Creep experiment data analysis processing method for power station boiler

Abstract

The invention provides a creep experiment data analysis processing method for a power station boiler, and belongs to the technical field of data processing, and the method specifically comprises the steps: determining the deviation condition of the experiment data of the power station boiler under other similar operation conditions according to the analysis result of the experiment data of each position in different dimensions, and determining the deviation condition of the experiment data of the power station boiler; determining an experimental data deviation position in the positions based on the deviation condition, and when determining that the power station boiler does not need to adopt a preset strategy to analyze and process the experimental data according to the deviation condition of the experimental data deviation position, dividing the experimental data, the heating data and the distribution position of each position into different similar position groups; and the temperature fields and the stress fields of the characteristic positions in different similar position groups are analyzed to draw the creep curve, and the service life of each position of the similar position groups is evaluated, so that the analysis and processing efficiency of creep experiment data is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of data processing, and in particular relates to a creep test data analysis and processing method for a power station boiler. Background Art

[0002] The main influencing factor of creep is temperature. Studies have shown that creep damage will inevitably occur as long as the operating condition exceeds 350°C. The operating temperature of large thermal power plant units is generally above 500°C. High-temperature creep damage is an important factor in tube failure. In-depth research on the organization, room-temperature mechanical properties, and high-temperature creep properties of T91 steel is of great significance to ensure the safe operation of thermal power plant boilers.

[0003] Specifically, in the invention patent application CN202410841223.4 "Method and system for quantitative diagnosis of creep damage and life assessment of dissimilar steel welded joints", the current life stage and creep remaining life of the dissimilar steel welded joints are determined based on the analysis results of experimental data, thereby improving the accuracy of quantitative diagnosis of creep structure damage of dissimilar steel welded joints.

[0004] However, for power plant boilers, if creep curves are drawn for all locations in the pipeline and the remaining life is evaluated, the overall evaluation process will inevitably be too inefficient. Therefore, how to combine experimental data and operating data to determine a differentiated evaluation and processing strategy for the remaining life of the pipeline has become a technical problem that needs to be solved urgently.

[0005] In view of the above technical problems, the present application specifically provides a method for analyzing and processing creep test data for power station boilers. Summary of the invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: Specifically, the present application provides a creep test data analysis and processing method for a power station boiler, which specifically includes: S1 determines based on the historical operation data of the power plant boiler that the change of the heating data of the heating pipe of the power plant boiler meets the requirements, and then proceeds to the next step; S2: when it is determined that the position does not belong to the abnormal position of the experimental data based on the analysis results of the experimental data of each position in different dimensions at each position of the heated pipeline, the deviation of the experimental data of the power plant boiler under other similar operating conditions is determined based on the analysis results of the experimental data of each position in different dimensions; S3: determining the experimental data deviation position in the position based on the deviation situation, and when it is determined that the power plant boiler does not need to adopt a preset strategy to analyze and process the experimental data according to the deviation situation of the experimental data deviation position, proceeding to the next step; S4 divides the experimental data, heating data and distribution position of each position into different similar position groups, analyzes the temperature field and stress field of the characteristic positions in different similar position groups, draws creep curves, and evaluates the service life of each position in the similar position groups.

[0007] The beneficial effects of the present invention are: Based on the historical operating data of the power plant boiler, it is determined whether the change in the heating data of the heating pipes of the power plant boiler meets the requirements, thereby realizing the screening of power plant boilers with more drastic changes in the heating data of the heating pipes of the power plant boiler, and realizing the service life evaluation of all positions, avoiding the technical problem of inaccurate service life evaluation and analysis results caused by using similar position groups, and ensuring the accuracy of the evaluation and analysis.

[0008] The temperature field and stress field of characteristic positions in different similar position groups are analyzed to draw creep curves, and the service life of each position in the similar position group is evaluated, thereby avoiding the technical problem of a large amount of data processing caused by analyzing the temperature field and stress field of all positions, improving the efficiency of the service life evaluation processing of the heating pipes of the power station boiler, and also ensuring the accuracy of the service life evaluation processing of the heating pipes of the power station boiler.

