Fatigue reliability assessment method and device for defect-containing composite material structure

Abstract

The invention discloses a fatigue reliability assessment method for a defect-containing composite material structure, and the method comprises the steps: building a residual strength model according to random variables which comprise working stress, fatigue limit, residual static strength, defect detection probability and fatigue cycle times; acquiring information; carrying out analytic statistics on the information to respectively obtain probability density functions of the working stress, the fatigue limit, the residual static strength and the defect detection probability; according to the probability density function of the working stress, the fatigue limit, the residual static strength and the defect detection probability, sampling each random variable based on a Monte-Carlo algorithm to obtain a sampling result; and calculating the fatigue reliability based on a stretching and compression failure calculation formula. The method has the advantages of being simple, convenient, practical and less in required test data, the fatigue reliability of the defective composite material structure can be evaluated by only needing a small amount of fatigue data and random load data of the defective composite material structure, and other additional test data is not needed.

Description

Technical Field

[0001] The present invention relates to the technical field of composite material structure reliability assessment, and in particular to a fatigue reliability assessment method and device for a defective composite material structure. Background Art

[0002] Composite materials are widely used in aircraft structures because of their high specific strength, high specific stiffness and lightweight properties. Composite structures inevitably have manufacturing defects and foreign body impact damage during the manufacturing, operation and maintenance stages. Compared with intact materials, the fatigue life of defective composite structures is significantly reduced. Therefore, accurate assessment of the fatigue reliability of defective composite structures is particularly important for aircraft structural safety. At present, there is still a lack of a fast and accurate fatigue reliability assessment method for defective composite structures. Therefore, a fatigue reliability assessment method for defective composite structures is developed to solve the above problems. Summary of the invention

[0003] The present invention proposes a fatigue reliability assessment method for a defective composite material structure, so as to solve the problem that the prior art lacks a fast and accurate fatigue reliability assessment method for a defective composite material structure.

[0004] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0005] The present invention provides a fatigue reliability assessment method for a defective composite material structure, comprising:

[0006] Constructing a residual strength model based on random variables, wherein the random variables include working stress, fatigue limit, residual static strength, defect detection probability, and fatigue cycle number;

[0007] Acquiring information, the information including data on working stress, fatigue limit after being subjected to fatigue load, residual static strength, and defect detection probability of the composite material structure containing initial defects;

[0008] Analyzing and statistically analyzing the information, respectively obtaining the probability density functions of the working stress, fatigue limit, residual static strength, and defect detection probability;

[0009] Entering an iterative loop: sampling each of the random variables based on the Monte-Carlo algorithm according to the probability density function of the working stress, fatigue limit, residual static strength, and defect detection probability, obtaining a sampling result, judging whether the defect is detected, and if so, repairing the defective composite material structure according to the residual static strength; if not, calculating the residual strength of the defective composite material structure based on the residual strength model according to the sampling result, judging whether the residual strength is less than the working stress, obtaining an output result of the indicator function according to the judgment result, repeating the iterative loop until the number of iterative calculations reaches a preset threshold, and terminating the loop;

[0010] According to the output results of all the indicator functions under all iterative calculations, the fatigue reliability is calculated based on the tensile and compressive failure calculation formulas.

[0011] Specifically, a residual strength model is constructed according to random variables, and the calculation formula of the residual strength model is as follows:

[0012]

[0013] for The remaining strength after the cycle is is the working stress, is the fatigue limit, is the residual static strength, is the number of fatigue cycles, POD is the probability of defect detection, , , All are material fatigue performance parameters.

[0014] Specifically, the information is analyzed and counted to obtain the probability density functions of the working stress, fatigue limit, residual static strength, and defect detection probability, respectively, where:

[0015] The probability density function of the working stress is:

[0016]

[0017] The probability density function of the fatigue limit is:

[0018]

[0019] The probability density function of the residual static strength is:

[0020]

[0021] The probability density function of the defect detection probability is:

[0022]

[0023] In the formula, is the parameter of the exponential distribution of working stress, , Residual static strength The mean and standard deviation of , Fatigue limit The mean and standard deviation of , are the mean and standard deviation of the defect detection probability, respectively.

