Method, device and storage medium for determining low cycle fatigue of mechanical components

Abstract

A method, device, and storage medium for determining low-cycle fatigue of a mechanical component. The method includes obtaining a plurality of cyclic operating conditions of the mechanical component over a plurality of operating cycles; calculating a Weibull proportional parameter for each of the plurality of operating cycles based on a corresponding one of the plurality of cyclic operating conditions; calculating a hazard rate of the mechanical component based on the Weibull proportional parameter for each of the plurality of operating cycles; and determining the low-cycle fatigue of the mechanical component based on each hazard rate in the plurality of operating cycles; wherein the Weibull proportional parameter is used to describe the geometric shape and stress-strain state of the mechanical component. This method and device solve the problem of inaccurate low-cycle fatigue determination due to fixed cyclic operating conditions in related technologies, thereby improving the accuracy of low-cycle fatigue risk assessment.

Description

Technical Field

[0001] The present disclosure relates to the field of computers, and in particular to a method, device, and storage medium for determining low-cycle fatigue of a mechanical component. Background Art

[0002] Gas turbine components have complex geometric shapes and structural forms, and operate under harsh conditions of high temperature and high speed, making them prone to various failures. Among them, low-cycle fatigue (LCF) failure is the most important factor affecting and limiting the safe use of gas turbine components.

[0003] Low-cycle fatigue (LCF) assessment is a method for analyzing the mechanical integrity of gas turbine components. Traditionally, LCF assessment of gas turbine components has been based on deterministic crack initiation or LCF calculation models with standardized operating cycles. In reality, crack initiation is a stochastic phenomenon, and actual operating conditions vary from cycle to cycle.

[0004] Probabilistic LCF models and tools have been developed to account for the stochastic nature of crack initiation. This approach accounts for material property uncertainty (dispersion) in the model, as well as for nonuniform strain forces on the component and the size effect of crack initiation. However, this approach does not account for the variability or uncertainty of operating conditions.

[0005] In other words, although deterministic and probabilistic LCF evaluation methods have been applied to mechanical component integrity analysis in related art, in both methods, the operating cycles and corresponding boundary conditions of the mechanical components are given by standard operating cycles and several specific operating cycles defined by the mechanical component designer. Therefore, neither method considers variable and random operating conditions.

[0006] How to extend the traditional probabilistic LCF model with fixed (standard) periodic operating conditions to the situation where the periodic operating conditions vary from period to period is an urgent problem to be solved. Summary of the Invention

[0007] According to one aspect of an embodiment of the present disclosure, a method for determining low-cycle fatigue of a mechanical component is provided, the method comprising: obtaining a plurality of cyclic operating conditions of the mechanical component in a plurality of operating cycles; for each of the plurality of operating cycles, calculating a Weibull proportional parameter based on a corresponding one-cycle operating condition in the plurality of cyclic operating conditions; for each of the plurality of operating cycles, calculating a hazard rate of the mechanical component based on the Weibull proportional parameter; and determining the low-cycle fatigue of the mechanical component based on each of the hazard rates in the plurality of operating cycles; wherein the Weibull proportional parameter is used to describe the expected influence of the geometry and stress-strain state of the mechanical component on the low-cycle fatigue life of the mechanical component; wherein the hazard rate is the probability that crack initiation occurs in the predetermined cycle when there is no crack initiation until the cycle before the predetermined cycle, wherein the predetermined cycle is an operating cycle in the plurality of operating cycles corresponding to the hazard rate.

[0008] The above method solves the problem of inaccurate low-cycle fatigue determined due to fixed periodic operating conditions in related technologies, thereby improving the accuracy of low-cycle fatigue risk assessment.

[0009] In one embodiment of the present disclosure, obtaining the multiple periodic operating conditions of the mechanical component in the multiple operating cycles includes: obtaining the multiple historical periodic operating conditions of the mechanical component itself in the multiple operating cycles, and using them as the multiple periodic operating conditions; or obtaining the probability distribution estimates of the multiple periodic operating conditions of the mechanical component as the multiple periodic operating conditions, wherein the probability distribution estimates are obtained by referring to the statistical results of the multiple periodic operating conditions of other components that are the same as the mechanical component but distributed in different geographical locations in the multiple operating cycles, or are predetermined probability distributions that satisfy the multiple periodic operating conditions of the mechanical component.