[0009] A further technical solution is that the historical operation data of the power station boiler includes load data and heating data of the power station boiler at different times.

[0010] A further technical solution is to determine whether the change of the heating data of the heating pipe of the power station boiler meets the requirements, which specifically includes: Based on the heating data of the heating pipe of the power plant boiler, determine the period during which the operating temperature of the heating pipe of the power plant boiler is within a preset operating temperature range, and use it as the matching temperature period; Based on the change of the operating temperature at different moments in different matching temperature periods, determining the operating temperature change moments in different matching temperature periods, and determining the temperature change period in the matching temperature period by using the number ratio of the operating temperature change moments; The accumulated duration of the temperature variation period is used to determine whether the variation of the heating data of the heating pipe of the power station boiler meets the requirements.

[0011] A further technical solution is that the operating temperature change moment is a moment when the change amount of the operating temperature at an adjacent moment does not meet the requirement.

[0012] A further technical solution is that the temperature change period in the matching temperature period is a matching temperature period in which the proportion of the number of operating temperature change moments is greater than the proportion of the number of preset moments.

[0013] A further technical solution is to use the accumulated duration of the temperature change period to determine whether the change in the heating data of the heating pipe of the power station boiler meets the requirements, specifically including: When the accumulated duration of the temperature variation period is greater than a preset duration threshold, it is determined that the variation of the heating data of the heating pipe of the power station boiler does not meet the requirement.

[0014] A further technical solution is that, when the variation of the heating data of the heating pipe of the power station boiler does not meet the requirements, a preset strategy is used to analyze and process the experimental data of the heating pipe.

[0015] A further technical solution is to divide them into different similar location groups, specifically including: Based on the experimental data, heating data and distribution position of each position, determine the deviation coefficient of the experimental data at different positions, the deviation coefficient of the operating time in different operating temperature ranges, and the distance deviation coefficient of the distribution position; Determine the data deviation coefficients at different locations based on the deviation coefficients of experimental data at different locations, the deviation coefficients of operating time at different operating temperature ranges, and the average value of the distance deviation coefficients of the distribution locations; The locations are divided into different similar location groups based on the data deviation coefficients of different locations.

[0016] A further technical solution is to divide the positions into different similar position groups based on the data deviation coefficients of different positions, specifically including: The data deviation coefficients between different positions within a preset range are divided into the same similar position group.

[0017] A further technical solution is that the characteristic position in the similar position group is a position in the similar position group having the smallest average value of data deviation coefficients with different positions in the similar position group.

[0018] A further technical solution is to evaluate the service life of each position in the similar position group, specifically including: Based on the creep curve and experimental data of the position, determining the curve position of the position in the creep curve; The service life of each position of the similar position group is evaluated according to the interval time from the curve position to the warning creep state of the creep curve.

[0019] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0022] Figure 1 It is a flow chart of a creep test data analysis and processing method for power station boilers; Figure 2 A flow chart for determining whether the change of the heating data of the heating pipe of the power station boiler meets the requirements; Figure 3 is a flow chart for determining that the position does not belong to an abnormal position of experimental data; Figure 4 is a flow chart of a method for determining a position of deviation in experimental data. DETAILED DESCRIPTION

[0023] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their detailed description will be omitted.

[0024] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to express an open-ended inclusive meaning and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc.

[0025] Example 1 To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a creep test data analysis and processing method for a power station boiler is provided, which specifically includes: S1 determines based on the historical operation data of the power plant boiler that the change of the heating data of the heating pipe of the power plant boiler meets the requirements, and then proceeds to the next step; Furthermore, the historical operation data of the power plant boiler includes load data and heating data of the power plant boiler at different times.