[0024] Specifically, sampling the working stress according to the probability density function of the working stress to obtain a sampling result includes:

[0025]

[0026] In the formula, For uniform distribution Random numbers.

[0027] Specifically, according to the probability density functions of the fatigue limit and the residual static strength, the fatigue limit and the residual static strength are sampled to obtain sampling results, including:

[0028] Generate normally distributed random numbers based on the Box-Muller transformation method, namely:

[0029]

[0030] In the formula, , Independent uniform distribution Random numbers, according to the probability density function of the fatigue limit and the residual static strength, generate normal distribution random numbers based on the Box-Muller transformation method to randomly sample the residual static strength and the fatigue limit, that is:

[0031]

[0032] In the formula, and They are the input values ​​of tensile and compressive residual static strength in Monte-Carlo simulation respectively; and Enter values ​​for tensile and compressive fatigue limits; , is the mean and standard deviation of the tensile residual static strength; , is the mean and standard deviation of the compressive residual static strength; , is the mean and standard deviation of the tensile fatigue limit; , are the mean and standard deviation of the compressive fatigue limit.

[0033] Specifically, according to the probability density function of the defect detection probability, the defect detection probability is sampled to obtain a sampling result, including:

[0034] The defect detection probability follows a log-normal distribution , first transform the lognormal distribution into a normal distribution, let ,but It obeys the normal distribution, that is:

[0035]

[0036] In the formula, , are the mean and standard deviation of the normal distribution after the lognormal distribution changes. Perform random sampling, that is:

[0037] .

[0038] Specifically, judging whether the residual strength is less than the working stress, and obtaining the output result of the indicator function according to the judging result, including:

[0039] The single simulation indicator function of the Monte-Carlo algorithm is as follows:

[0040]

[0041] when When , the indicator function outputs 0. , the indicator function output is 1.

[0042] Specifically, according to the output results of all the indicator functions under all iterative calculations, the fatigue reliability is calculated based on the tensile and compressive failure calculation formulas, including:

[0043] statistics The number of times, i represents the number of iterations;

[0044] use Calculate the tensile failure probability separately , Compression failure probability ;

[0045] use Calculate the fatigue reliability of defective composite structures.

[0046] Specifically, obtain information, including:

[0047] The working stress of the composite material structure with initial defects is obtained by measuring the load spectrum;

[0048] The fatigue limit, residual static strength and defect detection probability of defective composite structures after being subjected to fatigue loads are obtained through experiments.

[0049] The present invention also provides a fatigue reliability assessment device for a defective composite material structure, comprising:

[0050] A construction module, wherein the construction module is used to construct a residual strength model according to random variables, wherein the random variables include working stress, fatigue limit, residual static strength, defect detection probability, and fatigue cycle number;

[0051] An acquisition module, the acquisition module is used to acquire information, the information including data of working stress of the composite material structure containing initial defects, fatigue limit after being subjected to fatigue load, residual static strength and defect detection probability;

[0052] An analysis module, the analysis module is used to analyze and count the information to obtain the probability density functions of the working stress, fatigue limit, residual static strength, and defect detection probability;

[0053] A loop module, the loop module is used to enter an iterative loop: according to the probability density function of the working stress, fatigue limit, residual static strength, and defect detection probability, each of the random variables is sampled based on the Monte-Carlo algorithm to obtain a sampling result, and it is determined whether the defect is detected. If so, the defective composite material structure is repaired according to the residual static strength. If not, the residual strength of the defective composite material structure is calculated based on the residual strength model according to the sampling result, and it is determined whether the residual strength is less than the working stress. According to the judgment result, an output result of the indicator function is obtained, and the iterative loop is repeated until the number of iterative calculations reaches a preset threshold, and the loop is terminated;

[0054] A calculation module is used to calculate fatigue reliability based on the tensile and compressive failure calculation formulas according to the output results of all the indicator functions under all iterative calculations.

