Prediction method for residual fatigue life of corroded steel wire based on Markov chain

Abstract

The embodiment of the invention discloses a Markov chain-based corroded steel wire residual fatigue life prediction method, and relates to the technical field of corroded steel wire life prediction. The method comprises the following steps: calculating probability distribution of initial crack depth of a steel wire according to the corrosion degree of the steel wire; randomly extracting a plurality of initial crack depths from the probability distribution, and respectively executing the following operations: taking the current initial crack depth and the critical crack depth as two boundary values, and performing state division of the crack depths; describing the probability of crack depth expansion by utilizing geometric distribution, determining a state transition matrix based on geometric distribution characteristics, and substituting the state transition matrix into a Markov chain to obtain a curve of crack depth changing along with time; substituting the critical crack depth into the curve, and predicting the residual fatigue life of the steel wire at the current initial crack depth; and carrying out probability fitting on the residual fatigue life under the plurality of initial crack depths to obtain probability distribution of the residual fatigue life of the steel wire. The embodiment improves the prediction effect of the fatigue life of the steel wire.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of corroded steel wire life prediction, and in particular to a method for predicting the remaining fatigue life of corroded steel wire based on a Markov chain. Background Art

[0002] The high-strength steel wires in bridge cables and suspenders serve in harsh environments such as the marine atmosphere and industrially polluted areas for a long time. They are continuously exposed to salt spray, moisture, and chemical media corrosion, while bearing the repeated effects of alternating stresses such as vehicle loads and wind loads. Under the coupling effect of such complex environments and mechanical conditions, local corrosion damage (such as pitting and crack initiation) is prone to occur on the surface of the steel wire, and the alternating stress further accelerates the penetration of the corrosive medium and the expansion of damage, forming a "corrosion-fatigue" synergistic deterioration mechanism. This multi-physics coupling effect not only significantly weakens the mechanical properties of the steel wire, but also leads to a high degree of randomness in the evolution of damage - the distribution of microscopic defects, the heterogeneity of corrosion morphology, and the uncertainty of crack propagation paths all pose severe challenges to the accuracy of life prediction.

[0003] The existing technology for predicting the fatigue life of steel wire is usually based on deterministic assumptions, and the impact of corrosion is difficult to quantify. For example:

[0004] One of the methods is based on the fracture mechanics theory and proposes a crack propagation estimation model for pre-corroded steel wire. This method calculates the fatigue threshold value ΔK of pearlite steel wire by statistically analyzing the fatigue threshold value ΔK of pearlite steel wire. th and crack growth rate data, an empirical formula considering the material yield strength and stress ratio was established, and the Donahue three-parameter model was used to quantify the crack growth behavior near the threshold area. By assuming one-dimensional crack growth and combining it with the numerical integration method, the life prediction of corroded steel wire under constant amplitude and variable amplitude loads was achieved. The test showed that this method can well simulate the discreteness of the fatigue life of corroded steel wire, especially under constant amplitude conditions, it can reflect the downward trend of the fatigue limit band. However, the initial crack depth needs to be preset to a fixed value, which fails to characterize the randomness of the actual pit distribution, resulting in the life prediction under variable amplitude load being too sensitive to the threshold value and significant discreteness.

[0005] Another method starts from the staged evolution mechanism of corrosion fatigue, decomposing the damage process into seven stages, including galvanized layer failure, substrate pit initiation, and short crack propagation, and establishing a multi-stage life superposition model. The pit propagation rate is quantified by Faraday's electrochemical law, and the crack propagation behavior is described in combination with the Paris formula. The study pointed out that the short crack propagation time accounts for more than 60% of the total life, and when the stress amplitude increases from 100 MPa (megapascals) to 150 MPa, the crack propagation time drops sharply from 18.82 years to 6.77 years, highlighting the significant impact of load amplitude on life. Although this method is more in line with the actual corrosion process through staged modeling, the model relies on deterministic assumptions and does not consider the impact of the randomness of the spatial distribution of pits on local stress concentration. It also does not distinguish the dynamic differences in the propagation rates of short and long cracks (for example, the short crack propagation rate is significantly affected by the microstructure), resulting in deviations in life prediction under high stress amplitudes.