[0026] Specifically, Figure 2 As shown, determining whether the change of the heating data of the heating pipe of the power station boiler meets the requirements specifically includes: Based on the heating data of the heating pipe of the power plant boiler, determine the period during which the operating temperature of the heating pipe of the power plant boiler is within a preset operating temperature range, and use it as the matching temperature period; Based on the change of the operating temperature at different moments in different matching temperature periods, determining the operating temperature change moments in different matching temperature periods, and determining the temperature change period in the matching temperature period by using the number ratio of the operating temperature change moments; The accumulated duration of the temperature variation period is used to determine whether the variation of the heating data of the heating pipe of the power station boiler meets the requirements.

[0027] It should be noted that the operating temperature change time is the time when the change amount of the operating temperature at the adjacent time does not meet the requirement.

[0028] Specifically, the temperature change period in the matching temperature period is a matching temperature period in which the proportion of the number of operating temperature change moments is greater than the proportion of the number of preset moments.

[0029] It can be understood that the cumulative duration of the temperature change period is used to determine whether the change of the heating data of the heating pipe of the power station boiler meets the requirements, specifically including: When the accumulated duration of the temperature variation period is greater than a preset duration threshold, it is determined that the variation of the heating data of the heating pipe of the power station boiler does not meet the requirement.

[0030] It should be noted that when the variation of the heating data of the heating pipe of the power station boiler does not meet the requirements, a preset strategy is used to analyze and process the experimental data of the heating pipe.

[0031] Specifically, the preset strategy is to perform temperature field and stress field analysis on heated pipes at all positions, draw creep curves, and perform service life evaluation at different positions.

[0032] S2: when it is determined that the position does not belong to the abnormal position of the experimental data based on the analysis results of the experimental data of each position in different dimensions at each position of the heated pipeline, the deviation of the experimental data of the power plant boiler under other similar operating conditions is determined based on the analysis results of the experimental data of each position in different dimensions; Furthermore, the experimental data include tensile strength, hardness, endurance strength, microstructure damage and creep cracking.

[0033] Specifically, Figure 3 As shown, determining that the position does not belong to an abnormal position of the experimental data specifically includes: Determine the preset data intervals of the experimental data in different dimensions based on the analysis results of the experimental data in different dimensions; Determine the abnormal weight coefficients of the experimental data of different dimensions according to the preset data intervals in which the experimental data of different dimensions are located and the preset data abnormal weight coefficients corresponding to the preset data intervals; The abnormal impact weight values ​​of the experimental data of different dimensions are determined according to the types of the experimental data of different dimensions, and the comprehensive abnormal value of the position is determined in combination with the abnormal weight coefficients of the experimental data of different dimensions, and the comprehensive abnormal value is used to determine whether the position belongs to the abnormal position of the experimental data.

[0034] It can be understood that using the comprehensive abnormal value to determine whether the position belongs to an abnormal position of experimental data specifically includes: When the comprehensive abnormal value of the position is within the preset abnormal value interval, it is determined that the position belongs to the abnormal position of the experimental data.

[0035] Specifically, when the position is an abnormal position of experimental data, the temperature field and stress field analysis are performed on the heated pipes at the position, and the creep curve is drawn to perform service life evaluation.

