A fatigue life distribution model design method under random load history

Abstract

The application discloses a kind of fatigue life distribution model design methods under random load history, comprising: according to the conditional probability function of fatigue life under symmetric cycle condition, deduce critical damage distribution model;Life mean under random load history is substituted into the curve under symmetric cycle condition S N Equivalent symmetric stress of random load spectrum is obtained;The specific expression of critical damage distribution is obtained by substituting the equivalent symmetric stress into the critical damage distribution model;The random data sample of damage critical value with sample capacity n Is obtained by Monte Carlo sampling method;Damage accumulation theory and the random data sample of damage critical value are combined, and the random data sample of fatigue life with sample capacity n Is calculated;Based on the random data sample of fatigue life, the fatigue life distribution model under random load history is established.The application is simple and intuitive, with simple steps, cost saving and engineering application value.​

Description

Technical Field

[0001] This invention relates to the field of fatigue life reliability technology for aero-engine compressor disks, and in particular to a method for designing a fatigue life distribution model under random load history. Background Technology

[0002] During engine service, compressor disks typically operate under conditions of high-speed rotation and gas impact, bearing centrifugal force from their own mass, as well as centrifugal force and aerodynamic loads transmitted from the blades. For turbine disks and high-pressure compressor disks, they also experience thermal stress from high-temperature environments, and thinner disk components may experience vibration stress. Furthermore, during disk-to-disk or disk-to-shaft assembly, interference fits generate assembly stress, and under certain operating conditions, deformation inconsistencies create various additional stresses. Due to the presence and combined effects of these multiple stresses, disk-type components often operate under complex and random stress environments. This not only greatly complicates fatigue reliability assessment but also poses significant safety hazards during aircraft flight operations. Fatigue failure of the compressor disk often results in non-containment damage with catastrophic consequences. Its fragments can penetrate the engine casing, potentially damaging oil lines and operating systems, and even penetrating the cockpit and fuel tanks, posing a serious threat to the aircraft, crew, and passengers. Therefore, ensuring high fatigue reliability in the structural and strength design of the compressor disk is crucial for the safety of the aircraft engine, aircraft, and personnel.

[0003] Due to various uncertainties leading to significant dispersion in fatigue life, fatigue reliability assessment is an essential step in ensuring the safe and stable operation of mechanical structures or components during service. Traditional structural safety design relies solely on the "safety factor," while fatigue reliability design incorporates both the "safety factor" and reliability, resulting in more reliable assessments. Currently, in structural fatigue resistance design, there are two types of fatigue reliability indices under different load histories: predicting the fatigue life of a component or specimen at a given survival rate (probabilistic fatigue life calculation); and calculating the survival rate of a component or specimen at a given fatigue life. To date, domestic researchers have summarized fatigue reliability assessment methods into three types: reliability assessment models based on fatigue damage accumulation theory, reliability assessment models based on residual strength degradation, and reliability assessment models based on fatigue life. T. Shimokawa et al., based on Miner's damage theory, treat the critical damage value as a random variable with a mean of 1. Xie Liyang et al. argue that if the shape parameters of the Weibull distribution of fatigue life are the same at different stress levels, then the critical damage value distribution is the same at different stress levels, meaning the critical damage distribution is not sensitive to stress level. Furthermore, Xie Liyang et al. proposed a dimensionless interference model for fatigue reliability under random constant amplitude loading based on the "stress-strength interference model." This model can predict the fatigue failure probability or reliability under random constant amplitude loading, but its evaluation accuracy is highly dependent on the accuracy of the random load probability statistical model. All three fatigue reliability assessment methods mentioned above establish corresponding reliability assessment models around the fatigue reliability problem. The difference lies in the different control parameters used to clarify the survival rate, thus leading to differences in the established cumulative distribution functions.

