 200 is specifically used for: performing nonlinear mode decomposition on the multi-dimensional harmonic feature dataset to obtain the decomposed fundamental component and harmonic components of each order; arranging the decomposed fundamental component and harmonic components of each order according to the frequency dimension to form a harmonic distortion parameter set; calculating the nonlinear evolution rate of the parameters in the harmonic distortion parameter set within a continuous test period; and arranging the nonlinear evolution rate according to the test time series to construct a harmonic state evolution matrix representing different harmonic parameters and column-represented time series data.

[0088] Optionally, in some embodiments, the construction module 300 is specifically used to: determine the dimension of the high-temperature aging state space based on the number of harmonic parameters in the harmonic state evolution matrix; statistically analyze the spatial aggregation degree of feature points corresponding to historical failure events in the high-temperature aging state space to obtain the aging state distribution density value; and determine the harmonic parameters corresponding to feature points in the aging state distribution density value that are greater than the warning boundary to obtain the aging warning feature index set.

[0089] Optionally, in some embodiments, the similarity matching module 400 is specifically used to: extract real-time harmonic feature parameters of the same dimension as the harmonic distortion parameter set from the high-temperature dynamic response signal, construct a real-time aging state vector; map the real-time aging state vector to the high-temperature aging state space, calculate the weighted average of the spatial distances between the feature points in the high-temperature aging state space and the feature points in the aging warning feature index set, and output the real-time aging correlation degree.

[0090] Optionally, in some embodiments, the probability prediction and test parameter generation module 500 is specifically used to: input the real-time aging correlation degree and harmonic distortion parameter set into a preset high-temperature aging probability prediction model; calculate the nonlinear evolution acceleration of harmonic parameters in the current test stage based on the harmonic state evolution matrix; predict the aging failure probability of the high-heat resistant steel sample in the remaining test cycle based on the nonlinear evolution acceleration; and generate test parameters including temperature gradient adjustment, liquid medium flow rate correction value and mechanical load compensation when the aging failure probability is greater than a preset critical value.

[0091] Optionally, in some embodiments, when generating test parameters including temperature gradient adjustment, liquid medium flow rate correction, and mechanical load compensation, the probability prediction and test parameter generation module 500 is specifically used to: establish a mapping relationship library between the microstructure changes of the high-heat resistant steel sample and harmonic characteristic parameters; when the real-time aging correlation is greater than or equal to a preset threshold, match the corresponding material lattice distortion type from the mapping relationship library; and optimize the liquid medium composition ratio in the test parameters according to the material lattice distortion type.

[0092] Optionally, in some embodiments, before establishing the high-temperature aging state space based on the harmonic state evolution matrix, the construction module 300 is further configured to: set up multiple sets of reference sample groups in a high-temperature liquid medium environment, and simultaneously collect the harmonic characteristic parameters of the reference sample groups as reference data; calibrate the deviation of the real-time harmonic characteristic parameters of the high-heat resistant steel sample based on the reference data, so as to establish the high-temperature aging state space according to the calibrated real-time harmonic characteristic parameters.

[0093] Optionally, in some embodiments, when determining the aging failure probability and test parameters of the high-heat resistant steel sample based on the real-time aging correlation, harmonic distortion parameter set, and harmonic state evolution matrix, the probability prediction and test parameter generation module 500 is also used to: construct a test environment steady-state index; and stop predicting the aging failure probability of the high-heat resistant steel sample when the test environment steady-state index is less than a preset standard.

[0094] It should be noted that the foregoing explanation of the embodiment of the water immersion harmonic nonlinear high-temperature aging test method for high-heat resistant steel also applies to the water immersion harmonic nonlinear high-temperature aging test system for high-heat resistant steel in this embodiment, and will not be repeated here.

[0095] The high-temperature aging test system for high-heat resistant steel, proposed in this embodiment, acquires the real-time dynamic response signal of the high-heat resistant steel sample in a high-temperature liquid medium and generates a multi-dimensional harmonic feature dataset based on the real-time dynamic response signal. It extracts the harmonic distortion parameter set of the high-heat resistant steel sample based on the multi-dimensional harmonic feature dataset and constructs a harmonic state evolution matrix based on the harmonic distortion parameter set. It establishes a high-temperature aging state space based on the harmonic state evolution matrix, calculates the distribution density of historical failure events of the high-heat resistant steel sample in the high-temperature aging state space, and determines the aging early warning feature index set based on the distribution density. It obtains the real-time aging state vector of the high-heat resistant steel sample based on the dynamic response signal of the sample, and performs spatial similarity matching between the real-time aging state vector and the aging early warning feature index set in the high-temperature aging state space to output the real-time aging correlation degree. Finally, it determines the aging failure probability and test parameters of the high-heat resistant steel sample based on the real-time aging correlation degree, the harmonic distortion parameter set, and the harmonic state evolution matrix. This solves the problem that existing testing technologies for high-temperature aging of heat-resistant steel cannot meet the needs of accurate, real-time, and comprehensive aging tests in high-temperature liquid media environments. The quantified aging failure probability provides users with more specific risk assessment data, facilitating the development of targeted maintenance or replacement plans based on the failure probability. Simultaneously, the output of test parameter adjustment schemes can optimize test conditions based on real-time changes in aging status, ensuring the accuracy and effectiveness of subsequent tests. It also provides a reference for adjusting the operating parameters of heat-resistant steel components in practical engineering, further expanding the application value of the testing system.

