A method and system for predicting fatigue life of a main steam pipe elbow of a thermal power unit

Abstract

This invention discloses a method and system for predicting the fatigue life of elbows in main steam pipelines of thermal power units, relating to the field of safety monitoring and operation and maintenance technology for thermal power plant equipment. The prediction method includes acquiring operational monitoring data of the elbow; determining multiple stress cycle characteristic parameters for fatigue life prediction based on the operational monitoring data, wherein the stress cycle characteristic parameters include at least the equivalent stress amplitude of each stress cycle; calculating a cumulative fatigue damage factor based on each equivalent stress amplitude and its corresponding cycle number, combined with correction coefficients related to the structural characteristics and operating temperature characteristics of the elbow; and calculating the predicted fatigue life of the elbow using a fatigue life prediction model based on the cumulative fatigue damage factor. This method significantly improves the accuracy and engineering guidance value of the prediction results, providing a reliable basis for the safe operation and preventative maintenance of equipment.

Description

Technical Field

[0001] This invention belongs to the field of safety monitoring and operation and maintenance technology of thermal power plant equipment, specifically involving a method and system for predicting the fatigue life of the elbow of the main steam pipeline of a thermal power unit. Background Technology

[0002] The main steam pipeline elbow is a core pressure-bearing component of the steam transmission system in a thermal power plant. It endures the combined effects of high temperature and pressure, centrifugal force, temperature fluctuations, and scouring loads over long periods. The accumulation and evolution of fatigue damage directly determines the operational safety and service life of the pipeline. Therefore, accurate fatigue life prediction of the main steam pipeline elbow is a crucial technical aspect for ensuring the safe and economical operation of thermal power plants.

[0003] Currently, the industry's prediction of fatigue life for such critical pressure-bearing components mainly relies on classical fatigue theories (such as Miner's linear cumulative damage theory) and general fatigue analysis models. The conventional approach is to collect the component's operating stress data, perform load spectrum analysis using methods such as rainflow counting, and then combine this with the material's fatigue performance curves to estimate the life.

[0004] However, this generalized technical approach reveals significant limitations when applied to the specific object of the main steam pipeline elbow in thermal power units: First, existing methods fail to adequately adapt to the unique mechanical characteristics of the elbow itself. The stress concentration effect caused by the elbow's geometry and the degradation of material properties under high-temperature environments are the dominant factors in its fatigue damage. However, general models often simplify it as a uniform component without introducing targeted corrections, leading to a disconnect between the prediction model and the actual damage mechanism, resulting in poor adaptability. Second, existing methods lack systematicity and rigor in integrating and applying core fatigue algorithms. From the preprocessing of raw stress data to the cyclic analysis of complex alternating loads (such as cyclic screening rules), and then to the correction considering the influence of average stress (such as the specific application of the Goodman criterion), each step is often fragmented or simplified, failing to form a logical closed loop, resulting in insufficient accuracy in the final damage calculation. Summary of the Invention

[0005] The purpose of this invention is to overcome the problems of insufficient adaptability of general fatigue prediction models to the specific characteristics of main steam pipeline elbows in the existing technology, and the lack of systematicness and rigor in the application of key fatigue algorithms, resulting in low prediction accuracy. This invention provides a method and system for predicting the fatigue life of main steam pipeline elbows in thermal power units.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for predicting the fatigue life of elbows in main steam pipelines of thermal power units, comprising the following steps: Obtain the operational monitoring data of the elbow; Based on the operational monitoring data, multiple stress cycle characteristic parameters for fatigue life prediction are determined, wherein the stress cycle characteristic parameters include at least the equivalent stress amplitude of each stress cycle. Based on each equivalent stress amplitude and its corresponding number of cycles, and combined with correction coefficients related to the elbow structural characteristics and operating temperature characteristics, the cumulative fatigue damage factor is calculated. Based on the cumulative fatigue damage factor, the predicted fatigue life of the elbow is calculated using a fatigue life prediction model.

[0007] A further improvement of the present invention is that the step of determining multiple stress cycle characteristic parameters for fatigue life prediction specifically includes: Based on the operational monitoring data, multiple effective stress cycles are extracted, and the stress amplitude and average stress of each stress cycle are obtained. The stress amplitude of each stress cycle is corrected to the equivalent stress amplitude based on the corresponding average stress.

[0008] A further improvement of the present invention is that the step of extracting multiple effective stress cycles and obtaining the stress amplitude and average stress of each stress cycle specifically includes: The collected stress data of the elbow is preprocessed to obtain stress time series data; The stress time series data were analyzed using the rainflow counting method to extract multiple stress cycles; Calculate the stress amplitude and mean stress for each extracted stress cycle.