[0004] In summary, existing theoretical models for predicting the fatigue life of disc-shaped components still have certain limitations and cannot reflect the dispersion of fatigue life. Furthermore, traditional methods often require a large amount of experimental data to establish fatigue life distribution models, resulting in significant time and financial costs. Since compressor discs are critical components of aero-engines, assessing their reliability is of great importance. Summary of the Invention

[0005] Existing reliability assessment methods for fatigue life distribution models require fatigue testing and a large amount of fatigue test data, a process that is typically time-consuming and costly. Furthermore, traditional structural safety design relies solely on the "safety factor," often leading to overly risky or conservative reliability assessments. Therefore, to save time and money and establish a more reasonable reliability assessment method, this invention proposes a fatigue life distribution model design method under random load histories. This method can effectively characterize the dispersion of compressor disk fatigue life under random load histories and perform reliability assessments.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] A method for designing a fatigue life distribution model under random load history includes the following steps:

[0008] S1. Based on the conditional probability function of fatigue life under symmetrical cyclic conditions, derive the critical damage distribution model;

[0009] S2. Substitute the lifetime mean under random load history into the SN curve under symmetrical cyclic conditions to obtain the equivalent symmetrical stress of the random load spectrum.

[0010] S3. Substitute the equivalent symmetrical stress into the critical damage distribution model to obtain the specific expression for the critical damage distribution;

[0011] S4. Based on the specific expression of the critical damage distribution, random data samples of the damage critical value with a sample size of n are obtained by Monte Carlo sampling method.

[0012] S5. Combine the damage accumulation theory with the random data sample of the damage critical value to calculate the random data sample of fatigue life with a sample size of n.

[0013] S6. Based on the random data samples of the fatigue life, establish a fatigue life distribution model under random load history. As a preferred embodiment of the present invention, step S1 specifically includes:

[0014] S11. Conduct fatigue tests to obtain fatigue life samples of standard specimens under symmetrical loading conditions and multiple stress levels.

[0015] S12. Based on the fatigue life sample, select a probabilistic statistical model to establish the conditional probability function of fatigue life. When the fatigue life satisfies the Weibull distribution, the fatigue life distribution model f(n) is obtained, as follows:

[0016]

[0017] In the formula, n is the independent variable of the probability density function; β>0 is the shape parameter; γ≥0 is the position parameter; η>0 is the scale parameter;

[0018] When γ = 0, the three-parameter Weibull distribution degenerates into a two-parameter Weibull distribution;

[0019] Wherein, the shape parameter β represents the material, and the position parameter γ and scale parameter η are functions of the stress level, as shown in the following formula:

[0020] γ(S)=Ae B·S ;

[0021] η(S)=Ce D·S

[0022] In the formula, S is the nominal stress amplitude; A, B, C, and D are all fitting constants;

[0023] Combining the fatigue life distribution model f(n) and the stress level function, the conditional probability function f(n|S) of fatigue life is obtained, as shown in the following formula:

[0024]

[0025] S13. Based on Miner's damage accumulation theory, the critical damage expression is obtained as follows:

[0026]

[0027] In the formula, D cr Critical damage; N f The fatigue life sample represents the fatigue life under any stress level. This represents the median probabilistic lifetime.

[0028] The critical damage distribution model f(d) is derived. cr The formula is as follows:

[0029]

[0030] in:

[0031]

[0032] In the formula, d cr β1, η1, and γ1 are the independent variables of the damage critical value; β1, η1, and γ1 are the shape parameter, scale parameter, and location parameter of the random quantity of the damage critical value, respectively.

[0033] As a preferred embodiment of the present invention, step S2 specifically includes:

[0034] S21. Calculate the average fatigue test life under random load history directly from fatigue test data. Alternatively, the average theoretical fatigue life can be calculated based on Miner's damage accumulation theory. The calculation formula is as follows:

[0035]

[0036] In the formula, m is the total number of load types included in the random load spectrum; n i N represents the load cycle number corresponding to the i-th stress level; i Let be the fatigue life corresponding to the i-th stress level; the theoretical average fatigue life satisfies

[0037] S22, Average fatigue test life Or the average theoretical fatigue life Substituting the SN curve under symmetrical cyclic conditions, we obtain the equivalent symmetrical stress S. eq ;

[0038] The critical damage distribution of the random load spectrum is the same as that of the critical damage distribution under equivalent symmetric stress, as shown in the following equation:

[0039]

[0040] In the formula, d cr The independent variable is the damage threshold; (S1, S2, S3…S) p ) represents the load elements contained in the multi-level load spectrum.