[0055] The beneficial effects of the present invention are:

[0056] The fatigue reliability evaluation method of a defective composite material structure proposed in the present invention has the advantages of being simple, practical, and requiring little test data. Only a small amount of fatigue data and random load data of the defective composite material structure are required to evaluate the fatigue reliability of the defective composite material structure, without the need for other additional test data. The present invention has important engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1A method flow chart of a fatigue reliability assessment method for a composite material structure containing defects in an embodiment of the present application;

[0058] Figure 2 This is a flow chart of a method for extracting random variables and calculating reliability using Monte-Carlo in an embodiment of the present application;

[0059] Figure 3 This is a flow chart of fatigue reliability assessment of defective composite material structure in an embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0061] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0063] In the description of the present invention, it should be understood that the terms "upper", "lower", "inside", "outside", "left", "right", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, or are directions or positional relationships in which the product of the invention is usually placed when in use, or are directions or positional relationships commonly understood by those skilled in the art. These directions or positional relationships 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 direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation on the present invention.

[0064] Furthermore, the terms “first”, “second”, etc. are merely used for distinguishing descriptions and should not be understood as indicating or implying relative importance.

[0065] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms such as "setting" and "connection" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0066] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0067] like Figure 1 As shown, a fatigue reliability assessment method for a defective composite material structure comprises the following steps:

[0068] Step 1: construct a residual strength model based on random variables, wherein the random variables include working stress, fatigue limit, residual static strength, defect detection probability, and fatigue cycle number;

[0069] Step 2: obtaining information, including data on the working stress of the composite material structure containing initial defects, the fatigue limit after being subjected to fatigue load, the residual static strength, and the probability of defect detection;

[0070] Step 3: Analyze and count the information to obtain the probability density functions of the working stress, fatigue limit, residual static strength, and defect detection probability;

[0071] Step 4: Entering an iterative loop: sampling each of the random variables based on the Monte-Carlo algorithm according to the probability density function of the working stress, fatigue limit, residual static strength, and defect detection probability, obtaining a sampling result, judging whether the defect is detected, and if so, repairing the defective composite material structure according to the residual static strength; if not, calculating the residual strength of the defective composite material structure based on the residual strength model according to the sampling result, judging whether the residual strength is less than the working stress, obtaining an output result of the indicator function according to the judgment result, repeating the iterative loop until the number of iterative calculations reaches a preset threshold, and terminating the loop;

[0072] Step 5: According to the output results of all the indicator functions under all iterative calculations, the fatigue reliability is calculated based on the tensile and compressive failure calculation formulas.

[0073] Combination Figure 2 and Figure 3 As shown in the figure, the Monte-Carlo algorithm is used to evaluate the fatigue reliability of composite material structures. The specific process is as follows:

[0074] Step 1: Establish fatigue reliability analysis model

[0075] A residual strength model is constructed according to random variables, wherein the random variables include working stress, fatigue limit, residual static strength, defect detection probability, and fatigue cycle number.

[0076] The fatigue loads that composite structures are subjected to during their service are all random. When the load is greater than the residual strength of the composite structure, the composite structure can no longer continue to work normally, and the composite structure is considered to have failed. Considering the randomness of fatigue loads and the dispersion of material properties, a stress-strength interference model is established.

[0077] (1)

[0078] In the formula, is fatigue reliability; for Remaining strength after cycles; is the working stress. Under the service fatigue load, As the number of cycles increases, it will deteriorate, resulting in performance degradation. The residual strength and working stress of composite structures after random loads , fatigue limit , residual static strength , Fatigue cycle number , the probability of defect detection POD, that is

[0079] (2)

[0080] Considering the working stress, fatigue limit, residual static strength, defect detection probability, number of cycles and other random variables of the composite structure during service, the residual strength model shown below is established:

[0081] (3)

[0082] In the formula, , , All are material fatigue performance parameters.

[0083] Step 2: Obtain information, including data on the working stress of the composite material structure containing initial defects, the fatigue limit after being subjected to fatigue load, the residual static strength, and the probability of defect detection.

[0084] The working stress of the composite material structure with initial defects is obtained by measuring the load spectrum, and the fatigue limit, residual static strength and defect detection probability of the composite material structure after being subjected to fatigue load are obtained through experiments.

[0085] Step three, analyzing and statistically analyzing the information to obtain the probability density functions of the working stress, fatigue limit, residual static strength, and defect detection probability.