[0009] A further improvement of the present invention is that, when analyzing the stress time series data using the rainflow counting method, it further includes: A minimum stress amplitude threshold is set, and stress cycles with stress amplitudes lower than the minimum stress amplitude threshold are identified as invalid cycles and discarded.

[0010] A further improvement of the present invention is that the correction factor includes a bending stress concentration correction factor. and temperature fluctuation correction factor ; The cumulative fatigue damage factor D is calculated according to the following formula:

[0011] in, For the first The actual number of cycles corresponding to the equivalent stress amplitude of the first level. This represents the theoretical fatigue life corresponding to the equivalent stress amplitude. It is the total series of effective equivalent stress amplitudes.

[0012] A further improvement of this invention is that the fatigue life prediction model is as follows:

[0013] in, To predict fatigue life, The fatigue life of the material under standard operating conditions. The cumulative fatigue damage factor, The peak bending stress of the elbow is taken as the value. It is a function with the independent variable.

[0014] A further improvement of the present invention is that it also includes a dynamic update step: acquiring monitoring data of the elbow in a new operating cycle, and based on the monitoring data, repeatedly executing the fatigue life prediction method for the main steam pipeline elbow of the thermal power unit to update the predicted fatigue life of the elbow.

[0015] Secondly, the present invention provides a fatigue life prediction system for the elbow of the main steam pipeline of a thermal power unit, comprising the following modules: The data acquisition module is used to acquire the operation monitoring data of the elbow; The feature parameter determination module is used to determine multiple stress cycle feature parameters for fatigue life prediction based on the operation monitoring data. The stress cycle feature parameters include at least the equivalent stress amplitude of each stress cycle. The cumulative damage calculation module is used to calculate the cumulative fatigue damage factor based on each equivalent stress amplitude and its corresponding number of cycles, combined with correction coefficients related to the elbow structural characteristics and operating temperature characteristics. The life prediction module is used to calculate the predicted fatigue life of the elbow based on the cumulative fatigue damage factor and through a fatigue life prediction model.

[0016] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-mentioned method for predicting the fatigue life of the main steam pipeline elbow of a thermal power unit.

[0017] Fourthly, the present invention provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for predicting the fatigue life of the main steam pipeline elbow of a thermal power unit.

[0018] Compared with the prior art, the present invention has the following beneficial effects: This invention discloses a method for predicting the fatigue life of elbows in main steam pipelines of thermal power units. By acquiring operational monitoring data of the elbows, characteristic parameters, including the equivalent stress amplitude of each stress cycle, are determined. Then, a cumulative fatigue damage factor is calculated using correction coefficients related to the elbow structure and operating temperature characteristics. Finally, a predicted life value is obtained based on this factor through a prediction model. The core advantage of this method lies in constructing a complete, closed-loop technical framework from data to damage to life. By systematically integrating data processing, stress correction, and damage accumulation calculation, the overall logical rigor and calculation accuracy of fatigue life prediction are significantly improved. Specifically, the method explicitly introduces specific correction coefficients for the elbow's structural characteristics (such as bending stress concentration) and actual operating environment characteristics (such as temperature fluctuations). This allows general fatigue theory to effectively adapt to the unique service conditions of elbows in main steam pipelines of thermal power units, overcoming the insufficient adaptability of traditional general models when dealing with local stress concentration and complex thermo-mechanical coupling environments in elbows. This makes the prediction results more closely match the damage evolution law of actual components, greatly enhancing the reliability and engineering guidance value of the evaluation results. Furthermore, the standardized process formed by this method has good repeatability and scalability, and can provide stable and consistent technical support for the condition assessment and operation and maintenance decision-making of elbows in different power plant environments, which is conducive to the refinement and scientification of preventive maintenance and life management. Attached Figure Description

[0019] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the invention in any way. Furthermore, the shapes and proportions of the components in the drawings are merely illustrative to aid in understanding the invention and do not specifically limit the shapes and proportions of the components of the invention.

