A method, device and equipment for analyzing service reliability of a gas turbine turbine blade

Abstract

The present application relates to the field of turbine blades, and discloses a gas turbine turbine blade service reliability analysis method, device and equipment, the method comprises the following steps: obtaining the data information of the gas turbine turbine blade before and after service in multiple service cycles and the measurement data of the gas turbine turbine blade to be evaluated; a blade health state database is established based on the data information; the size difference value data is analyzed according to the measurement data and the data information before service; the deformation grade is obtained by comparative analysis according to the size difference value data, the maximum deformation position and the blade health state database; the deformation grade is compared with the preset safety grade to obtain the analysis result. By analyzing the data information of the blades in different service cycles to establish the blade health state database, the deformation degree can be obtained only by measuring the geometric deviation of the gas turbine blade to be evaluated, and then the safety evaluation is carried out to obtain the result, which provides a technical reference for the repair and blade replacement of the gas turbine blade.

Description

Technical Field

[0001] This invention relates to the field of turbine blades, and more specifically to a method, apparatus, and equipment for analyzing the service reliability of gas turbine blades. Background Technology

[0002] Turbine blades are characterized by their large size, heavy mass, and complex geometry. Operating for extended periods in harsh environments with high temperatures and centrifugal forces, different parts of the blade experience significant variations in thermal and mechanical loads. Creep and fatigue are the primary damage mechanisms leading to blade failure. As a critical hot-end component of a gas turbine, the service reliability of turbine blades is crucial for the safe operation of the gas turbine.

[0003] Currently, service evaluation of turbine blades mainly focuses on non-destructive testing (NDT) methods, as well as blade dissection for service damage assessment and corresponding life prediction. NDT aims to detect cracks caused by fatigue. However, besides fatigue failure, turbine blades undergo creep during long-term service, leading to microstructural degradation and further creep deformation. This significantly impacts the turbine's aerodynamic performance and operating condition. Excessive plastic deformation or creep fracture directly threatens the safety of the entire gas turbine. NDT methods such as magnetic particle testing, penetrant testing, ultrasonic testing, and radiographic testing primarily detect and analyze existing cracks in the blades. They are difficult to detect the insidious dangers caused by creep deformation resulting from prolonged high-temperature service, which alters the matrix structure. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, apparatus and equipment for analyzing the service reliability of gas turbine blades, in order to solve the problems that the existing technology for monitoring and analyzing gas turbine blades is not accurate and reliable enough and has potential safety hazards.

[0005] In a first aspect, embodiments of the invention provide a method for analyzing the service reliability of gas turbine blades, including:

[0006] Acquire data on gas turbine blades during multiple service cycles before and after service, as well as measurement data on the gas turbine blades to be evaluated;

[0007] Establish a leaf health status database based on data information;

[0008] Based on measurement data and pre-service data of the gas turbine blades, the dimensional difference data of the maximum deformation location of the gas turbine blades to be evaluated was analyzed.

[0009] Based on the dimensional difference data, the location of maximum deformation, and the blade health status database, a comparative analysis was conducted on the turbine blades of the gas turbine to be evaluated to obtain the deformation level.

[0010] The deformation level and the preset safety level are compared to obtain the analysis results.

[0011] By analyzing data from blades at different service cycles, a blade health status database is established. This allows for the determination of deformation degree simply by measuring geometric deviations of the gas turbine blades to be evaluated, thereby enabling safety assessments and providing technical references for the repair and replacement of gas turbine blades.

[0012] In one optional implementation, a leaf health status database is established based on data information, including:

[0013] The data is classified and processed to obtain dimensional parameters, microstructure parameters, and material parameters;

[0014] By comparing and analyzing the dimensional parameters of gas turbine blades at different service cycles after service with those of gas turbine blades before service, multiple deformation locations and the degree of deformation corresponding to each deformation location of gas turbine blades at different service cycles are obtained.

[0015] Microstructure analysis was performed on gas turbine blades with different service cycles based on dimensional parameters, deformation locations, and microstructure parameters, and microstructure data corresponding to each deformation location of the gas turbine blades with different service cycles were obtained.

[0016] Based on material parameters, microstructure data and a pre-set creep test database, the creep stages of gas turbine blades at different service periods were analyzed to obtain creep stage data corresponding to each deformation position of the gas turbine blades at different service periods.

[0017] The deformation degree, microstructure, and creep stage data of each deformation position of the gas turbine blade at different service cycles are compared with the preset deformation level requirements to obtain the deformation level corresponding to different dimensional parameters, microstructure parameters, and material parameters, and to establish the mapping relationship between the deformation level and the dimensional difference data and the deformation position corresponding to the dimensional difference data.

[0018] A blade health status database is established based on the mapping relationship between deformation level and dimensional difference data, and the corresponding deformation location of dimensional difference data.

[0019] By comparing deformation degree, microstructure, and creep stage data with preset deformation level requirements, deformation levels corresponding to different dimensional, microstructure, and material parameters are obtained. A mapping between these parameters and deformation levels is established to create a blade health status database. This data source is more reliable, and the analysis accuracy is higher, thereby improving the accuracy and reliability of subsequent analysis of the deformation degree of the gas turbine blades under evaluation based on the blade health status database. Furthermore, by establishing a blade health status database, non-destructive evaluation of the gas turbine blades under evaluation can be performed. Compared to the existing method of assessment through dissection, non-destructive evaluation allows serviceable blades to continue operating, reducing the economic losses caused by blade scrapping due to dissection.