[0096] Figure 7 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. The electronic device may include: The memory 701, the processor 702, and the computer program stored in the memory 701 and capable of running on the processor 702.

[0097] When the processor 702 executes the program, it implements the high-temperature aging test method for high-heat resistant steel by water immersion harmonic nonlinearity provided in the above embodiments.

[0098] Furthermore, electronic devices also include: Communication interface 703 is used for communication between memory 701 and processor 702.

[0099] The memory 701 is used to store computer programs that can run on the processor 702.

[0100] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0101] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0102] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0103] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0104] This invention also provides a computer program product, on which a computer program is stored, which, when executed by a processor, implements the above-described method for water immersion harmonic nonlinear high-temperature aging test of high-heat resistant steel.

[0105] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0106] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0107] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be specifically implemented in any computer program product for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer program product" can be any means that can contain, store, communicate, propagate, or transmit a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples of computer program products (a non-exhaustive list) include the following: an electrical connection having one or N wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic device, and portable optical disc read-only memory (CDROM). Furthermore, the computer program product can even be paper or other suitable medium on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0109] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0110] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer program product, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0111] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer program product.

[0112] The computer program product mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A water immersion method for high-temperature aging testing of high-heat resistant steel using harmonic nonlinearity, characterized in that, Includes the following steps: Real-time dynamic response signals of high-heat resistant steel samples in high-temperature liquid media are collected, and multi-dimensional harmonic feature datasets are generated based on the real-time dynamic response signals. Based on the multi-dimensional harmonic feature dataset, the harmonic distortion parameter set of the high-heat resistant steel sample is extracted, and the harmonic state evolution matrix is ​​constructed according to the harmonic distortion parameter set. A high-temperature aging state space is established based on the harmonic state evolution matrix. The distribution density of historical failure events of the high-heat resistant steel sample in the high-temperature aging state space is calculated, and the aging early warning characteristic index set is determined based on the distribution density. The real-time aging state vector of the high-heat resistant steel sample is obtained based on the dynamic response signal of the high-temperature resistant steel sample, and the spatial similarity matching between the real-time aging state vector and the aging early warning feature index set in the high-temperature aging state space is performed to output the real-time aging correlation. The aging failure probability and test parameters of the high-heat resistant steel sample are determined based on the real-time aging correlation, the harmonic distortion parameter set, and the harmonic state evolution matrix.

2. The method according to claim 1, characterized in that, The process involves acquiring real-time dynamic response signals of high-heat resistant steel samples in a high-temperature liquid medium, and generating a multi-dimensional harmonic feature dataset based on these signals, including: The real-time dynamic response signal of the high-heat resistant steel sample is synchronously acquired by a multi-channel sensor array in a high-temperature liquid medium environment. The real-time dynamic response signal is filtered by amplitude threshold to remove abnormal data segments that exceed the preset reasonable fluctuation range; The filtered dynamic response signals are classified and integrated according to the type of physical quantity to generate the multi-dimensional harmonic feature dataset.

3. The method according to claim 1, characterized in that, The step of extracting the harmonic distortion parameter set of the high-heat resistant steel sample based on the multi-dimensional harmonic feature dataset, and constructing a harmonic state evolution matrix based on the harmonic distortion parameter set, includes: Nonlinear mode decomposition is performed on the multidimensional harmonic feature dataset to obtain the fundamental component and harmonic components of each order after decomposition. The decomposed fundamental component and each order harmonic component are arranged according to the frequency dimension to form a harmonic distortion parameter set. Calculate the nonlinear evolution rate of the lumped parameters of the harmonic distortion parameters over a continuous test period; The nonlinear evolution rates are arranged according to the test time series to construct a harmonic state evolution matrix that represents different harmonic parameters and columns that represent time series data.