[0020] Figure 1 A schematic diagram of the method for predicting the fatigue life of elbows in the main steam pipeline of a thermal power unit. Figure 2 A schematic diagram of the fatigue life prediction system for the main steam pipeline elbow of a thermal power unit. Figure 3 This is a schematic diagram of an electronic device. Detailed Implementation

[0021] 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, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0023] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0024] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0025] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0026] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0027] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1 like Figure 1 As shown, this invention provides a method for predicting the fatigue life of elbows in main steam pipelines of thermal power units, comprising the following steps: S1, Obtain the operation monitoring data of the elbow; Specifically, it includes the following sub-steps: Operating conditions are classified as follows: Based on the operating rules of thermal power plants and the stress characteristics of the main steam pipeline bends, the operating conditions are classified into start-up and shutdown conditions (sudden changes in temperature and pressure, instantaneous fluctuations in bending stress and thermal stress), rated load conditions (stable parameters, constant centrifugal force and bending stress), variable load conditions (dynamic adjustment of parameters, continuous changes in stress gradient), and abnormal disturbance conditions (sudden pressure rise or airflow fluctuations, causing stress impact). Determining core parameters: Determine the key parameters used for fatigue life calculation, including material property parameters, elbow-specific characteristic parameters, and basic operating parameters; Among them, the material performance parameters include the symmetrical cyclic fatigue limit, tensile strength, and material fatigue cycle curve; Elbow-specific characteristic parameters include peak bending stress, average operating temperature, bending radius, and pipe wall thickness; Basic operating parameters include the time elapsed since the bend was started and the percentage of cumulative running time for each operating condition.

[0028] S2, Based on the operational monitoring data, determine multiple stress cycle characteristic parameters for fatigue life prediction, wherein the stress cycle characteristic parameters include at least the equivalent stress amplitude of each stress cycle; Specifically, it includes the following sub-steps: (1) Data acquisition and preprocessing: Based on the stress and operating characteristics of the main steam pipeline elbow, data on bending stress distribution, average operating temperature, and temperature fluctuation during operation should be collected, along with the elbow's operating time and material performance parameters. The data collection cycle should cover at least one complete operating cycle.

[0029] The collected stress, temperature, and pressure data undergo cleaning and standardization preprocessing, including: Identify and remove outliers from the data, and verify them in conjunction with operating condition logic to eliminate invalid data; For consecutive missing data segments, linear interpolation is used to fill them in, ensuring the integrity of the data sequence. The original stress data were smoothed using a moving average method to eliminate sensor noise interference.

[0030] (2) Calculation of key stress parameters: Based on the preprocessed data, key stress parameters are calculated in segments according to different working conditions, including: Maximum stress: Take the peak value in the stress time series data under each working condition, and take the statistical maximum value of multiple cycles under the same working condition as the maximum stress under that working condition. Minimum stress: Take the valley value in the stress time series data under each working condition, and take the statistical minimum value of multiple cycles of the same working condition as the minimum stress of that working condition; Stress amplitude, calculated using the following formula:

[0031] Reflects the fluctuation amplitude of stress cycling; The average stress is calculated using the following formula:

[0032] Quantify the mean level of stress cycles; The stress ratio is calculated using the following formula:

[0033] Used to distinguish stress cycle types.

[0034] (3) Stress cycle decomposition: An improved rainflow counting method is used to analyze complex load spectra, specifically including: Counting process: Arrange stress time series data in chronological order, and divide stress segments with peak and valley values ​​as dividing points; starting from each peak or valley value, "rain" down along the stress-time curve, and stop when the rain flow encounters a peak value higher than the starting point or a valley value lower than the starting point, forming a complete stress cycle; Cyclic screening criteria: Set minimum stress amplitude threshold Invalid cycles with stress amplitudes below the threshold are eliminated to avoid calculation errors introduced by minor fluctuations. Cycle merging rule: Cycles at the same stress level are merged and counted, and the total number of cycles is accumulated.

[0035] in, For the first Number of stress cycles, This represents the effective stress level number; Counting result output: A set of parameters is output for each effective stress cycle. This forms a standardized stress cycle matrix.

[0036] S3, the stress amplitude of each stress cycle is corrected to the equivalent stress amplitude based on the corresponding average stress; Specifically, it includes the following sub-steps: Equivalent stress amplitude correction: The mean stress effect is corrected based on the Goodman criterion, specifically including: Cycle type determination: based on stress ratio Define the loop type, if For a pure stretching cycle, if It contains compression cycles; The stress cycles are corrected using a modified formula, which is as follows:

[0037] in, For the first The equivalent amplitude of the cyclic loop, The symmetrical cyclic fatigue limit of the material. The tensile strength of the material; Correction validity check: If the corrected equivalent stress amplitude If the cycle has no significant effect on fatigue damage, it is determined that the cycle is excluded.