[0020] In one optional implementation, by comparing and analyzing the dimensional parameters of gas turbine blades at different service cycles after service with those before service, multiple deformation locations of the gas turbine blades at different service cycles and the degree of deformation corresponding to each deformation location are obtained, including:

[0021] The dimensional parameters for each service cycle are compared with the dimensional parameters before service to obtain multiple deformation locations and geometric deviation data corresponding to each deformation location;

[0022] The geometric deviation data is compared with multiple preset deformation ranges to obtain the deformation degree corresponding to each deformation position.

[0023] By comparing geometric deviation data with multiple preset deformation ranges, the degree of deformation can be quickly and intuitively determined, with high efficiency and accuracy.

[0024] In one optional implementation, microstructure analysis is performed on gas turbine blades at different service lifespans based on dimensional parameters, deformation locations, and microstructure parameters to obtain microstructure data corresponding to each deformation location of the gas turbine blades at different service lifespans, including:

[0025] Extract the coordinate information corresponding to the deformation location from the dimensional parameters;

[0026] Deformation feature points are obtained based on coordinate information analysis;

[0027] Tissue state data for each deformation feature point is extracted from tissue parameters.

[0028] By analyzing the tissue state data of blades under different service cycles, the accuracy and reliability of the blade health status database can be effectively improved.

[0029] In one optional implementation, the creep stages of gas turbine blades at different service lifespans are analyzed based on material parameters, microstructure data, and a pre-set creep test database to obtain creep stage data corresponding to each deformation position of the gas turbine blades at different service lifespans, including:

[0030] Extract test data corresponding to material parameters from a pre-set creep test database. The test data includes test microstructure data corresponding to different creep stages.

[0031] By comparing the tissue state data with the experimental data, the creep stage data corresponding to each deformation location were obtained.

[0032] Because existing technologies require dissection to determine creep conditions, this application establishes a pre-set creep test database and obtains creep stage data for each blade through comparative analysis. By establishing a blade health status database in this way, non-destructive assessment of the turbine blades of the gas turbine to be evaluated can be performed. Compared with the existing technology of determining creep conditions through dissection, non-destructive assessment can allow blades that are still in service to continue to serve, reducing the economic losses caused by blade scrapping due to dissection.

[0033] In one optional implementation, the deformation level is obtained by comparing and analyzing the dimensional difference data, the location of maximum deformation, and a blade health status database of the gas turbine blade to be evaluated, including:

[0034] Filter multiple deformation locations corresponding to size difference data from the leaf health status database;

[0035] By comparing the location of maximum deformation with the location of maximum deformation, the coordinate difference between each deformation location and the location of maximum deformation is obtained;

[0036] Select the position with the smallest coordinate difference from the deformed positions as the target position;

[0037] Based on the target location, extract the deformation level corresponding to the target location from the blade health status database.

[0038] By selecting the deformation level corresponding to the closest position with the same degree of deformation, it better matches the actual condition of the turbine blade of the gas turbine to be evaluated, and has high accuracy and reliability.

[0039] In one optional implementation, the deformation level and a preset safety level are compared to obtain the analysis results, including:

[0040] Compare the deformation level with the preset safety level;

[0041] When the deformation level is less than the preset safety level, the blade condition of the gas turbine blade to be evaluated is healthy.

[0042] When the deformation level is greater than or equal to the preset safety level, the blade condition of the gas turbine blade to be evaluated is dangerous.

[0043] When the blade condition of the gas turbine blade to be evaluated is healthy, it means that the gas turbine blade to be evaluated can continue to serve and has high service reliability; when the blade condition of the gas turbine blade to be evaluated is dangerous, the blade service reliability is low and timely maintenance or replacement is required, and technical support should be provided to maintenance personnel.

[0044] Secondly, embodiments of the present invention provide a gas turbine blade service reliability analysis device, comprising:

[0045] The acquisition module is used to acquire data information of gas turbine blades before and after multiple service cycles, as well as measurement data of gas turbine blades to be evaluated.

[0046] A module is established to create a database of leaf health status based on data information;

[0047] The analysis module is used to analyze the dimensional difference data of the maximum deformation location of the gas turbine blade to be evaluated based on measurement data and pre-service data of the gas turbine blade;

[0048] The deformation module is used to compare and analyze the turbine blades of the gas turbine under evaluation based on the dimensional difference data, the location of the maximum deformation, and the blade health status database to obtain the deformation level.

[0049] The comparison module is used to compare the deformation level with the preset safety level to obtain the analysis results.

[0050] Thirdly, embodiments of the present invention provide a computer device, including:

[0051] The memory and processor are interconnected and communicate with each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the gas turbine blade service reliability analysis method provided in this embodiment of the invention.