[0036] Optionally, determining that the position is not an abnormal position of experimental data specifically includes: Determine the preset data intervals where the experimental data of different dimensions are located based on the analysis results of the experimental data of different dimensions, and determine the abnormal weight coefficients of the experimental data of different dimensions according to the preset data intervals where the experimental data of different dimensions are located and the preset data abnormal weight coefficients corresponding to the preset data intervals; When there is experimental data with an abnormal weight coefficient greater than a preset weight coefficient, it is determined that the position belongs to an abnormal position of the experimental data; When there is no experimental data with abnormal weight coefficient greater than the preset weight coefficient: Determine the abnormal value of the position by summing the abnormal weight coefficients of the experimental data of different dimensions, and when the abnormal value of the position does not meet the requirements, determine that the position belongs to the abnormal position of the experimental data; When the outlier value at the position meets the requirements: When it is determined that there is no experimental data with an abnormal weight coefficient within a preset weight coefficient interval using abnormal weight coefficients of experimental data of different dimensions, it is determined that the position does not belong to an abnormal position of experimental data; When there is experimental data with abnormal weight coefficients within the preset weight coefficient range: When the number of experimental data with abnormal weight coefficients within the preset weight coefficient interval does not meet the requirement, the position is determined to be an abnormal position of experimental data; When the number of experimental data with abnormal weight coefficients within the preset weight coefficient range meets the requirements: Obtaining the sum of abnormal weight coefficients of experimental data whose abnormal weight coefficients are within a preset weight coefficient interval, and when the sum of abnormal weight coefficients of experimental data whose abnormal weight coefficients are within the preset weight coefficient interval is greater than a preset value of the weight coefficient, determining that the position is an abnormal position of the experimental data; When the sum of the abnormal weight coefficients of the experimental data within the preset weight coefficient interval is not greater than the preset value of the weight coefficient: The abnormal impact weight values ​​of the experimental data of different dimensions are determined according to the types of the experimental data of different dimensions, and the comprehensive abnormal value of the position is determined in combination with the abnormal weight coefficients of the experimental data of different dimensions, and the comprehensive abnormal value is used to determine whether the position belongs to the abnormal position of the experimental data.

[0037] S3: determining the experimental data deviation position in the position based on the deviation situation, and when it is determined that the power plant boiler does not need to adopt a preset strategy to analyze and process the experimental data according to the deviation situation of the experimental data deviation position, proceeding to the next step; Furthermore, the power station boiler under the similar operating conditions is a power station boiler whose deviations from the power station boiler in different heating temperature ranges all meet the requirements.

[0038] Specifically, the deviation of the experimental data includes the deviation amount of the experimental data in different dimensions.

[0039] It should be noted that if Figure 4 As shown, the method for determining the experimental data deviation position in the position is: Determine the deviation amount from the experimental data of the position of the power plant boiler in different dimensions under different similar operating conditions using the experimental data of the position in different dimensions; Determine the deviation of the experimental data of the power plant boiler under different similar operating conditions according to the average value of the deviation of the experimental data in different dimensions; Whether the position is an experimental data deviation position is determined by the experimental data deviation amount of the power plant boiler under different similar operating conditions.

[0040] Further, determining whether the position is an experimental data deviation position by comparing the experimental data deviation of the power plant boiler under different similar operating conditions specifically includes: Determine the number of power plant boilers under similar operating conditions whose experimental data deviations are not within a preset data deviation range by comparing the experimental data deviations of power plant boilers under different similar operating conditions; When the number of power plant boilers under similar operating conditions whose experimental data deviation is not within the preset data deviation range is greater than the preset deviation power plant boiler number, the position is determined to be the experimental data deviation position.

[0041] In one possible embodiment, determining that the power plant boiler does not need to adopt a preset strategy to analyze and process the experimental data specifically includes: Determine the number of experimental data deviation positions in the heated pipeline according to distribution data of experimental data deviation positions in the heated pipeline, and determine the proportion of the experimental data deviation positions in the heated pipeline excluding abnormal experimental data positions; By using the deviation conditions of different experimental data deviation positions, the average value of the experimental data deviation coefficients of the power plant boilers under different experimental data deviation positions and different similar operating conditions is determined, and the average value is used as the deviation coefficient mean value; The product of the average value of the deviation coefficients of different experimental data deviation positions and the proportion of the number of experimental data deviation positions in the heated pipeline excluding abnormal experimental data positions is used as the experimental data deviation value, and the experimental data deviation value is used to determine that the power plant boiler does not need to adopt a preset strategy to analyze and process the experimental data.

[0042] Furthermore, when the experimental data deviation value is greater than the deviation coefficient setting value, it is determined that the power station boiler needs to adopt a preset strategy to analyze and process the experimental data.

[0043] S4 divides the experimental data, heating data and distribution position of each position into different similar position groups, analyzes the temperature field and stress field of the characteristic positions in different similar position groups, draws creep curves, and evaluates the service life of each position in the similar position groups.