[0041] As a preferred embodiment of the present invention, step S3 specifically includes:

[0042] S31, the equivalent symmetrical stress S eq Substitute the fatigue life distribution model f(n) from step S12 into the value of the shape parameter, position parameter and scale parameter of the fatigue life distribution model f(n);

[0043] S32. Substitute the parameter values ​​into the critical damage distribution model f(d) from step S13. cr The specific expression for the critical damage distribution is calculated as f(d). cr |S eq ).

[0044] As a preferred embodiment of the present invention, in step S4, the specific expression f(d) of the critical damage distribution described in step S32 is used. cr |S eq Using MATLAB data analysis software and the Monte Carlo sampling method, random data samples of damage threshold values ​​with a sample size of n were obtained (D). cr1 D cr2 D cr3 …D cr·j …D cr·n ), where D cr·j This represents a random data sample representing the j-th damage threshold.

[0045] As a preferred embodiment of the present invention, in step S5, a random data sample (D) based on the damage threshold is used. cr1 D cr2 D cr3 …D cr·j …D cr·n Based on Miner's damage accumulation theory, the fatigue life N for a sample size of n is calculated.g A random data sample, denoted as (N) g1 N g2 N g3 …N g·j …N g·n The formula is as follows:

[0046]

[0047] As a preferred embodiment of the present invention, the fatigue life N is calculated. g When dealing with random data samples, the influence of the mean stress effect is considered, and a modified Walker model is used to correct the mean stress, as shown in the following equation:

[0048]

[0049] Among them, S max The peak stress is generated under asymmetric loading; ν is the fitting parameter, and R is the stress ratio, where ν satisfies:

[0050]

[0051] Where A1 and t0 are the influence coefficients of peak stress on mean stress effect; t1 is the fitting constant.

[0052] As a preferred embodiment of the present invention, in step S6, the fatigue life random data samples are used to construct a fatigue life distribution model under random load history through a three-parameter Weibull distribution.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] This invention derives the dispersion characteristics of critical damage based on the fatigue life distribution model of standard specimens under symmetrical loading conditions. Then, by combining this with damage accumulation theory, a fatigue life distribution model under random loading history can be established, and corresponding reliability assessments can be performed. The process is simple and intuitive. Furthermore, it eliminates the need to conduct fatigue tests to obtain fatigue test data for establishing the life distribution model, thus saving significant testing costs.

[0055] The fatigue life distribution model design method under random load history proposed in this invention has simple steps, requires relatively simple and easy-to-obtain various functional relationships, and is convenient and quick in practical engineering applications, thus having certain engineering application value.

[0056] By combining the fatigue life distribution model under symmetrical loading conditions, the critical damage distribution, and the damage accumulation theory, a compressor disk fatigue life distribution model under random load history can be established. This model is simple, intuitive, and involves concise steps. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] in:

[0059] Figure 1 This is a flowchart of the method of the present invention;

[0060] Figure 2 This is a schematic diagram of the measured random stress spectrum of the compressor disc in an embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram illustrating the functional relationship between scale parameters, position parameters, and nominal stress amplitude in an embodiment of the present invention;

[0062] Figure 4 This is a schematic diagram of the three-parameter Weibull distribution conditional probability density function of the fatigue life of the standard specimen in an embodiment of the present invention;

[0063] Figure 5 This is a schematic diagram of the critical damage distribution under random load spectrum in an embodiment of the present invention;

[0064] Figure 6 This is a schematic diagram of the SN curve of a standard sample under symmetrical loading in an embodiment of the present invention;

[0065] Figure 7 This is a schematic diagram of the fitting results of the fatigue life distribution model in an embodiment of the present invention;

[0066] Figure 8 This is a schematic diagram of the experimental fatigue life fitting results in an embodiment of the present invention. Detailed Implementation

[0067] 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0068] like Figures 1-5 As shown, this is an embodiment of the present invention, which provides a method for designing a fatigue life distribution model under random load history, including the following steps:

[0069] S1: Based on the conditional probability function of fatigue life under symmetrical cyclic conditions, the critical damage distribution model is derived;

[0070] S11. Conduct fatigue tests to obtain fatigue life samples of standard specimens under symmetrical loading conditions and multiple stress levels.