[0052] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions for causing a computer to execute the gas turbine blade service reliability analysis method provided in embodiments of the present invention. Attached Figure Description

[0053] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating the service reliability analysis method for gas turbine blades according to an embodiment of the present invention;

[0055] Figure 2 This is a flowchart illustrating another method for analyzing the service reliability of gas turbine blades according to an embodiment of the present invention;

[0056] Figure 3 This is a morphological comparison diagram according to an embodiment of the present invention;

[0057] Figure 4 This is a flowchart illustrating another method for analyzing the service reliability of gas turbine blades according to an embodiment of the present invention;

[0058] Figure 5 This is a flowchart illustrating another method for analyzing the service reliability of gas turbine blades according to an embodiment of the present invention.

[0059] Figure 6 This is a structural block diagram of a gas turbine blade service reliability analysis device according to an embodiment of the present invention;

[0060] Figure 7 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Turbine blades are characterized by their large size, heavy mass, and complex geometry. Operating for extended periods in harsh environments with high temperatures and centrifugal forces, different parts of the blade experience significant variations in thermal and mechanical loads. Creep and fatigue are the primary damage mechanisms leading to blade failure. As a critical hot-end component of a gas turbine, the service reliability of turbine blades is crucial for the safe operation of the turbine. Current assessments of turbine blade service reliability primarily focus on non-destructive testing methods, as well as blade dissection for service damage evaluation and corresponding lifespan prediction.

[0063] The purpose of nondestructive testing (NDT) is to detect cracks caused by fatigue. However, it is noteworthy that, in addition to fatigue failure, turbine blades undergo creep during long-term service, leading to microstructural degradation and further creep deformation. This significantly impacts the turbine's aerodynamic performance and operating condition. Excessive plastic deformation or creep fracture directly threatens the safety of the entire gas turbine. While some literature uses blade dissection for service damage assessment and life prediction, this method is still necessary for life prediction or service reliability evaluation of subsequent blades of the same type. Other approaches involve material-level and simulated creep fatigue tests on blades to establish the relationship between creep damage and microstructural evolution, thereby predicting turbine blade creep life. However, these methods lack comparison with the actual deformation and damage of in-service blades.

[0064] This invention provides a method for analyzing the service reliability of gas turbine blades, comprising: acquiring data information of gas turbine blades during multiple service cycles before and after service, as well as measurement data of the gas turbine blade to be evaluated; establishing a blade health status database based on the data information; analyzing the dimensional difference data of the maximum deformation position of the gas turbine blade to be evaluated based on the measurement data and the data information of the gas turbine blade before service; comparing and analyzing the dimensional difference data, the maximum deformation position, and the blade health status database to obtain the deformation level; and comparing the deformation level with a preset safety level to obtain the analysis result. By analyzing the data information of blades at different service cycles to establish a blade health status database, the degree of deformation can be obtained simply by measuring the geometric deviation of the gas turbine blade to be evaluated, thereby enabling safety assessment and providing technical reference for the repair and replacement of gas turbine blades.

[0065] According to an embodiment of the present invention, a method for service reliability analysis of gas turbine blades is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0066] This embodiment provides a method for analyzing the service reliability of gas turbine blades, which can be used to analyze the service status of gas turbine blades. Figure 1 This is a flowchart of a gas turbine blade service reliability analysis method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0067] Step S101: Acquire data information of gas turbine blades during multiple service cycles before and after service, as well as measurement data of the gas turbine blades to be evaluated. Specifically, the gas turbine blades before service are the finished standard blades after all production stages are completed. Data information includes dimensional parameters, microstructure parameters, and material parameters. The dimensional parameters are obtained through three-dimensional reverse engineering. The three-dimensional reverse engineering includes: three-dimensional data measurement, three-dimensional data processing, three-dimensional reconstruction, and three-dimensional model data output. First, a heavy-duty gas turbine blade positioning fixture is used to position the blades before service. A three-dimensional scanner is opened for calibration, a scanning reference is established, and a continuous three-dimensional scan of the blade is performed at 45°. After scanning around the blade, a three-dimensional point cloud map and three-dimensional point cloud data of the blade are obtained. After processing the data, the shape and size parameters of different parts of the blade are obtained based on the coordinates. Since the damage conditions of the blades are different in different service cycles, comparing the data from different service cycles with the data before service provides more comprehensive data, which facilitates the establishment of a subsequent blade health status database.

[0068] Step S102: Establish a blade health status database based on data information. Specifically, by establishing a blade health status database, the turbine blades of the gas turbine to be evaluated can be analyzed quickly and efficiently to obtain their deformation level. Furthermore, the blade health status database established based on data information has high accuracy. When analyzing the turbine blades of the gas turbine to be evaluated, there is no need to dissect the blades; analysis can be performed simply by measuring them. This not only saves a significant amount of time, but also allows for non-destructive assessment, enabling serviceable blades to continue operating and reducing the economic losses caused by blade scrapping due to dissection.

[0069] Step S103: Analyze the dimensional difference data of the maximum deformation location of the gas turbine blade to be evaluated based on the measurement data and the pre-service data of the gas turbine blade. Specifically, since the maximum deformation location better reflects the damage condition of the gas turbine blade, selecting the maximum deformation location is more reliable and can make the final analysis results more accurate.