[0010] Through the above method, low-cycle fatigue risk assessment can be performed not only based on fixed values ​​such as historical cyclic operating conditions, but also based on the probability distribution estimation of cyclic operating conditions.

[0011] In one embodiment of the present disclosure, calculating the Weibull proportional parameter based on a corresponding one of the multiple periodic operating conditions includes: calculating the periodic strain state of the surface position based on the periodic operating condition and the surface position of the mechanical component; calculating the point-by-point deterministic low-cycle fatigue life of the surface position based on the periodic strain state and the surface position; and calculating the Weibull proportional parameter for the entire surface area of ​​the mechanical component based on the point-by-point deterministic low-cycle fatigue life.

[0012] By using the above method to calculate the Weibull proportional parameter, the expected influence of the overall geometry and stress-strain state of the mechanical component on the low-cycle fatigue life can be calculated more accurately.

[0013] In one embodiment of the present disclosure, calculating the Weibull proportional parameter based on a corresponding one of the plurality of periodic operating conditions includes: calculating the Weibull proportional parameter based on each case of a corresponding one of the probability distributions of the plurality of periodic operating conditions, when the plurality of periodic operating conditions are estimates of the probability distributions of the plurality of periodic operating conditions.

[0014] Through the above method, when the cyclic operating conditions are estimated by probability distribution, the Weibull proportional parameter is accurately calculated for each case in each probability distribution, which makes it possible to improve the accuracy of low-cycle fatigue risk assessment.

[0015] In one embodiment of the present disclosure, calculating the hazard rate of the mechanical component based on the Weibull proportional parameters includes: when the multiple periodic operating conditions are the multiple historical periodic operating conditions, calculating the hazard rate based on the Weibull proportional parameters and the Weibull shape parameters that are independent of the strain state; and when the multiple periodic operating conditions are the probability distribution estimates of the multiple periodic operating conditions, calculating the hazard rate based on the probability distributions of the multiple periodic operating conditions and the Weibull proportional parameters corresponding to each case in the probability distributions, and the Weibull shape parameters that are independent of the strain state.

[0016] Through the above approach, an enhanced probabilistic LCF model is used to account for variability or uncertainty in the operating cycle, thereby providing a more accurate quantitative method for the evaluation of the LCF of mechanical components, which in turn can optimize risk assessment and help reduce product development or service costs.

[0017] In one embodiment of the present disclosure, determining the low-cycle fatigue of the mechanical component includes: when the multiple cycle operating conditions are the multiple historical cycle operating conditions, based on each of the hazard rates in the multiple operating cycles, calculating the risk probability of the low-cycle fatigue occurring to determine the low-cycle fatigue of the mechanical component; when the multiple cycle operating conditions are the probability distribution estimates of the multiple cycle operating conditions, based on each of the hazard rates in the multiple operating cycles, evaluating the probability distribution of the low-cycle fatigue life of the mechanical component to predict the low-cycle fatigue of the mechanical component.

[0018] The above method has the enhanced capability of considering the variable / random operating cycle. This can improve and reduce the cost of product design and evaluation, and can also optimize product service models.

[0019] In one embodiment of the present disclosure, after calculating a hazard rate based on the Weibull proportional parameter, the method further includes calculating a survival function based on each of the hazard rates of the plurality of operating cycles, wherein the survival function is the probability that no crack initiation occurs in the mechanical component within a predetermined period.

[0020] By using the above method, the probability of no crack initiation in a mechanical component during a certain cycle can be accurately determined.

[0021] In one embodiment of the present disclosure, calculating the survival function includes: multiplying the difference between the hazard rate of each operating cycle in the plurality of operating cycles and 1 to obtain the survival function.

[0022] By using the above method, the survival function can be accurately calculated to determine the probability of the absence of crack initiation.