[0086] By analyzing and counting the data obtained in step 2, it is found that the working stress obeys an exponential distribution, and its probability density function is:

[0087] (4)

[0088] In the formula, is the parameter of the working stress obeying exponential distribution. The fatigue limit and residual static strength obey normal distribution, and their probability density functions are:

[0089] (5)

[0090] (6)

[0091] In the formula, , Residual static strength The mean and standard deviation of , Fatigue limit The probability of defect detection follows a log-normal distribution, and its probability density function is:

[0092] (7)

[0093] In the formula, , are the mean and standard deviation of the defect detection probability, respectively.

[0094] Step 4, entering an iterative loop: based on the probability density function of the working stress, fatigue limit, residual static strength, and defect detection probability, sampling is performed on each of the random variables based on the Monte-Carlo algorithm to obtain a sampling result, and it is determined whether the defect is detected. If so, the defective composite material structure is repaired according to the residual static strength. If not, the residual strength of the defective composite material structure is calculated based on the residual strength model according to the sampling result, and it is determined whether the residual strength is less than the working stress. The output result of the indicator function is obtained according to the judgment result, and the iterative loop is repeated until the number of iterative calculations reaches a preset threshold, and the loop is terminated, which specifically includes:

[0095] After determining the probability density functions of working stress, fatigue limit, residual static strength and defect detection probability, Monte-Carlo is used to randomly sample the parameters, and different distribution functions are sampled according to different methods.

[0096] The working stress obeys the exponential distribution. According to the sampling method of the exponential distribution, the input value of the working stress is obtained, that is:

[0097] (8)

[0098] In the formula, For uniform distribution Random numbers.

[0099] The fatigue limit and residual static strength obey the normal distribution. According to the normal distribution sampling method, they are sampled. First, the Box-Muller transformation method is used to generate normal distribution random numbers, that is,

[0100] (9)

[0101] In the formula, , Independent uniform distribution Random numbers, based on the Box-Muller transformation method, generate normal distribution random numbers for random sampling of residual static strength and fatigue limit, that is:

[0102] (10)

[0103] In the formula, and They are the input values ​​of tensile and compressive residual static strength in Monte-Carlo simulation respectively; and Enter values ​​for tensile and compressive fatigue limits; , is the mean and standard deviation of the tensile residual static strength; , is the mean and standard deviation of the compressive residual static strength; , is the mean and standard deviation of the tensile fatigue limit; , are the mean and standard deviation of the compressive fatigue limit.

[0104] The probability of defect detection follows a log-normal distribution , first transform the lognormal distribution into a normal distribution, let ,but It obeys normal distribution, that is:

[0105] (11)

[0106] In the formula, , are the mean and standard deviation of the normal distribution after the lognormal distribution is changed. Perform random sampling, that is:

[0107] (12)

[0108] In order to obtain accurate simulation results, repeated sampling is used. The Monte-Carlo single simulation indicator function is as follows:

[0109] (13)

[0110] when When , the indicator function outputs 0; when When , the indicator function output is 1. Substitute all random sampling results into formula (3) to calculate the residual strength of the composite material structure.

[0111] Sample different random variables according to equations (8)-(12).

[0112] Random sampling is carried out on working stress, fatigue limit, residual static strength and defect detection probability according to different sampling methods.

[0113] If the defect is detected, it is repaired according to the residual static strength, and the composite material structure is considered to have not failed; if the pit is not detected, it is repaired according to the residual static strength. Calculate the residual strength of composite structures.

[0114] If the residual strength is less than the working stress, the indicator function output is 1; otherwise, it outputs 0.

[0115] Repeat the cycle until the number of simulation iterations reaches The cycle ends when the

[0116] Step 5: Evaluate the fatigue reliability of defective composite structures

[0117] According to the output results of all the indicator functions under all iterative calculations, the fatigue reliability is calculated based on the tensile and compressive failure calculation formulas.

[0118] statistics The number of times, using Calculate the tensile and compressive failure probabilities separately.

[0119] use Calculate the fatigue reliability of defective composite structures. is the tensile failure probability; is the compression failure probability.