[0070] Step S104: Based on the dimensional difference data, the location of maximum deformation, and the blade health status database, a comparative analysis is performed on the gas turbine blades to be evaluated to obtain the deformation level. Specifically, the higher the deformation level of the gas turbine blade, the lower its safety performance. When the deformation level exceeds the preset safety level, its safety performance can no longer be guaranteed, and replacement or repair is required.

[0071] Step S105: Compare the deformation level with the preset safety level to obtain the analysis results. Specifically, by comparing with the preset safety level, determine whether the turbine blade of the gas turbine to be evaluated can continue to be in service.

[0072] Through steps S101 to S105 above, the gas turbine blade service reliability analysis method provided in this embodiment of the invention establishes a blade health status database by analyzing data information of blades at different service cycles. This allows for obtaining the degree of deformation simply by measuring the geometric deviation of the gas turbine blade under evaluation, and then conducting a safety assessment to obtain the results, providing a technical reference for the repair and replacement of gas turbine blades. The damage-free assessment method of this application can also be used for the service reliability analysis of other blades, such as aero-engine turbine blades.

[0073] This embodiment provides a method for analyzing the service reliability of gas turbine blades, which can be used to analyze the service status of gas turbine blades. Figure 2 This is a flowchart of a gas turbine blade service reliability analysis method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0074] Step S201: Acquire data information of the gas turbine blades during multiple service cycles before and after service, as well as measurement data of the gas turbine blades to be evaluated. Specifically, the detailed description of this step is the same as that of step S101 above, and will not be repeated here.

[0075] Step S202: Establish a leaf health status database based on the data information. Specifically, the detailed description of this step is the same as that of step S102 above, and will not be repeated here.

[0076] Step S203: Analyze the dimensional difference data of the maximum deformation location of the gas turbine blade to be evaluated based on the measurement data and the pre-service data of the gas turbine blade. Specifically, the further detailed description of this step is the same as that of step S103 above, and will not be repeated here.

[0077] Step S204: Based on the dimensional difference data, the location of maximum deformation, and the blade health status database, a comparative analysis is performed on the turbine blades of the gas turbine to be evaluated to obtain the deformation level. Specifically, the further detailed description of this step is the same as that of step S104 above, and will not be repeated here.

[0078] Step S205: Compare the deformation level with the preset safety level to obtain the analysis results. Specifically, the further detailed description of this step is the same as that of step S105 above, and will not be repeated here.

[0079] Specifically, step S202 above includes:

[0080] Step S2021: Classify and process the data to obtain dimensional parameters, microstructure parameters, and material parameters. Specifically, the dimensional parameters are obtained through 3D reverse engineering. For example, the blade number before service is designated as O1, and the blades after service at different service cycles are designated as S1, S2, S3, S4, and S5. The 3D reverse engineering of the blades after service includes: 3D data measurement, 3D data processing, 3D reconstruction, and 3D model data output. During measurement, a heavy-duty gas turbine blade positioning fixture is first used to position the blades after service. A 3D scanner is then opened for calibration, a scanning base is established, and a continuous 3D scan of the blade is performed at a 45° angle. After scanning around the blade, a 3D point cloud image and 3D point cloud data of the blade are obtained. After processing the data, the shape and dimensional parameters of different parts of the blade of this model at different service cycles are obtained based on the coordinates. The microstructure parameters mainly include: grain size / number of grains, primary dendrite spacing, γ' phase morphology, γ' phase size, γ' phase volume fraction, γ' phase raft perfection, γ' phase raft thickness, percentage content of each type of carbide, percentage content of each type of TCP precipitate, and creep porosity and percentage content. The material parameters include the alloy type, chemical composition and content, heat treatment regime, physical properties, elastic properties, chemical properties, and mechanical properties of the alloy corresponding to the blade matrix.

[0081] Step S2022: By comparing and analyzing the dimensional parameters of gas turbine blades at different service cycles after service with those before service, multiple deformation locations and the degree of deformation corresponding to each deformation location of the gas turbine blades at different service cycles are obtained. Specifically, by comparing the dimensions, the degree of deformation of the blade's shape after service can be reflected most intuitively.

[0082] Step S2023: Based on dimensional parameters, deformation locations, and microstructure parameters, perform microstructure analysis on gas turbine blades at different service life stages to obtain microstructure data corresponding to each deformation location of the gas turbine blades at different service life stages. Specifically, after entering service, the microstructure of the blades changes with increasing service time, such as... Figure 3 As shown, dendrite morphology, γ' phase morphology, carbides, and matrix morphology all change with service time. By analyzing the tissue state data of blades under different service cycles, the accuracy and reliability of the blade health status database can be effectively improved.

[0083] Step S2024: Analyze the creep stages of gas turbine blades at different service cycles based on material parameters, microstructure data, and a pre-set creep test database to obtain creep stage data corresponding to each deformation location of the gas turbine blades at different service cycles. Specifically, by establishing a pre-set creep test database and obtaining creep stage data for each blade through comparative analysis, a blade health status database is established. This allows for non-destructive assessment of the gas turbine blades to be evaluated. Compared to the existing method of assessment through dissection, this can effectively extend the service life of blades that can continue to serve.