[0071] S12. Based on the fatigue life sample, select a probabilistic statistical model to establish the conditional probability function of fatigue life. When the fatigue life satisfies the Weibull distribution, the fatigue life distribution model f(n) is obtained, as follows:

[0072]

[0073] In the formula, n is the independent variable of the probability density function; β>0 is the shape parameter; γ≥0 is the position parameter; η>0 is the scale parameter;

[0074] When γ = 0, the three-parameter Weibull distribution degenerates into a two-parameter Weibull distribution;

[0075] Wherein, the shape parameter β represents the material, and the position parameter γ and scale parameter η are functions of the stress level, as shown in the following formula:

[0076] γ(S)=Ae B·S ;

[0077] η(S)=Ce D·S

[0078] In the formula, S is the nominal stress amplitude; A, B, C, and D are all fitting constants;

[0079] Combining the fatigue life distribution model f(n) and the stress level function, the conditional probability function f(n|S) of fatigue life is obtained, as shown in the following formula:

[0080]

[0081] S13. Based on Miner's damage accumulation theory, the critical damage expression is obtained as follows:

[0082]

[0083] In the formula, D cr Critical damage; N f The fatigue life sample represents the fatigue life under any stress level. This represents the median probabilistic lifetime.

[0084] The critical damage distribution model f(d) is derived. cr The formula is as follows:

[0085]

[0086] in:

[0087]

[0088] In the formula, d cr β1, η1, and γ1 are the shape, scale, and location parameters of the damage critical value random variable, respectively.

[0089] S2: Substitute the lifetime mean under random load history into the SN curve under symmetrical cyclic conditions to obtain the equivalent symmetrical stress of the random load spectrum.

[0090] S21. Calculate the average fatigue test life under random load history directly from fatigue test data. Alternatively, based on Miner's damage accumulation theory, the theoretical average fatigue life N can be calculated. t The calculation formula is as follows:

[0091]

[0092] In the formula, m is the total number of load types included in the random load spectrum; n i N represents the load cycle number corresponding to the i-th stress level; i Let be the fatigue life corresponding to the i-th stress level; the theoretical average fatigue life satisfies

[0093] S22, Average fatigue test life Or the average theoretical fatigue life Substituting the SN curve under symmetrical cyclic conditions, we obtain the equivalent symmetrical stress S. eq ;

[0094] The critical damage distribution of the random load spectrum is the same as that of the critical damage distribution under equivalent symmetric stress, as shown in the following equation:

[0095] f[d cr |(S1,S2,S3…S p )]=f(d cr |S eq )

[0096] In the formula, d cr For the damage critical value variable; (S1,S2,S3…S p ) represents the load elements contained in the multi-level load spectrum.

[0097] S3: Substitute the equivalent symmetrical stress into the critical damage distribution model to obtain the specific expression for the critical damage distribution; S31: Substitute the equivalent symmetrical stress S eqSubstitute the fatigue life distribution model f(n) from step S12 into the value of the shape parameter, position parameter and scale parameter of the fatigue life distribution model f(n);

[0098] S32. Substitute the parameter values ​​into the critical damage distribution model f(d) from step S13. cr The specific expression for the critical damage distribution is calculated as f(d). cr |S eq ).

[0099] S4: Based on the specific expression of the critical damage distribution, random data samples of the damage critical value with a sample size of n are obtained by Monte Carlo sampling method;

[0100] Based on the specific expression f(d) of the critical damage distribution described in step S32 cr |S eq Using MATLAB data analysis software and the Monte Carlo sampling method, random data samples of damage threshold values ​​with a sample size of n were obtained (D). cr1 D cr2 D cr3 …D cr·j …D cr·n ), where D cr·j This represents a random data sample representing the j-th damage threshold.