[0120] This embodiment also discloses a fatigue reliability assessment device for a defective composite material structure, comprising:

[0121] A construction module, wherein the construction module is used to construct a residual strength model according to random variables, wherein the random variables include working stress, fatigue limit, residual static strength, defect detection probability, and fatigue cycle number;

[0122] An acquisition module, the acquisition module is used to acquire information, the information including data of working stress of the composite material structure containing initial defects, fatigue limit after being subjected to fatigue load, residual static strength and defect detection probability;

[0123] An analysis module, the analysis module is used to analyze and count the information to obtain the probability density functions of the working stress, fatigue limit, residual static strength, and defect detection probability;

[0124] A loop module, the loop module is used to enter an iterative loop: according to the probability density function of the working stress, fatigue limit, residual static strength, and defect detection probability, each of the random variables is sampled based on the Monte-Carlo algorithm to obtain a sampling result, and it is determined whether the defect is detected. If so, the defective composite material structure is repaired according to the residual static strength. If not, the residual strength of the defective composite material structure is calculated based on the residual strength model according to the sampling result, and it is determined whether the residual strength is less than the working stress. According to the judgment result, an output result of the indicator function is obtained, and the iterative loop is repeated until the number of iterative calculations reaches a preset threshold, and the loop is terminated;

[0125] A calculation module is used to calculate fatigue reliability based on the tensile and compressive failure calculation formulas according to the output results of all the indicator functions under all iterative calculations.

[0126] The fatigue reliability evaluation method of a composite material structure with defects proposed in the present invention is simple, practical, and requires little test data. Only a small amount of fatigue data and random load data of the composite material structure with defects are needed to evaluate the fatigue reliability of the composite material structure with defects, and no other additional test data is required. In addition, considering the problem of fatigue with defects, since composite materials are prone to various defects in engineering applications, but the defects cannot be completely detected, composite materials structures with defects may still be in service during the service stage of the composite materials. This is consistent with the actual application of composite materials. This method can accurately evaluate the fatigue reliability of composite materials structures with initial defects.

[0127] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A fatigue reliability assessment method for a defective composite material structure, characterized in that: include: Constructing a residual strength model based on random variables, wherein the random variables include working stress, fatigue limit, residual static strength, defect detection probability, and fatigue cycle number; Acquiring information, the information including data on working stress, fatigue limit after being subjected to fatigue load, residual static strength, and defect detection probability of the composite material structure containing initial defects; Analyzing and statistically analyzing the information, respectively obtaining the probability density functions of the working stress, fatigue limit, residual static strength, and defect detection probability; Entering an iterative loop: sampling each of the random variables based on the Monte-Carlo algorithm according to the probability density function of the working stress, fatigue limit, residual static strength, and defect detection probability, obtaining a sampling result, judging whether the defect is detected, and if so, repairing the defective composite material structure according to the residual static strength; if not, calculating the residual strength of the defective composite material structure based on the residual strength model according to the sampling result, judging whether the residual strength is less than the working stress, obtaining an output result of the indicator function according to the judgment result, repeating the iterative loop until the number of iterative calculations reaches a preset threshold, and terminating the loop; According to the output results of all the indicator functions under all iterative calculations, the fatigue reliability is calculated based on the tensile and compressive failure calculation formulas.

2. The fatigue reliability assessment method of a defective composite material structure according to claim 1, characterized in that: A residual strength model is constructed according to random variables, and the calculation formula of the residual strength model is as follows: ， for The remaining strength after the cycle is is the working stress, is the fatigue limit, is the residual static strength, is the number of fatigue cycles, POD is the probability of defect detection, , , All are material fatigue performance parameters.

3. The fatigue reliability assessment method of a defective composite material structure according to claim 2, characterized in that: The information is analyzed and counted to obtain the probability density functions of the working stress, fatigue limit, residual static strength, and defect detection probability, respectively, where: The probability density function of the working stress is: ， The probability density function of the fatigue limit is: ， The probability density function of the residual static strength is: ， The probability density function of the defect detection probability is: ， In the formula, is the parameter of the exponential distribution of working stress, , Residual static strength The mean and standard deviation of , Fatigue limit The mean and standard deviation of , are the mean and standard deviation of the defect detection probability, respectively.