[0084] Step S2025: Compare the deformation degree, microstructure data and creep stage data corresponding to each deformation position of the gas turbine blade at different service cycles with the preset deformation level requirements to obtain the deformation level corresponding to different dimensional parameters, microstructure parameters and material parameters, and establish the mapping relationship between the deformation level and the dimensional difference data and the deformation position corresponding to the dimensional difference data.

[0085] Step S2026: Establish a blade health status database based on the mapping relationship between deformation level and dimensional difference data and the deformation position corresponding to the dimensional difference data.

[0086] Specifically, when the microstructure of the service blade corresponds to that of the standard creep specimen in the standard heat-treated state, its deformation degree is 0; when the deformation location of the service blade corresponds to the microstructure of the standard creep specimen in the middle of the first stage of creep, its deformation degree is Class I; when the deformation location of the service blade corresponds to the microstructure of the standard creep specimen in the early stage of the second stage of creep, its deformation degree is Class II; when the deformation location of the service blade corresponds to the microstructure of the standard creep specimen in the middle of the second stage of creep, its deformation degree is Class III; when the deformation location of the service blade corresponds to the microstructure of the standard creep specimen in the late stage of the second stage of creep, its deformation degree is Class IV, indicating that the microstructure of this part is about to enter the third stage of creep. At this time, the safe service of the blade is not reliable, and an early warning is needed for blade maintenance or replacement; when creep enters the third stage, that is, the accelerated creep stage, defects such as creep pores will grow, connect and form cracks at a faster rate, which will accelerate the creep rate and further lead to creep fracture. Therefore, when the deformation location of the blade in service corresponds to the microstructure of the standard creep specimen in the middle of the third stage of creep, and when the standard creep specimen fractures, the corresponding degree of deformation is classified as Level IV deformation.

[0087] By comparing deformation degree, microstructure, and creep stage data with preset deformation level requirements, deformation levels corresponding to different dimensional, microstructure, and material parameters are obtained. A mapping between these parameters and deformation levels is established to create a blade health status database. This data source is more reliable, and the analysis accuracy is higher, thereby improving the accuracy and reliability of subsequent analysis of the deformation degree of the gas turbine blades under evaluation based on the blade health status database. Furthermore, by establishing a blade health status database, non-destructive evaluation of the gas turbine blades under evaluation can be performed. Compared to the existing method of assessment through dissection, non-destructive evaluation allows serviceable blades to continue operating, reducing the economic losses caused by blade scrapping due to dissection.

[0088] Existing non-destructive testing technologies, such as magnetic particle testing, penetrant testing, ultrasonic testing, and radiographic testing, primarily detect and analyze existing cracks in blades. They are unreliable for detecting creep deformation caused by prolonged service under high temperatures, which alters the matrix structure. This application establishes a database by comprehensively analyzing deformation degree, microstructure data, and creep stage data, effectively avoiding this risk and offering higher safety and reliability.

[0089] Specifically, when establishing a health status database, a mapping can be established between the service stage corresponding to the dimensional difference data and the deformation level. When evaluating the turbine blades of the gas turbine to be evaluated, the service stage can be comprehensively considered while taking into account the dimensional difference data and the location of the maximum deformation, thereby making the results more accurate.

[0090] In some optional implementations, step S2022 above includes:

[0091] Step a1: Compare the dimensional parameters of each service cycle with the dimensional parameters before service to obtain multiple deformation locations and the corresponding geometric deviation data for each deformation location.

[0092] Step a2: Compare the geometric deviation data with multiple preset deformation ranges to obtain the deformation degree corresponding to each deformation position.

[0093] Specifically, by comparing the geometric deviations of different parts of the blade O0 before service and the blades S1, S2, S3, S4, and S5 after service, the degree of deformation of the blades is classified according to the geometric deviation of each blade. For example, the degree of deformation is classified as Level I for 0-1mm, Level II for 1-2mm, Level III for 2-3mm, and Level IV for >3mm. The range of deformation classification can be set according to actual needs, and no specific restrictions are imposed here.

[0094] By comparing geometric deviation data with multiple preset deformation ranges, the degree of deformation can be quickly and intuitively determined, with high efficiency and accuracy.

[0095] In some optional implementations, step S2023 above includes:

[0096] Step b1: Extract the coordinate information corresponding to the deformation position from the dimensional parameters.

[0097] Step b2: Analyze the coordinate information to obtain the deformation feature points.

[0098] Step b3: Extract tissue state data for each deformation feature point from the tissue parameters.

[0099] Specifically, by dissecting blades O0, S1, S2, S3, S4, and S5 according to preset cross-sections, the inlet edge, blade back, blade base, and exhaust edge of each cross-section can be used as basic observation points. Tissue analysis is then performed on various deformation characteristic points of the in-service blades. The cross-sections are either cross-sectional or longitudinal. Simultaneously, the locations corresponding to these deformation characteristic points within the O0, S1, S2, S4, and S5 blades are identified and analyzed to obtain tissue state data for each deformation characteristic point. By analyzing the tissue state data of blades under different service cycles, the accuracy and reliability of the blade health status database can be effectively improved.