[0023] In one embodiment of the present disclosure, after calculating a hazard rate based on the Weibull proportional parameter, the method further includes: calculating a probability distribution function satisfied by the low-cycle fatigue life based on each of the hazard rates of the plurality of operating cycles, wherein the probability distribution function is a cumulative distribution function or a probability mass function, wherein the cumulative distribution function is the probability of crack initiation in the mechanical component from the initial cycle to the end of the predetermined cycle, and the probability mass function is the degree to which the probability of crack initiation in the mechanical component from the initial cycle to the end of the predetermined cycle is higher than the probability of crack initiation in the stage from the initial cycle to the end of the previous cycle of the predetermined cycle.

[0024] Through the above method, the probability of crack initiation in the mechanical component from the initial cycle to the end of the predetermined cycle can be calculated, as well as the extent to which the probability of crack initiation in the stage from the initial cycle to the end of the predetermined cycle is higher than the probability of crack initiation in the stage from the initial cycle to the end of the previous cycle of the predetermined cycle. This allows for quantitative risk assessment of low-cycle fatigue from various angles, which helps reduce product development or service costs.

[0025] According to another aspect of the embodiments of the present disclosure, a storage medium is provided, on which a program is stored. When the program is executed by a computer, any of the above methods is performed.

[0026] The above-mentioned medium solves the problem of inaccurate low-cycle fatigue determined due to fixed periodic operating conditions in the related art, thereby improving the accuracy of low-cycle fatigue risk assessment.

[0027] According to another aspect of the embodiments of the present disclosure, a device for determining low-cycle fatigue of a mechanical component is provided, comprising: an acquisition module configured to acquire a plurality of cyclic operating conditions of the mechanical component in a plurality of operating cycles; a parameter calculation module configured to calculate a Weibull proportional parameter for each of the plurality of operating cycles based on a corresponding one of the plurality of cyclic operating conditions; a hazard rate calculation module configured to calculate a hazard rate of the mechanical component based on the Weibull proportional parameter for each of the plurality of operating cycles; and a determination module configured to determine the low-cycle fatigue of the mechanical component based on each of the hazard rates in the plurality of operating cycles.

[0028] The above device solves the problem in the related art of inaccurate low-cycle fatigue determination due to fixed periodic operating conditions, thereby improving the accuracy of low-cycle fatigue risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are intended to explain the present disclosure and do not constitute an improper limitation of the present disclosure. In the accompanying drawings:

[0030] Figure 1 is a flow chart of a method for determining low cycle fatigue of a mechanical component according to an embodiment of the present disclosure;

[0031] Figure 2 is a flow chart of another method for determining low cycle fatigue of a mechanical component according to an embodiment of the present disclosure; and

[0032] Figure 3 It is a schematic structural diagram of an apparatus for determining low-cycle fatigue of a mechanical component according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0033] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0034] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meaning as commonly understood by ordinary technicians in the technical field to which this application belongs.

[0035] In the present disclosure, unless otherwise specified, directional words such as "up, down, top, bottom" are usually used with reference to the directions shown in the drawings, or with reference to the components themselves in the vertical, perpendicular or gravity direction; similarly, for ease of understanding and description, "inside and outside" refer to the inside and outside relative to the outline of each component itself, but the above directional words are not used to limit the present disclosure.

[0036] First, a method of determining the LCF without considering the variability of periodic operating conditions will be described.

[0037] For a given surface and a component Ω for a given set of periodic operating conditions θ, under a strain state ∈=∈(x;θ) depending on the surface position x and the periodic operating condition θ, at a set of surface positions and times The crack count N(B,∈) in is a Poisson Point Process:

[0038] N(B,∈(·;θ))~Po(λ(B,∈(·;θ))),

[0039] The Poisson parameter λ(B,∈) can be modeled by the crack formation density function ρ = ρ(n,∈) (the average number of crack initiations per unit surface area and per unit time):

[0040] λ(B,∈)=∫ B ρ(n,∈(x;θ))dAdn..