4. The fatigue reliability assessment method of a defective composite material structure according to claim 3, characterized in that: The working stress is sampled according to the probability density function of the working stress to obtain a sampling result, including: ， In the formula, For uniform distribution Random numbers.

5. The fatigue reliability assessment method of a defective composite material structure according to claim 3, characterized in that: According to the probability density functions of the fatigue limit and the residual static strength, the fatigue limit and the residual static strength are sampled to obtain sampling results, including: Generate normally distributed random numbers based on the Box-Muller transformation method, namely: ， In the formula, , Independent uniform distribution Random numbers, according to the probability density function of the fatigue limit and the residual static strength, generate normal distribution random numbers based on the Box-Muller transformation method to randomly sample the residual static strength and the fatigue limit, that is: ， In the formula, and They are the input values ​​of tensile and compressive residual static strength in Monte-Carlo simulation respectively; and Enter values ​​for tensile and compressive fatigue limits; , is the mean and standard deviation of the tensile residual static strength; , is the mean and standard deviation of the compressive residual static strength; , is the mean and standard deviation of the tensile fatigue limit; , are the mean and standard deviation of the compressive fatigue limit.

6. The fatigue reliability assessment method of a defective composite material structure according to claim 3, characterized in that: According to the probability density function of the defect detection probability, the defect detection probability is sampled to obtain a sampling result, including: The defect detection probability follows a log-normal distribution , first transform the lognormal distribution into a normal distribution, let ,but It obeys the normal distribution, that is: ， In the formula, , are the mean and standard deviation of the normal distribution after the lognormal distribution changes. Perform random sampling, that is: 。 7. The fatigue reliability assessment method of a defective composite material structure according to claim 1, characterized in that: Determine whether the residual strength is less than the working stress, and obtain the output result of the indicator function according to the determination result, including: The single simulation indicator function of the Monte-Carlo algorithm is as follows: ， when When , the indicator function outputs 0. , the indicator function output is 1.

8. The fatigue reliability assessment method of a defective composite material structure according to claim 7, characterized in that: According to the output results of all the indicator functions under all iterative calculations, the fatigue reliability is calculated based on the tensile and compressive failure calculation formulas, including: statistics The number of times, i represents the number of iterations; use Calculate the tensile failure probability separately , Compression failure probability ; use Calculate the fatigue reliability of defective composite structures.

9. The fatigue reliability assessment method of a defective composite material structure according to claim 1, characterized in that: Get information, including: The working stress of the composite material structure with initial defects is obtained by measuring the load spectrum; The fatigue limit, residual static strength and defect detection probability of defective composite structures after being subjected to fatigue loads are obtained through experiments.

10. A fatigue reliability assessment device for a defective composite material structure, characterized in that: include: A construction module, wherein the construction module is used to construct a residual strength model according to random variables, wherein the random variables include working stress, fatigue limit, residual static strength, defect detection probability, and fatigue cycle number; An acquisition module, the acquisition module is used to acquire information, the information including data of working stress of the composite material structure containing initial defects, fatigue limit after being subjected to fatigue load, residual static strength and defect detection probability; An analysis module, the analysis module is used to analyze and count the information to obtain the probability density functions of the working stress, fatigue limit, residual static strength, and defect detection probability; A loop module, the loop module is used to enter an iterative loop: according to the probability density function of the working stress, fatigue limit, residual static strength, and defect detection probability, each of the random variables is sampled based on the Monte-Carlo algorithm to obtain a sampling result, and it is determined whether the defect is detected. If so, the defective composite material structure is repaired according to the residual static strength. If not, the residual strength of the defective composite material structure is calculated based on the residual strength model according to the sampling result, and it is determined whether the residual strength is less than the working stress. According to the judgment result, an output result of the indicator function is obtained, and the iterative loop is repeated until the number of iterative calculations reaches a preset threshold, and the loop is terminated; A calculation module is used to calculate fatigue reliability based on the tensile and compressive failure calculation formulas according to the output results of all the indicator functions under all iterative calculations.

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