[0100] In some optional implementations, step S2024 above includes:

[0101] Step c1 involves extracting test data corresponding to the material parameters from a pre-set creep test database. This data includes microstructure data corresponding to different creep stages. Specifically, the pre-set creep test database is obtained by conducting multiple sets of creep tests on the alloy material corresponding to the blade under near-service conditions. Standard creep specimens are selected, and the following parameters are observed and statistically analyzed for standard creep specimens at standard heat-treated state C0, mid-creep stage C1, early creep stage C2, mid-creep stage C3, late creep stage C4, mid-creep stage C5, and creep fracture C6: grain size / number of grains, primary dendrite spacing, γ' phase morphology, γ' phase size, γ' phase volume fraction, γ' phase raft perfection, γ' phase raft thickness, percentage content of various carbide types, percentage content of various TCP precipitates, and creep porosity and percentage content.

[0102] Step c2 involves comparing the tissue state data with the experimental data to obtain the creep stage data corresponding to each deformation location.

[0103] Specifically, since existing technologies require dissection to determine creep conditions, this application establishes a pre-set creep test database and obtains creep stage data for each blade through comparative analysis. By establishing a blade health status database in this way, non-destructive assessment of the turbine blades of the gas turbine to be evaluated can be performed. Compared with the existing technology of determining creep conditions through dissection, non-destructive assessment can allow serviceable blades to continue to serve, reducing the economic losses caused by blade scrapping due to dissection.

[0104] This embodiment provides a method for analyzing the service reliability of gas turbine blades, which can be used to analyze the service status of gas turbine blades. Figure 4 This is a flowchart of a gas turbine blade service reliability analysis method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps:

[0105] Step S301: Acquire data information of the gas turbine blades during multiple service cycles before and after service, as well as measurement data of the gas turbine blades to be evaluated. Specifically, the further detailed description of this step is the same as that of step S101 above, and will not be repeated here.

[0106] Step S302: Establish a leaf health status database based on the data information. Specifically, the detailed description of this step is the same as that of step S102 above, and will not be repeated here.

[0107] Step S303: Analyze the dimensional difference data of the maximum deformation location of the gas turbine blade to be evaluated based on the measurement data and the pre-service data of the gas turbine blade. Specifically, the further detailed description of this step is the same as that of step S103 above, and will not be repeated here.

[0108] Step S304: Based on the dimensional difference data, the location of maximum deformation, and the blade health status database, a comparative analysis is performed on the turbine blades of the gas turbine to be evaluated to obtain the deformation level. Specifically, the further detailed description of this step is the same as that of step S104 above, and will not be repeated here.

[0109] Step S305: Compare the deformation level with the preset safety level to obtain the analysis results. Specifically, the further detailed description of this step is the same as that of step S105 above, and will not be repeated here.

[0110] Specifically, step S304 above includes:

[0111] Step S3041: Filter multiple deformation locations corresponding to the size difference data from the blade health status database.

[0112] Step S3042: Compare the maximum deformation position with the deformation position to obtain the coordinate difference between each deformation position and the maximum deformation position.

[0113] Step S3043: Select the position with the smallest coordinate difference from the deformed positions as the target position.

[0114] Step S3044: Extract the deformation level corresponding to the target location from the blade health status database based on the target location.

[0115] Specifically, by selecting the deformation level corresponding to the closest position with the same degree of deformation, it better matches the actual condition of the turbine blade of the gas turbine to be evaluated, and has higher accuracy and reliability.

[0116] This embodiment provides a method for analyzing the service reliability of gas turbine blades, which can be used to analyze the service status of gas turbine blades. Figure 5 This is a flowchart of a gas turbine blade service reliability analysis method according to an embodiment of the present invention, such as... Figure 5 As shown, the process includes the following steps:

[0117] Step S401: Acquire data information of the gas turbine blades during multiple service cycles before and after service, as well as measurement data of the gas turbine blades to be evaluated. Specifically, the further detailed description of this step is the same as that of step S101 above, and will not be repeated here.

[0118] Step S402: Establish a leaf health status database based on the data information. Specifically, the detailed description of this step is the same as that of step S102 above, and will not be repeated here.

[0119] Step S403: Analyze the dimensional difference data of the maximum deformation location of the gas turbine blade to be evaluated based on the measurement data and the pre-service data of the gas turbine blade. Specifically, the further detailed description of this step is the same as that of step S103 above, and will not be repeated here.

[0120] Step S404: Based on the dimensional difference data, the location of maximum deformation, and the blade health status database, a comparative analysis is performed on the turbine blades of the gas turbine to be evaluated to obtain the deformation level. Specifically, the further detailed description of this step is the same as that of step S104 above, and will not be repeated here.

[0121] Step S405: Compare the deformation level with the preset safety level to obtain the analysis results. Specifically, the further detailed description of this step is the same as that of step S105 above, and will not be repeated here.

[0122] Specifically, step S405 above includes:

[0123] Step S4051: Compare the deformation level with the preset safety level.

[0124] Step S4052: When the deformation level is less than the preset safety level, the blade condition of the gas turbine blade to be evaluated is healthy.

[0125] Step S4053: When the deformation level is greater than or equal to the preset safety level, the blade condition of the gas turbine blade to be evaluated is dangerous.