4. The method according to claim 1, characterized in that, The high-temperature aging state space is established based on the harmonic state evolution matrix. The distribution density of historical failure events of the high-heat resistant steel sample in the high-temperature aging state space is calculated, and the aging early warning characteristic index set is determined based on the distribution density, including: The dimension of the high-temperature aging state space is determined based on the number of harmonic parameters in the harmonic state evolution matrix. The spatial clustering degree of feature points corresponding to historical failure events within the high-temperature aging state space is statistically analyzed to obtain the aging state distribution density value. The harmonic parameters corresponding to the feature points in the aging state distribution density that are greater than the warning boundary are determined to obtain the aging warning feature index set.

5. The method according to claim 1, characterized in that, The process involves obtaining the real-time aging state vector of the high-heat resistant steel sample based on its dynamic response signal, and performing spatial similarity matching between the real-time aging state vector and the aging early warning feature index set within the high-temperature aging state space to output the real-time aging correlation, including: Extract real-time harmonic feature parameters of the same dimension as the harmonic distortion parameter set from the real-time dynamic response signal to construct the real-time aging state vector; The real-time aging state vector is mapped to the high-temperature aging state space, and the weighted average of the spatial distances between the vector vector and the feature points in the aging early warning feature index set within the high-temperature aging state space is calculated. The real-time aging correlation degree is then output.

6. The method according to claim 1, characterized in that, The determination of the aging failure probability and test parameters of the high-heat resistant steel sample based on the real-time aging correlation, the harmonic distortion parameter set, and the harmonic state evolution matrix includes: The real-time aging correlation degree and the harmonic distortion parameter set are input into the preset high-temperature aging probability prediction model; The nonlinear evolution acceleration of harmonic parameters in the current test phase is calculated based on the harmonic state evolution matrix. The probability of aging failure of the high-heat resistant steel sample during the remaining test cycle is predicted based on the nonlinear evolution acceleration. When the aging failure probability is greater than a preset critical value, test parameters are generated, including temperature gradient adjustment, liquid medium flow rate correction, and mechanical load compensation.

7. The method according to claim 6, characterized in that, When generating test parameters that include temperature gradient adjustment, liquid medium flow rate correction, and mechanical load compensation, the following should be included: Establish a mapping relationship library between the material microstructure changes of the high-heat resistant steel sample and the harmonic characteristic parameters; When the real-time aging correlation is greater than or equal to a preset threshold, the corresponding material lattice distortion type is matched from the mapping relationship library; The composition ratio of the liquid medium in the test parameters is optimized based on the type of material lattice distortion.

8. The method according to claim 1, characterized in that, Before establishing the high-temperature aging state space based on the harmonic state evolution matrix, the following steps are included: Multiple sets of reference samples were set up in a high-temperature liquid medium environment, and the harmonic characteristic parameters of the reference sample sets were collected simultaneously as reference data. The real-time harmonic characteristic parameters of the high-heat resistant steel sample are calibrated based on the reference data to establish a high-temperature aging state space according to the calibrated real-time harmonic characteristic parameters.

9. The method according to claim 1, characterized in that, When determining the aging failure probability and test parameters of the high-heat resistant steel sample based on the real-time aging correlation, the harmonic distortion parameter set, and the harmonic state evolution matrix, the following steps are included: Construct a steady-state index for the test environment; When the steady-state index of the test environment is less than the preset standard, the prediction of the aging failure probability of the high-heat resistant steel sample is stopped.

10. A water immersion harmonic nonlinear high-temperature aging test system for high-heat resistant steel, characterized in that, include: The acquisition module is used to acquire the real-time dynamic response signal of the high-heat resistant steel sample in the high-temperature liquid medium, and generate a multi-dimensional harmonic feature dataset based on the real-time dynamic response signal. The feature processing module is used to extract the harmonic distortion parameter set of the high-heat resistant steel sample based on the multi-dimensional harmonic feature dataset, and to construct the harmonic state evolution matrix based on the harmonic distortion parameter set. The module is used to establish a high-temperature aging state space based on the harmonic state evolution matrix, calculate the distribution density of historical failure events of the high-heat resistant steel sample in the high-temperature aging state space, and determine the aging early warning feature index set based on the distribution density. The similarity matching module is used to obtain the real-time aging state vector of the high-temperature resistant steel sample based on the dynamic response signal of the high-temperature resistant steel sample, and to perform spatial similarity matching between the real-time aging state vector and the aging warning feature index set in the high-temperature aging state space, and output the real-time aging correlation degree. The probability prediction and test parameter generation module is used to determine the aging failure probability and test parameters of the high-heat resistant steel sample based on the real-time aging correlation, the harmonic distortion parameter set, and the harmonic state evolution matrix.

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