[0044] Specifically, they are divided into different similar location groups, including: Based on the experimental data, heating data and distribution positions of each position, the deviation coefficient of the experimental data at different positions, the deviation coefficient of the operating time in different operating temperature ranges, and the distance deviation coefficient of the distribution position are determined; Determine the data deviation coefficients at different locations based on the deviation coefficients of experimental data at different locations, the deviation coefficients of operating time at different operating temperature ranges, and the average value of the distance deviation coefficients of the distribution locations; The locations are divided into different similar location groups based on the data deviation coefficients of different locations.

[0045] Furthermore, the locations are divided into different similar location groups based on the data deviation coefficients of different locations, specifically including: The data deviation coefficients between different positions within a preset range are divided into the same similar position group.

[0046] It should also be noted that the characteristic position in the similar position group is the position in the similar position group that has the smallest average value of data deviation coefficients with different positions in the similar position group.

[0047] It can be understood that the service life evaluation of each position in the similar position group specifically includes: Determining a curve position of the position in the creep curve based on the creep curve and experimental data of the position; The service life of each position of the similar position group is evaluated according to the interval time from the curve position to the warning creep state of the creep curve.

[0048] Example 2 Optionally, determining whether a change in the heating data of the heating pipe of the power station boiler meets the requirements specifically includes: Based on the heating data of the heating pipe of the power station boiler, determine the period when the operating temperature of the heating pipe of the power station boiler is within the preset operating temperature range, and use it as the matching temperature period. When the cumulative duration of different matching temperature periods is less than the preset cumulative duration threshold, it is determined that the change of the heating data of the heating pipe of the power station boiler meets the requirements; When the cumulative duration of different matching temperature periods is not less than the preset cumulative duration threshold: Based on the variation of the operating temperature at different moments in different matching temperature periods, the operating temperature variation moments in different matching temperature periods are determined; when the number of the operating temperature variation moments in different matching temperature periods is within a preset variation moment number range, it is determined that the variation of the heating data of the heating pipe of the power station boiler meets the requirements; When the sum of the number of operating temperature change moments in different matching temperature periods is not within the preset change moment number range: When the sum of the number of operating temperature change moments in different matching temperature periods is greater than the preset number of change moments, it is determined that the change of the heating data of the heating pipe of the power station boiler does not meet the requirements; When the sum of the number of operating temperature change moments in different matching temperature periods is not greater than the preset number of change moments: When it is determined by using the proportion of the number of the operating temperature change moments that there is no temperature change period in the matching temperature period, it is determined that the change of the heating data of the heating pipe of the power station boiler meets the requirements; When determining that there is a temperature change period in the matching temperature period by using the proportion of the number of the operating temperature change moments: When the accumulated duration of the temperature change period is greater than the preset duration threshold: it is determined that the change of the heating data of the heating pipe of the power station boiler does not meet the requirements; When the accumulated duration of the temperature change period is not greater than the preset duration threshold: The heat variation coefficient of the heating pipe of the power plant boiler is determined based on the duration of different temperature variation periods, and the heat variation coefficient is used to determine whether the variation of the heating data of the heating pipe of the power plant boiler meets the requirements.

[0049] Example 3 Optionally, determining that the power plant boiler does not need to adopt a preset strategy to analyze and process the experimental data specifically includes: S31 determines the number of experimental data deviation positions of the heated pipeline according to the distribution data of the experimental data deviation positions in the heated pipeline, determines the proportion of the experimental data deviation positions in the heated pipeline excluding the experimental data abnormal positions, and determines the position distribution abnormality coefficient of the heated pipeline in combination with the number of experimental data deviation positions of the heated pipeline; S32 uses the deviation conditions of different experimental data deviation positions to determine the average value of experimental data deviation coefficients of power plant boilers under different experimental data deviation positions and different similar operating conditions, and uses it as the deviation coefficient mean value, and determines the experimental data deviation coefficient of the heated pipe based on the deviation coefficient mean value of different experimental data deviation positions; S33 determines the experimental data deviation value based on the average of the experimental data deviation coefficient and the position distribution anomaly coefficient, and uses the experimental data deviation value to determine that the power plant boiler does not need to adopt a preset strategy to analyze and process the experimental data.