[0126] Specifically, if the blade condition of the gas turbine blade to be evaluated is healthy, it means that the gas turbine blade to be evaluated can continue to serve and has high service reliability; when the blade condition of the gas turbine blade to be evaluated is dangerous, the blade service reliability is low and timely maintenance or replacement is required, and technical support should be provided to maintenance personnel.

[0127] In some optional implementations, step S4053 includes: when the blade condition of the gas turbine blade to be evaluated is dangerous, reporting the deformation level of the gas turbine blade to be evaluated.

[0128] Specifically, by reporting the deformation level, maintenance personnel can promptly identify potential safety hazards and take timely measures to repair or replace the gas turbine blades, thus better ensuring safety.

[0129] This embodiment also provides a gas turbine blade service reliability analysis device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0130] This embodiment provides a device for analyzing the service reliability of gas turbine blades, such as... Figure 6 As shown, it includes:

[0131] The acquisition module 601 is used to acquire data information of gas turbine blades before and after service for multiple service cycles, as well as measurement data of gas turbine blades to be evaluated.

[0132] Module 602 is established to create a database of blade health status based on data information.

[0133] Analysis module 603 is used to analyze the dimensional difference data of the maximum deformation position of the gas turbine blade to be evaluated based on measurement data and pre-service data of the gas turbine blade.

[0134] The deformation module 604 is used to compare and analyze the turbine blades of the gas turbine to be evaluated based on the dimensional difference data, the location of the maximum deformation, and the blade health status database to obtain the deformation level.

[0135] The comparison module 605 is used to compare the deformation level with the preset safety level to obtain the analysis results.

[0136] In some alternative implementations, the establishment module 602 includes:

[0137] The classification unit is used to classify and process data information to obtain dimensional parameters, microstructure parameters, and material parameters.

[0138] The comparison unit is used to compare and analyze the dimensional parameters of the gas turbine blades at different service cycles after service with the dimensional parameters of the gas turbine blades before service, so as to obtain multiple deformation positions of the gas turbine blades at different service cycles and the degree of deformation corresponding to each deformation position.

[0139] The microstructure analysis unit is used to perform microstructure analysis on gas turbine blades of different service cycles based on dimensional parameters, deformation locations, and microstructure parameters, and to obtain microstructure data corresponding to each deformation location of the gas turbine blades of different service cycles.

[0140] The creep analysis unit is used to analyze the creep stages of gas turbine blades at different service cycles based on material parameters, microstructure data, and a preset creep test database, and to obtain creep stage data corresponding to each deformation position of the gas turbine blades at different service cycles.

[0141] The mapping unit is used to compare the deformation degree, microstructure and creep stage data of each deformation position of the gas turbine blade at different service cycles with the preset deformation level requirements, obtain the deformation level corresponding to different size parameters, microstructure parameters and material parameters, and establish the mapping relationship between the deformation level and the size difference data and the deformation position corresponding to the size difference data.

[0142] A unit is established to create a blade health status database based on the mapping relationship between deformation level and dimensional difference data and the corresponding deformation position of dimensional difference data.

[0143] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0144] In this embodiment, the gas turbine blade service reliability analysis device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0145] Further functional descriptions of the above modules are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0146] This invention also provides a computer device having the above-described features. Figure 6 The device shown is for analyzing the service reliability of gas turbine blades.

[0147] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 7As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take a processor 10 as an example.

[0148] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0149] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0150] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0151] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0152] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0153] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0154] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for analyzing the service reliability of gas turbine blades, characterized in that, include: Acquire data on gas turbine blades during multiple service cycles before and after service, as well as measurement data on the gas turbine blades to be evaluated; A leaf health status database is established based on the aforementioned data information; Based on the measurement data and the pre-service data of the gas turbine blades, the dimensional difference data of the maximum deformation location of the gas turbine blades to be evaluated was analyzed. Based on the dimensional difference data, the location of maximum deformation, and the blade health status database, the deformation level of the gas turbine blade to be evaluated is obtained by comparative analysis. The deformation level is compared with the preset safety level to obtain the analysis results; The process of establishing a leaf health status database based on the data information includes: The data information is classified and processed to obtain dimensional parameters, microstructure parameters, and material parameters; By comparing and analyzing the dimensional parameters of gas turbine blades after different service cycles after service with the dimensional parameters of gas turbine blades before service, multiple deformation positions of gas turbine blades after different service cycles and the degree of deformation corresponding to each deformation position are obtained. Based on the dimensional parameters, the deformation location, and the microstructure parameters, microstructure analysis was performed on the turbine blades of gas turbines with different service cycles to obtain microstructure data corresponding to each deformation location of the turbine blades of gas turbines with different service cycles. Based on the material parameters, the microstructure data, and the preset creep test database, the creep stages of gas turbine blades in different service cycles are analyzed to obtain creep stage data corresponding to each deformation position of the gas turbine blades in different service cycles. The deformation degree, microstructure data and creep stage data corresponding to each deformation position of the gas turbine blade at different service cycles are compared with the preset deformation level requirements to obtain the deformation level corresponding to different size parameters, microstructure parameters and material parameters, and a mapping relationship is established between the deformation level and the size difference data and the deformation position corresponding to the size difference data. A blade health status database is established based on the mapping relationship between the deformation level, the size difference data, and the deformation position corresponding to the size difference data; The deformation level is obtained by comparing and analyzing the gas turbine blade to be evaluated based on the dimensional difference data, the location of maximum deformation, and the blade health status database, including: Filter multiple deformation locations corresponding to the size difference data from the leaf health status database; The maximum deformation position is compared with the deformation position to obtain the coordinate difference between each deformation position and the maximum deformation position; Select the position with the smallest coordinate difference from the deformed positions as the target position; Based on the target location, the deformation level corresponding to the target location is extracted from the blade health status database.