[0041] In the Weibull method,

[0042]

[0043] where m is the Weibull shape parameter that is independent of the strain state, is the point-wise deterministic low-cycle fatigue life at a given surface location x with a cyclic strain state ∈(x;θ) with a given operating condition θ. Now consider the entire surface area of ​​the component. The Weibull scaling parameter is set to:

[0044]

[0045] Here θ is a constant, which is written as η = η(θ) for the sake of convenience and distinction from the following text. Then, the intensity parameter of the Poisson point process over the entire surface area in the time period [n1, n2] becomes:

[0046]

[0047] Since crack initiation is modeled by a Poisson point process, a conditional survival function can be derived as the probability that the crack initiation occurs at the component surface until time n. There is no probability of crack initiation:

[0048]

[0049] Then, the cumulative distribution function (CDF) of the random crack initiation time (i.e., LCF life) is obtained according to the survival probability calculated based on the conditional survival function:

[0050]

[0051] Next, the probability distribution function (PDF) is obtained according to the cumulative distribution function CDF, where the probability distribution function PDF is the derivative of the cumulative distribution function CDF:

[0052]

[0053] Finally, based on the probability distribution function PDF and conditional survival function obtained above, the hazard rate function is obtained, where the hazard rate function is defined as the instantaneous probability of crack initiation when no crack initiation has occurred so far (the hazard rate function will be particularly useful below):

[0054]

[0055] In the following, we will focus on describing the enhanced probabilistic LCF model that takes into account material uncertainties and variable periodic operating conditions.

[0056] In an embodiment of the present disclosure, a new probabilistic LCF evaluation method is proposed that takes into account the random nature of crack initiation and the variability or uncertainty of operating conditions. In this probabilistic LCF evaluation method, an enhanced probabilistic LCF model is adopted that takes into account variable periodic operating conditions.

[0057] First, we will describe the case where the cyclical operating conditions are historical cyclical operating conditions. To distinguish this from the case described below where the cyclical operating conditions are probability distributions, in this case, the crisis rate, survival function, probability distribution function, cumulative distribution function, and probability mass function are also referred to as the conditional crisis rate, conditional survival function, conditional probability distribution function, conditional cumulative distribution function, and conditional probability mass function.

[0058] In the modeling method of the probabilistic LCF model that does not consider the variability of periodic operating conditions, the random LCF life can be a set of positive real numbers. In the following, we consider the case where crack initiation can be observed only at the end of each cycle. This means that the random variable N is not to be described. i, but its ceiling integer:

[0059]

[0060] In short, in the following, the variable is an integer number of cycles, and is the real-valued (LCF lifetime) time.

[0061] When the cyclic operating conditions and the implicit strain state are constant throughout the cycle, the Poisson point process model of crack initiation in space and time naturally implies the fundamental assumption that random crack counts across disjoint groups of time and surface locations are independent, that is, uncorrelated. This assumption is made because LCF cracks are too small to alter the component's macroscopic strain state; therefore, crack initiation at one time and surface location has no effect on crack initiation at other times and locations. This assumption can be naturally extended to cases where the cyclic operating conditions and the resulting strain state vary from cycle to cycle. In effect, the component's macroscopic strain state is assumed to vary only due to the cyclic operating conditions, not due to any crack initiation. Furthermore, crack initiation is still affected only by the temporal and local strain state, but not by crack initiation at other times and surface locations. This assumption is referred to as the inter-cycle independence of random crack counts.

[0062] In the modeling method of the probabilistic LCF model that does not consider the variability of the cycle operating conditions, the cycle operating condition θ is constant in all cycles. However, from now on, the value of θ will be different in each cycle. Assume that a series of cycle operating conditions of the gas turbine components are It can be easily observed that within each cycle, the number of cracks still follows a Poisson random process. Given a cycle number k and a set of times and locations The number of cracks follows a Poisson distribution:

[0063] N(B,∈(·;θ k ))~Po(λ(B,∈(·;θ k ))).