[0050] Optionally, the above step S31 includes the following contents: S311 determines the number of experimental data deviation positions of the heated pipeline according to the distribution data of the experimental data deviation positions in the heated pipeline. When the number of experimental data deviation positions of the heated pipeline is greater than the preset number of deviation positions, it is determined that the power plant boiler needs to adopt a preset strategy to analyze and process the experimental data. When the number of experimental data deviation positions of the heated pipeline is not greater than the preset number of deviation positions, the process proceeds to step S312. S312 determines the ratio of the experimental data deviation positions in the heated pipe excluding the experimental data abnormal positions, and uses it as the deviation ratio. When the deviation ratio is greater than the preset deviation ratio threshold, the process proceeds to step S313. When the deviation ratio is not greater than the preset deviation ratio threshold, the process proceeds to step S314. S313: When the number of deviation positions of the experimental data of the heated pipe is within the preset deviation position number interval, it is determined that the power plant boiler needs to adopt a preset strategy to analyze and process the experimental data; when the number of deviation positions of the experimental data of the heated pipe is not within the preset deviation position number interval, the process proceeds to step S314; S314 determines the position distribution anomaly coefficient of the heated pipeline based on the deviation number ratio and the number of experimental data deviation positions of the heated pipeline. When the position distribution anomaly coefficient of the heated pipeline does not meet the requirements, it is determined that the power plant boiler needs to adopt a preset strategy to analyze and process the experimental data. When the position distribution anomaly coefficient of the heated pipeline meets the requirements, the process proceeds to step S315. S315 When the position distribution anomaly coefficient of the heated pipe is within the preset position distribution anomaly coefficient interval, proceed to step S32. When the position distribution anomaly coefficient of the heated pipe is not within the preset position distribution anomaly coefficient interval, it is determined that the power plant boiler does not need to adopt the preset strategy to analyze and process the experimental data.

[0051] Optionally, the above step S32 includes the following contents: S321 uses the deviation of different experimental data deviation positions to determine the average value of the experimental data deviation coefficient of the power plant boiler under different similar operating conditions and different experimental data deviation positions, and uses it as the deviation coefficient mean value. When there is an experimental data deviation position with a deviation coefficient mean value greater than a preset deviation coefficient threshold value, it proceeds to step S322. When there is no experimental data deviation position with a deviation coefficient mean value greater than the preset deviation coefficient threshold value, it proceeds to step S323. S322: When the number of experimental data deviation positions whose deviation coefficient mean is greater than the preset deviation coefficient threshold or the number of experimental data deviation positions whose deviation coefficient mean is greater than the preset deviation coefficient threshold and the proportion of the number of experimental data abnormal positions excluding the number of experimental data abnormal positions in the heated pipeline do not meet the requirements, it is determined that the power station boiler needs to adopt a preset strategy to analyze and process the experimental data; when the number of experimental data deviation positions whose deviation coefficient mean is greater than the preset deviation coefficient threshold or the number of experimental data deviation positions whose deviation coefficient mean is greater than the preset deviation coefficient threshold and the proportion of the number of experimental data abnormal positions excluding the number of experimental data abnormal positions in the heated pipeline meet the requirements, the process proceeds to step S323; S323 determines the experimental data deviation coefficient of the heated pipe based on the mean value of the deviation coefficient of different experimental data deviation positions. When the experimental data deviation coefficient of the heated pipe does not meet the requirements, it is determined that the power station boiler needs to adopt a preset strategy to analyze and process the experimental data. When the experimental data deviation coefficient of the heated pipe meets the requirements, it proceeds to step S33.