[0101] S5: Combine the damage accumulation theory with the random data sample of the damage critical value to calculate a random data sample of fatigue life with a sample size of n.

[0102] Random data samples of damage threshold (D) cr1 D cr2 D cr3 …D cr·j …D cr·n Based on Miner's damage accumulation theory, the fatigue life N for a sample size of n is calculated. g A random data sample, denoted as (N) g1 N g2 N g3 …N g·j …N g·n The formula is as follows:

[0103]

[0104] It should be noted that the fatigue life N at a certain stress level in the calculation of the random load spectrum is... iWhen calculating the mean stress, it is necessary to consider the effect of mean stress and use a corresponding mean stress correction model to correct the mean stress. In this example, the modified Walker model is used to correct the mean stress, and its expression is as follows:

[0105]

[0106] Among them, S max The peak stress is generated under asymmetric loading; ν is the fitting parameter, where ν satisfies:

[0107]

[0108] Where A1 and t0 are the influence coefficients of peak stress on mean stress effect; t1 is the fitting constant.

[0109] S6: Based on the random data samples of the fatigue life, establish a fatigue life distribution model under random load history;

[0110] The fatigue life random data samples were used to construct a fatigue life distribution model under random load history using a three-parameter Weibull distribution, such as... Figure 7 As shown.

[0111] To establish a fatigue life distribution model for a compressor disk under a certain random load spectrum, the above steps of this invention can be used to: first, derive the critical damage distribution model based on the conditional probability density function of fatigue life under symmetrical cyclic conditions; then, obtain the equivalent symmetrical stress of the random load spectrum; substitute the equivalent symmetrical stress into the critical damage distribution model to obtain a specific expression for the critical damage distribution model; then, use Monte Carlo sampling to obtain random data samples of the critical damage value; finally, combine the random data samples of the critical damage value from the damage accumulation theory to calculate random data samples of fatigue life; and finally, use the Weibull distribution to establish a fatigue life distribution model under random load history.

[0112] A fatigue life distribution model was established based on standard specimen test data under random loads, and compared with the method for establishing the compressor disk fatigue life distribution model under random load spectrum presented in this paper to evaluate the effectiveness of the invention. Figure 7 and Figure 8 The comparison leads to the conclusion that the life distribution model established based on the experimental results has a small error compared with the modeling results in this paper, and meets the evaluation requirements for the method of establishing a fatigue life distribution model of compressor disk under random load spectrum.

[0113] It should be noted that the accuracy of the fatigue life distribution model under random load history proposed in this paper is also affected by factors such as the accuracy of the damage accumulation model, the accuracy of the mean stress correction model, and the accuracy of the fatigue life probability density function under symmetrical loading conditions. When applying this method to establish a fatigue life distribution model under random load in practice, a more accurate damage accumulation model, a more accurate mean stress correction model, and a more accurate fatigue life probability density function under symmetrical loading conditions should be selected according to the actual situation.