[0064] make For a given set of operating conditions used for evaluation And for a given number of periods n, the conditional survival function depends only on the running period of the first n periods, that is:

[0065]

[0066] Based on the above hypothesis of inter-cycle independence of random crack counting, the conditional survival function with a given series of cyclic operating conditions can be written as the product of the probability of no crack initiation in each cycle time period:

[0067]

[0068] The last equation uses the result from equation (2). Therefore, the number of discrete random LCF life cycles is The conditional cumulative distribution function CDF is written as:

[0069]

[0070] use express The conditional probability mass function (PMF) of , then obviously, And, for n ≥ 1, the conditional probability mass function PMF is:

[0071]

[0072] The conditional hazard rate function will help to better understand the model. In the discrete case, the conditional hazard rate function is defined as the probability of crack initiation in a certain cycle given a series of cycle operating conditions, given that no crack initiation occurred until the previous cycle. It is expressed by the following mathematical formula:

[0073]

[0074] Equation (6) shows that the conditional hazard rate function under a specific number of cycles depends only on the periodic operating conditions of that cycle, that is, With this property, the conditional survival function, conditional cumulative distribution function CDF, and conditional probability mass function PMF in formulas (3) to (5) can be rewritten based on the conditional hazard rate function:

[0075]

[0076]

[0077]

[0078] Next, the case where the periodic operating condition is a probability distribution estimate will be described.

[0079] So far, we have been dealing with a given set of periodic operating conditions. If the exact set of periodic operating conditions is unknown, but we have an estimate of the probability distribution of the periodic operating conditions, we can model the periodic operating conditions as random variables. In general, we can assume that the periodic operating conditions of the nth period can be described by a distribution function In space A continuous random variable Θ on n The case with discrete random variables is similar and can be easily derived from the continuous case. Note that the distribution function It varies with the period. Then according to the definition of marginal probability, the following hazard rate function, survival function, CDF and PMF of the random LCF life cycle number can be obtained from equations (6) to (9):

[0080]

[0081]

[0082]

[0083]

[0084] Among all random variables In the special case that both satisfy the same distribution and they can be represented by the representative variable Θ (in the same space X Θ follows the same distribution f Θ (·)), we can get similar results by making only a slight modification to Equation (11) (i.e., replacing Θ with Θ n , replace θ with θ n ).

[0085] The following describes an algorithm for calculating the lifetime of an LCF with varying and / or random operating periods. Figure 1 The flowchart of the method for determining low cycle fatigue of a mechanical component according to an embodiment of the present disclosure is shown. In the enhanced probabilistic LCF model, an algorithm for determining low cycle fatigue is generated based on equations (1), (6)-(9), which can estimate the probability distribution of the LCF life cycle number under a given series of periodic operating conditions, that is, determine the probability of low cycle fatigue of the mechanical component. The flow of the determination method is as follows: Figure 1 As shown, the following steps are included:

[0086] S102, obtaining a series of cyclic operation conditions

[0087] S104, calculating the Weibull proportional parameter of each operation cycle.

[0088] With the Weibull shape parameter m fixed, the Weibull scale parameter η(θ) for each cycle is calculated using the existing ProbLCF tool using equation (1) k ). For each of the plurality of operating cycles, a Weibull distribution proportion parameter is calculated based on each possible situation of a corresponding cycle operating condition in the plurality of cycle operating conditions in the corresponding probability distribution.

[0089] S106, calculating the hazard rate function of each operation cycle.

[0090] The conditional hazard rate function for each period is calculated using equation (6).

[0091] S108, calculate the conditional survival function, conditional CDF and conditional PMF.

[0092] The conditional survival function, conditional CDF, and conditional PMF are obtained through equations (7) to (9).

[0093] This method is particularly useful for estimating the LCF crack risk or remaining LCF life of gas turbine components during the product service phase. Knowing the operating history allows accurate calculation of the component's conditional survival function and the conditional CDF of the LCF life. These functions provide a quantified LCF crack initiation risk during assessment and aid in service decision-making. For example, if the calculated conditional CDF value during assessment is close to 1, the risk of LCF crack initiation is relatively high, which may lead to the recommendation for component repair or replacement based on further engineering judgment and decision-making. Conversely, if the calculated conditional CDF value is well below 1, the component can still be safely serviced.

[0094] If a series of periodic operating conditions is unknown, but a probability distribution estimate of a random series of periodic operating conditions is available, then the random LCF lifetime can be calculated in the enhanced probabilistic LCF model using equations (1), (6), and (11)–(14), regardless of whether the periodic operating conditions are the same or not. Figure 2 FIG. 1 is a flow chart of another method for determining low cycle fatigue of a mechanical component according to an embodiment of the present disclosure. Figure 2 As shown, the method includes:

[0095] S202, sampling period operating conditions.