[0052] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0053] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0054] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A creep test data analysis and processing method for power station boilers, characterized in that: Specifically include: Based on the historical operation data of the power plant boiler, when it is determined that the change of the heating data of the heating pipe of the power plant boiler meets the requirements, proceed to the next step; When it is determined that the position does not belong to an abnormal position of the experimental data based on the analysis results of the experimental data of each position in different dimensions at the heated pipeline, the deviation of the experimental data of the power plant boiler under other similar operating conditions is determined based on the analysis results of the experimental data of each position in different dimensions; Determine the experimental data deviation position in the position based on the deviation situation, and when it is determined that the power plant boiler does not need to adopt a preset strategy to analyze and process the experimental data according to the deviation situation of the experimental data deviation position, proceed to the next step; The experimental data, heating data and distribution position of each position are divided into different similar position groups. The temperature field and stress field of the characteristic positions in different similar position groups are analyzed to draw creep curves, and the service life of each position in the similar position groups is evaluated.

2. The creep test data analysis and processing method for power station boilers according to claim 1, characterized in that: The historical operation data of the power station boiler includes load data and heating data of the power station boiler at different times.

3. The creep test data analysis and processing method for power station boilers according to claim 1, characterized in that: Determining whether the change of the heating data of the heating pipe of the power station boiler meets the requirements specifically includes: Based on the heating data of the heating pipe of the power plant boiler, determine the period during which the operating temperature of the heating pipe of the power plant boiler is within a preset operating temperature range, and use it as the matching temperature period; Based on the change of the operating temperature at different moments in different matching temperature periods, determining the operating temperature change moments in different matching temperature periods, and determining the temperature change period in the matching temperature period by using the number ratio of the operating temperature change moments; The accumulated duration of the temperature variation period is used to determine whether the variation of the heating data of the heating pipe of the power station boiler meets the requirements.

4. The creep test data analysis and processing method for power station boilers according to claim 3, characterized in that: The operating temperature change time is a time when the change amount of the operating temperature at the adjacent time does not meet the requirement.

5. The creep test data analysis and processing method for power station boilers according to claim 3, characterized in that: The temperature change period in the matching temperature period is a matching temperature period in which the proportion of the number of operating temperature change moments is greater than the proportion of the number of preset moments.

6. The creep test data analysis and processing method for power station boilers according to claim 3, characterized in that: Determining whether the change of the heating data of the heating pipe of the power plant boiler meets the requirements by using the accumulated duration of the temperature change period specifically includes: When the accumulated duration of the temperature variation period is greater than a preset duration threshold, it is determined that the variation of the heating data of the heating pipe of the power station boiler does not meet the requirement.

7. The creep test data analysis and processing method for power station boilers according to claim 1, characterized in that: When the variation of the heating data of the heating pipe of the power station boiler does not meet the requirements, a preset strategy is adopted to analyze and process the experimental data of the heating pipe.

8. The creep test data analysis and processing method for power station boilers according to claim 1, characterized in that: Divide them into different similar location groups, including: Based on the experimental data, heating data and distribution positions of each position, the deviation coefficient of the experimental data at different positions, the deviation coefficient of the operating time in different operating temperature ranges, and the distance deviation coefficient of the distribution position are determined; Determine the data deviation coefficients at different locations based on the deviation coefficients of experimental data at different locations, the deviation coefficients of operating time at different operating temperature ranges, and the average value of the distance deviation coefficients of the distribution locations; The locations are divided into different similar location groups based on the data deviation coefficients of different locations.

9. The creep test data analysis and processing method for power station boilers according to claim 8, characterized in that: The locations are divided into different similar location groups based on the data deviation coefficients of different locations, specifically including: The data deviation coefficients between different positions within a preset range are divided into the same similar position group.

10. The creep test data analysis and processing method for power station boilers according to claim 1, characterized in that: Performing a service life assessment of each position in the similar position group, specifically including: Determining a curve position of the position in the creep curve based on the creep curve and experimental data of the position; The service life of each position of the similar position group is evaluated according to the interval time from the curve position to the warning creep state of the creep curve.

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