[0038] S4. Based on each equivalent stress amplitude and its corresponding number of cycles, and combined with the correction coefficients related to the elbow structural characteristics and operating temperature characteristics, the cumulative fatigue damage factor is calculated. Specifically, it includes the following sub-steps: (1) Calculation of fatigue damage factor: The fatigue damage factor is calculated based on Miner's linear cumulative damage theory, including: Material fatigue curve fitting: Transforming the material's S-N curve into a mathematical expression:

[0039] in, , These are the material fatigue characteristic constants obtained by fitting experimental data; Single-level damage calculation: The damage value for a stress cycle of level 1 is:

[0040] in, This represents the actual cumulative number of times. This represents the fatigue life corresponding to the equivalent stress amplitude; Total damage factor calculation: Introducing the bending stress concentration correction factor for elbows. (Calculated based on the ratio of elbow bending radius to wall thickness) and temperature fluctuation correction factor (Based on temperature fluctuation amplitude calculation), the formula for calculating the total damage factor is:

[0041] Damage accumulation check: Accumulate the damage values ​​of all valid cycles. If the damage is from a single cycle... If so, the operating condition information of the high-damage cycle is recorded separately to provide key reference for subsequent life assessment.

[0042] (2) Data standardization processing: The damage factor and key parameters were normalized using the min-max normalization method. in, These are the original parameter values. , These are the historical minimum and maximum values ​​of the parameter, respectively; after standardization, the parameter range is [0,1], providing uniform dimensional data for the input of subsequent life prediction models.

[0043] S5. Based on the cumulative fatigue damage factor, the predicted fatigue life of the elbow is calculated using a fatigue life prediction model. Specifically, based on the damage mechanism and quantification results of the elbow, and combined with its specific characteristic parameters, a fatigue life prediction model is constructed:

[0044] in, To predict fatigue life, The fatigue life of the material under standard operating conditions. The cumulative fatigue damage factor, The peak bending stress of the elbow is taken as the value. It is a function with the independent variable.

[0045] The cumulative fatigue damage factor calculated in S2 By inputting the above model and combining it with the elbow's specific characteristic parameters, the predicted fatigue life of the elbow can be calculated.

[0046] S6, dynamically updated; During each maintenance cycle, perform the following dynamic update steps: Collect new operating data and test results for the elbow; Based on the new data, repeat steps S2 to S5 to update the stress cycle matrix and cumulative fatigue damage factor. And predicting fatigue life .

[0047] Periodic updates enable the model to adapt to factors such as equipment aging and changes in operating conditions, continuously ensuring the accuracy and reliability of prediction results.

[0048] Example 2 like Figure 2 As shown, the present invention also provides a fatigue life prediction system for the main steam pipeline elbow of a thermal power unit, comprising the following modules: The data acquisition module is used to acquire the operation monitoring data of the elbow; The feature parameter determination module is used to determine multiple stress cycle feature parameters for fatigue life prediction based on the operation monitoring data. The stress cycle feature parameters include at least the equivalent stress amplitude of each stress cycle. The cumulative damage calculation module is used to calculate the cumulative fatigue damage factor based on each equivalent stress amplitude and its corresponding number of cycles, combined with correction coefficients related to the elbow structural characteristics and operating temperature characteristics. The life prediction module is used to calculate the predicted fatigue life of the elbow based on the cumulative fatigue damage factor and through a fatigue life prediction model.

[0049] Example 3 Please see Figure 3 As shown, the present invention also provides an electronic device 100 for predicting the fatigue life of the elbow of the main steam pipeline of a thermal power unit; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and capable of running on the at least one processor 102, and at least one communication bus 104.

[0050] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the fatigue life prediction method for the main steam pipeline elbow of the thermal power unit described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0051] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0052] The memory 101 in the electronic device 100 stores multiple instructions to implement a fatigue life prediction method for the main steam pipeline elbow of a thermal power unit, and the processor 102 can execute the multiple instructions to achieve the following: Obtain the operational monitoring data of the elbow; Based on the operational monitoring data, multiple stress cycle characteristic parameters for fatigue life prediction are determined, wherein the stress cycle characteristic parameters include at least the equivalent stress amplitude of each stress cycle. Based on each equivalent stress amplitude and its corresponding number of cycles, and combined with correction coefficients related to the elbow structural characteristics and operating temperature characteristics, the cumulative fatigue damage factor is calculated. Based on the cumulative fatigue damage factor, the predicted fatigue life of the elbow is calculated using a fatigue life prediction model.