[0114] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0115] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0116] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for designing a fatigue life distribution model under random load history, characterized in that, Includes the following steps: S1. Based on the conditional probability function of fatigue life under symmetrical cyclic conditions, derive the critical damage distribution model; Step S1 specifically includes: S11. Conduct fatigue tests to obtain fatigue life samples of standard specimens under symmetrical loading conditions and multiple stress levels. S12. Based on the fatigue life sample, select a probabilistic statistical model to establish the conditional probability function of fatigue life. When the fatigue life satisfies the Weibull distribution, the fatigue life distribution model f(n) is obtained, as follows: ； In the formula, n is the independent variable of the probability density function; β>0 is the shape parameter; γ≥0 is the position parameter; η>0 is the scale parameter; When γ=0, the three-parameter Weibull distribution degenerates into a two-parameter Weibull distribution; Among them, shape parameters For material, position parameters and scale parameters The stress level is a function of the stress level, and the formula is as follows: ； ； In the formula, S is the nominal stress amplitude; A, B, C, and D are all fitting constants; Combining the fatigue life distribution model f(n) and the stress level function, the conditional probability function f(n|S) of fatigue life is obtained, as shown in the following formula: ； S13. Based on Miner's damage accumulation theory, the critical damage expression is obtained as follows: ； In the formula, This is critical damage; The fatigue life sample represents the fatigue life under any stress level. This represents the median probabilistic lifetime. The critical damage distribution model f(d) is derived. cr The formula is as follows: ； in: ； In the formula, The critical value of damage is the independent variable; , , These are the shape parameter, scale parameter, and location parameter of the random variable representing the damage critical value; S2. Substitute the lifetime mean under random load history into the SN curve under symmetrical cyclic conditions to obtain the equivalent symmetrical stress of the random load spectrum. S3. Substitute the equivalent symmetrical stress into the critical damage distribution model to obtain the specific expression for the critical damage distribution; S4. Based on the specific expression of the critical damage distribution, random data samples of the damage critical value with a sample size of n are obtained by Monte Carlo sampling method. S5. Combine the damage accumulation theory with the random data sample of the damage critical value to calculate the random data sample of fatigue life with a sample size of n. S6. Based on the random data samples of the fatigue life, establish a fatigue life distribution model under random load history.

2. The fatigue life distribution model design method under random load history according to claim 1, characterized in that, Step S2 specifically includes: S21. Calculate the average fatigue test life under random load history directly from fatigue test data. Alternatively, the average theoretical fatigue life can be calculated based on Miner's damage accumulation theory. The calculation formula is as follows: ； In the formula, m is the total number of load types contained in the random load spectrum; For the first The number of load cycles corresponding to each stress level; For the first Fatigue life corresponding to each stress level; the theoretical average fatigue life satisfies ; S22, Average fatigue test life Or the average theoretical fatigue life Substituting the SN curve under symmetrical cyclic conditions, we obtain the equivalent symmetrical stress. ; The critical damage distribution of the random load spectrum is the same as that of the critical damage distribution under equivalent symmetric stress, as shown in the following equation: ； In the formula, The critical value of damage is the independent variable; The load elements included in the multi-level load spectrum.

3. The fatigue life distribution model design method under random load history according to claim 2, characterized in that, Step S3 specifically includes: S31, the equivalent symmetrical stress Substitute the fatigue life distribution model f(n) from step S12 into the value of the shape parameter, position parameter and scale parameter of the fatigue life distribution model f(n); S32. Substitute the parameter values ​​into the critical damage distribution model f(d) from step S13. cr The specific expression for the critical damage distribution is calculated as follows: .

4. The fatigue life distribution model design method under random load history according to claim 3, characterized in that, In step S4, the specific expression for the critical damage distribution described in step S32 is used. Using MATLAB data analysis software and the Monte Carlo sampling method, random data samples of damage threshold values ​​with a sample size of n were obtained. ,in This represents a random data sample representing the j-th damage threshold.

5. The fatigue life distribution model design method under random load history according to claim 4, characterized in that, In step S5, random data samples based on the damage threshold. Based on Miner's damage accumulation theory, the fatigue life with a sample size of n was calculated. A random data sample, denoted as The formula is as follows: 。 6. The fatigue life distribution model design method under random load history according to claim 5, characterized in that, When calculating the random data sample of the fatigue life Ng, the influence of the mean stress effect is considered, and a modified Walker model is used to correct the mean stress, as shown in the following formula: ； in, The peak stress is generated under asymmetric loading; ν is the fitting parameter, and R is the stress ratio, where ν satisfies: ； Where A1 and t0 are the influence coefficients of peak stress on mean stress effect; t1 is the fitting constant.

7. The fatigue life distribution model design method under random load history according to claim 6, characterized in that, In step S6, the random data samples of fatigue life are used to construct a fatigue life distribution model under random load history through a three-parameter Weibull distribution.

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