[0096] For each period n, The space after Medium sampling period operating condition θ n .

[0097] S204, calculating the Weibull proportional parameter.

[0098] Fixed Weibull shape parameter m, the Weibull scale parameter η(θ) is calculated for each cycle and each sampled cycle operating condition using equation (1) using the existing ProbLCF tool n ).

[0099] S206, calculate the hazard rate.

[0100] For each cycle and each sampled period operating condition, the conditional hazard rate function is calculated using (6).

[0101] S208, calculate the hazard rate function, survival function, CDF and PMF

[0102] The hazard rate function, survival function, CDF and PMF are obtained through equations (11) to (14).

[0103] This method is particularly useful during the design phase of mechanical components. The probability distribution of future operating cycles can be estimated by statistical analysis of existing fleet data or through engineering judgment.

[0104] The present disclosure also provides an apparatus for determining low cycle fatigue of a mechanical component. Figure 3 It is a structural diagram of an apparatus for determining low-cycle fatigue of a mechanical component according to an embodiment of the present disclosure. The apparatus 300 for determining low-cycle fatigue of a mechanical component includes an acquisition module 32 , a parameter calculation module 34 , a hazard rate calculation module 36 and a determination module 38 .

[0105] The acquisition module 32 is configured to acquire a plurality of cyclic operating conditions of the mechanical component in a plurality of operating cycles; the parameter calculation module 34 is configured to calculate a Weibull proportional parameter for each of the plurality of operating cycles based on a corresponding cyclic operating condition in the plurality of cyclic operating conditions; the hazard rate calculation module 36 is configured to calculate a conditional hazard rate based on the Weibull proportional parameter for each of the plurality of operating cycles; and the determination module 38 is configured to determine whether the mechanical component is in low-cycle fatigue based on the respective conditional hazard rates in the plurality of operating cycles.

[0106] In this disclosure, an enhanced probabilistic LCF model is used to account for variability or uncertainty in the operating cycle. This embodiment provides a more accurate quantitative method for evaluating the LCF of mechanical components, which can optimize risk assessment and help reduce product development or service costs.

[0107] The present disclosure technically improves product design and service methods with enhanced functionality that takes into account variable / random operating cycles, which can improve and reduce the cost of product design and evaluation, and can also optimize product service models.

[0108] Obviously, the embodiments described above are only a part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0109] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, tasks, devices, components and / or combinations thereof.

[0110] It should be noted that the terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0111] The foregoing description is merely a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will readily appreciate that the present disclosure is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present disclosure shall be included within the scope of protection of the present disclosure.

Claims

1. A method for determining low cycle fatigue of a mechanical component, characterized in that include: Acquiring a plurality of periodic operating conditions of the mechanical component in a plurality of operating cycles; For each of the plurality of operating cycles, calculating a Weibull proportional parameter based on a corresponding one of the plurality of periodic operating conditions; calculating a hazard rate of the mechanical component based on the Weibull proportional parameter for each of the plurality of operation cycles; For each of the plurality of operating cycles, calculating a survival function based on the respective hazard rates of the plurality of operating cycles; as well as determining low cycle fatigue of the mechanical component based on each of the hazard rates in the plurality of operating cycles; Wherein, the Weibull proportional parameter is used to describe the influence of the geometric shape and stress-strain state of the mechanical component on the low-cycle fatigue life expectancy of the mechanical component; wherein the hazard rate is a probability that crack initiation occurs in the predetermined period when no crack initiation occurs in the period preceding the predetermined period, wherein the predetermined period is an operating period corresponding to the hazard rate among the plurality of operating periods; The survival function is the probability that no crack initiation occurs in the mechanical component within a predetermined period.