2. The method for analyzing the service reliability of gas turbine blades according to claim 1, characterized in that, The method involves comparing and analyzing the dimensional parameters of gas turbine blades at different service cycles after service with those before service to obtain multiple deformation locations and the degree of deformation corresponding to each deformation location, including: The dimensional parameters for each service cycle are compared with the dimensional parameters before service to obtain multiple deformation locations and geometric deviation data corresponding to each deformation location; The geometric deviation data is compared with multiple preset deformation ranges to obtain the deformation degree corresponding to each deformation position.

3. The method for analyzing the service reliability of gas turbine blades according to claim 1, characterized in that, The microstructure analysis of gas turbine blades at different service cycles is performed based on the dimensional parameters, deformation locations, and microstructure parameters to obtain microstructure data corresponding to each deformation location of the gas turbine blades at different service cycles, including: Extract the coordinate information corresponding to the deformation position from the dimensional parameters; Deformation feature points are obtained by analyzing the coordinate information. Tissue state data for each deformation feature point is extracted from the tissue parameters.

4. The method for analyzing the service reliability of gas turbine blades according to claim 1, characterized in that, The process involves analyzing the creep stages of gas turbine blades at different service lifespans based on the material parameters, microstructure data, and a preset creep test database. This yields creep stage data corresponding to each deformation position of the gas turbine blades at different service lifespans, including: Extract test data corresponding to the material parameters from the preset creep test database. The test data includes test microstructure data corresponding to different creep stages. By comparing the tissue state data with the experimental data, creep stage data corresponding to each deformation location are obtained.

5. The method for analyzing the service reliability of gas turbine blades according to claim 1, characterized in that, The step of comparing the deformation level with the preset safety level to obtain the analysis results includes: Compare the deformation level with the preset safety level; When the deformation level is less than the preset safety level, the blade condition of the gas turbine blade to be evaluated is healthy; When the deformation level is greater than or equal to the preset safety level, the blade condition of the gas turbine blade to be evaluated is dangerous.

6. A device for analyzing the service reliability of gas turbine blades, characterized in that, include: The acquisition module is used to acquire data information of gas turbine blades before and after multiple service cycles, as well as measurement data of gas turbine blades to be evaluated. A module is established to create a leaf health status database based on the data information; The analysis module is used to analyze the dimensional difference data of the maximum deformation position of the gas turbine blade to be evaluated based on the measurement data and the data information of the gas turbine blade before service. The deformation module is used to compare and analyze the gas turbine blade to be evaluated based on the size difference data, the location of the maximum deformation, and the blade health status database to obtain the deformation level; The comparison module is used to compare the deformation level with the preset safety level to obtain the analysis results; The modules to be built include: The classification unit is used to classify and process data information to obtain dimensional parameters, microstructure parameters, and material parameters; The comparison unit is used to compare and analyze the dimensional parameters of the gas turbine blades at different service cycles after service with the dimensional parameters of the gas turbine blades before service, so as to obtain multiple deformation positions of the gas turbine blades at different service cycles and the degree of deformation corresponding to each deformation position. The microstructure analysis unit is used to perform microstructure analysis on gas turbine blades with different service cycles based on dimensional parameters, deformation locations, and microstructure parameters, and to obtain microstructure state data corresponding to each deformation location of the gas turbine blades with different service cycles. The creep analysis unit is used to analyze the creep stages of gas turbine blades at different service cycles based on material parameters, microstructure data and a preset creep test database, and to obtain creep stage data corresponding to each deformation position of the gas turbine blade at different service cycles. The mapping unit is used to compare the deformation degree, microstructure and creep stage data of each deformation position of the gas turbine blade at different service cycles with the preset deformation level requirements, to obtain the deformation level corresponding to different size parameters, microstructure parameters and material parameters, and to establish the mapping relationship between the deformation level and the size difference data and the deformation position corresponding to the size difference data. Establish a unit to build a blade health status database based on the mapping relationship between deformation level and dimensional difference data and the deformation position corresponding to the dimensional difference data; The deformation module is specifically used for: filtering multiple deformation locations corresponding to the size difference data from the blade health status database; comparing the maximum deformation location with the deformation location to obtain the coordinate difference between each deformation location and the maximum deformation location; selecting the deformation location with the smallest coordinate difference as the target location; and extracting the deformation level corresponding to the target location from the blade health status database based on the target location.

7. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the service reliability analysis method for gas turbine blades as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the gas turbine blade service reliability analysis method according to any one of claims 1-5.

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