[0053] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0054] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0056] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0057] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

[0059] Many embodiments and applications beyond the examples provided will be apparent to those skilled in the art upon reading the foregoing description. Therefore, the scope of this teaching should not be determined by reference to the foregoing description, but rather by reference to the foregoing claims and the full scope of their equivalents. For purposes of completeness, all articles and references, including patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the foregoing claims is not intended as a waiver of that subject matter, nor should it be construed as an indication that the applicant has not considered that subject matter as part of the disclosed inventive subject matter.

[0060] The above content provides a further detailed description of the present invention. It should not be construed that the specific embodiments of the present invention are limited to this. For those skilled in the art, several simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered to fall within the scope of protection of the present invention as defined by the submitted claims.

Claims

1. A method for predicting the fatigue life of elbows in main steam pipelines of thermal power units, characterized in that, Includes the following steps: Obtain the operational monitoring data of the elbow; Based on the operational monitoring data, multiple stress cycle characteristic parameters for fatigue life prediction are determined, wherein the stress cycle characteristic parameters include at least the equivalent stress amplitude of each stress cycle. Based on each equivalent stress amplitude and its corresponding number of cycles, and combined with correction coefficients related to the elbow structural characteristics and operating temperature characteristics, the cumulative fatigue damage factor is calculated. Based on the cumulative fatigue damage factor, the predicted fatigue life of the elbow is calculated using a fatigue life prediction model.

2. The method for predicting the fatigue life of a main steam pipeline elbow in a thermal power unit according to claim 1, characterized in that, The step of determining multiple stress cycle characteristic parameters for fatigue life prediction specifically includes: Based on the operational monitoring data, multiple effective stress cycles are extracted, and the stress amplitude and average stress of each stress cycle are obtained. The stress amplitude of each stress cycle is corrected to the equivalent stress amplitude based on the corresponding average stress.

3. The method for predicting the fatigue life of a main steam pipeline elbow in a thermal power unit according to claim 2, characterized in that, The step of extracting multiple effective stress cycles and obtaining the stress amplitude and average stress of each stress cycle specifically includes: The collected stress data of the elbow is preprocessed to obtain stress time series data; The stress time series data were analyzed using the rainflow counting method to extract multiple stress cycles; Calculate the stress amplitude and mean stress for each extracted stress cycle.

4. The method for predicting the fatigue life of a main steam pipeline elbow in a thermal power unit according to claim 3, characterized in that, When analyzing the stress time series data using the rainflow counting method, the following is also included: A minimum stress amplitude threshold is set, and stress cycles with stress amplitudes lower than the minimum stress amplitude threshold are identified as invalid cycles and discarded.

5. The method for predicting the fatigue life of a main steam pipeline elbow in a thermal power unit according to claim 1, characterized in that, The correction factor includes the bending stress concentration correction factor. and temperature fluctuation correction factor ; The cumulative fatigue damage factor D is calculated according to the following formula: in, For the first The actual number of cycles corresponding to the equivalent stress amplitude of the first level. This represents the theoretical fatigue life corresponding to the equivalent stress amplitude. It is the total series of effective equivalent stress amplitudes.

6. The method for predicting the fatigue life of a main steam pipeline elbow in a thermal power unit according to claim 1, characterized in that, The fatigue life prediction model is as follows: in, To predict fatigue life, The fatigue life of the material under standard operating conditions. The cumulative fatigue damage factor, The peak bending stress of the elbow is taken as the value. It is a function with the independent variable.

7. The method for predicting the fatigue life of a main steam pipeline elbow in a thermal power unit according to claim 1, characterized in that, It also includes a dynamic update step: acquiring monitoring data of the elbow in a new operating cycle, and based on the monitoring data, repeatedly executing the fatigue life prediction method for the main steam pipeline elbow of the thermal power unit to update the predicted fatigue life of the elbow.

8. A fatigue life prediction system for the main steam pipeline elbow of a thermal power unit, characterized in that, Includes the following modules: The data acquisition module is used to acquire the operation monitoring data of the elbow; The feature parameter determination module is used to determine multiple stress cycle feature parameters for fatigue life prediction based on the operation monitoring data. The stress cycle feature parameters include at least the equivalent stress amplitude of each stress cycle. The cumulative damage calculation module is used to calculate the cumulative fatigue damage factor based on each equivalent stress amplitude and its corresponding number of cycles, combined with correction coefficients related to the elbow structural characteristics and operating temperature characteristics. The life prediction module is used to calculate the predicted fatigue life of the elbow based on the cumulative fatigue damage factor and through a fatigue life prediction model.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for predicting the fatigue life of the main steam pipeline elbow of a thermal power unit as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for predicting the fatigue life of the main steam pipeline elbow of a thermal power unit as described in any one of claims 1 to 7.

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