2. The method according to claim 1, characterized in that Acquiring a plurality of periodic operating conditions of the mechanical component in a plurality of operating cycles includes: Acquiring a plurality of historical periodic operating conditions of the mechanical component itself in the plurality of operating cycles and using them as the plurality of periodic operating conditions; or Obtain probability distribution estimates of each of the multiple periodic operating conditions of the mechanical component as the multiple periodic operating conditions, wherein the probability distribution estimates are obtained by referring to statistical results of the multiple periodic operating conditions of other components that are the same as the mechanical component but distributed at different geographical locations in the multiple operating cycles, or are predetermined probability distributions that satisfy the multiple periodic operating conditions of the mechanical component.

3. The method according to claim 2, characterized in that Calculating the Weibull proportional parameter based on a corresponding one of the plurality of periodic operating conditions includes: calculating a cyclic strain state of the surface position based on the cyclic operating condition and the surface position of the mechanical component; calculating a point-by-point deterministic low cycle fatigue life of the surface location based on the cyclic strain state and the surface location; and Based on the point-wise deterministic low cycle fatigue life, the Weibull proportionality parameter is calculated for the entire surface area of ​​the mechanical component.

4. The method according to claim 2, characterized in that Calculating the Weibull proportional parameter based on a corresponding one of the plurality of periodic operating conditions includes: calculating the Weibull proportional parameter based on each case of a corresponding one of the probability distributions of the plurality of periodic operating conditions, when the plurality of periodic operating conditions are estimates of the probability distributions of the plurality of periodic operating conditions.

5. The method according to claim 2, characterized in that Calculating the risk rate of the mechanical component based on the Weibull proportional parameter includes: When the plurality of periodic operating conditions are the plurality of historical periodic operating conditions, calculating the hazard rate based on the Weibull proportional parameter and a Weibull shape parameter that is independent of the strain state; and In a case where the plurality of periodic operating conditions are estimates of the probability distributions of the plurality of periodic operating conditions, the hazard rate is calculated based on the probability distributions of the plurality of periodic operating conditions, the Weibull scale parameter corresponding to each case in the probability distributions, and the Weibull shape parameter that is independent of the strain state.

6. The method according to claim 2, characterized in that Determining the low cycle fatigue of the mechanical component includes: When the plurality of cycle operating conditions are the plurality of historical cycle operating conditions, calculating the risk probability of occurrence of the low cycle fatigue based on each of the hazard rates in the plurality of operating cycles to determine the low cycle fatigue of the mechanical component; In the case where the multiple cycle operating conditions are probability distribution estimates of the respective multiple cycle operating conditions, the probability distribution of the low-cycle fatigue life of the mechanical component is evaluated based on the respective hazard rates in the multiple operating cycles to predict the low-cycle fatigue of the mechanical component.

7. The method according to claim 1, characterized in that Calculating the survival function includes multiplying the difference between the hazard rate of each operating cycle in the plurality of operating cycles and 1 to obtain the survival function.

8. The method according to claim 1, characterized in that After calculating a hazard rate based on the Weibull proportional parameter, the method further includes: calculating a probability distribution function satisfied by the low-cycle fatigue life based on each of the hazard rates of the plurality of operating cycles, wherein the probability distribution function is a cumulative distribution function or a probability mass function, wherein the cumulative distribution function is the probability of crack initiation in the mechanical component from the initial cycle to the end of the predetermined cycle, and the probability mass function is the extent to which the probability of crack initiation in the mechanical component from the initial cycle to the end of the predetermined cycle is higher than the probability of crack initiation in the stage from the initial cycle to the end of the cycle before the predetermined cycle.

9. A storage medium having a program stored thereon, characterized in that: When the program is executed by a computer, the method according to any one of claims 1 to 8 is performed.

10. A device for determining low cycle fatigue of a mechanical component, characterized in that The device is used to perform the method according to claim 1, and the device includes: an acquisition module configured to acquire a plurality of periodic operating conditions of the mechanical component in a plurality of operating cycles; a parameter calculation module configured to calculate a Weibull proportional parameter for each of the plurality of operation cycles based on a corresponding one of the plurality of cycle operation conditions; a hazard rate calculation module configured to calculate a hazard rate of the mechanical component based on the Weibull proportional parameter for each of the plurality of operation cycles; and The determination module is configured to determine low cycle fatigue of the mechanical component based on the respective hazard rates in the plurality of operation cycles.

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