[0006] In summary, the current methods for predicting the remaining fatigue life of corroded steel wire have the following limitations: traditional models rely on deterministic parameters and static assumptions, and are unable to characterize the randomness of damage evolution such as the random initiation of corrosion pits and the bifurcation of crack propagation paths, thus limiting the prediction effect. Summary of the Invention

[0007] The embodiment of the present invention provides a method for predicting the remaining fatigue life of a corroded steel wire based on a Markov chain, thereby achieving efficient and accurate prediction of the remaining life of the corroded steel wire.

[0008] In a first aspect, an embodiment of the present invention provides a method for predicting the remaining fatigue life of a corroded steel wire based on a Markov chain, comprising:

[0009] According to the corrosion degree of the steel wire, the probability distribution of the initial crack depth of the steel wire is calculated; according to the fracture toughness of the steel wire, the critical crack depth of the steel wire is calculated;

[0010] A plurality of initial crack depths are randomly selected from the probability distribution, and the following operations are performed for each initial crack depth:

[0011] S1-1, dividing the crack depth of the steel wire into multiple states using the current initial crack depth and the critical crack depth as two boundary values;

[0012] S1-2. Use geometric distribution to describe the probability of crack depth extension, and determine the state transition matrix in Markov theory based on the geometric distribution characteristics and the crack depth increments between each state;

[0013] S1-3, substituting the state transfer matrix into the Markov chain to obtain a curve showing crack depth changing with time;

[0014] S1-4, substituting the critical crack depth into the curve to predict the remaining fatigue life of the steel wire at the current initial crack depth;

[0015] Probabilistic fitting is performed on the remaining fatigue life at the multiple initial crack depths to obtain a probability distribution of the remaining fatigue life of the steel wire.

[0016] In a second aspect, an embodiment of the present invention provides an electronic device, comprising:

[0017] one or more processors;

[0018] a memory for storing one or more programs,

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement the Markov chain-based method for predicting the remaining fatigue life of a corroded steel wire as described in any embodiment.

[0020] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the Markov chain-based method for predicting the remaining fatigue life of corroded steel wire described in any embodiment.

[0021] In summary, the present invention provides a Markov chain-based method for predicting the remaining fatigue life of corroded steel wire. First, the probability distribution of the initial crack depth of the steel wire is calculated based on the corrosion level of the steel wire, and the critical crack depth is calculated based on the fracture toughness. The initial crack depth and the critical crack depth are then used as two boundary values ​​to divide the crack depth into several states, and a state transition probability matrix is ​​calculated based on the geometric distribution. Finally, a crack growth curve is obtained based on the Markov chain. On this basis, the Monte Carlo method is used to fit the fatigue life distribution model. This method has the following technical improvements and beneficial effects:

[0022] 1. Markov chain-based stochastic modeling: This is the first application of Markov chains to the prediction of the remaining fatigue life of corroded steel wire. The stochastic process of crack propagation is dynamically described through the state transition probability matrix, breaking through the limitations of traditional deterministic models.

[0023] 2. Probabilistic analysis of initial cracks: An extreme value type I distribution is introduced to characterize the randomness of the initial crack depth. Combined with the statistical relationship between the corrosion degree and the pitting coefficient, the uncertainty of the spatial distribution of corrosion pits is quantified, breaking through the limitation of traditional methods that set the initial crack depth to a fixed value. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 This is a schematic diagram of state transition in Markov theory provided by an embodiment of the present invention;

[0026] Figure 2 This is a flow chart of a method for predicting the remaining fatigue life of a corroded steel wire based on a Markov chain provided by an embodiment of the present invention;

[0027] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0029] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0030] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0031] The present invention provides a Markov chain-based method for predicting the remaining fatigue life of corroded steel wire. This method uses a Markov chain to describe the crack depth growth process in the steel wire and a geometric distribution to describe the probability of crack depth growth, thereby predicting the remaining fatigue life of the steel wire. To illustrate this method, the basic Markov theory underlying it is first introduced.

[0032] Specifically, in Markov theory, time and state are discrete, and it is believed that the state of the system changes over time. For example, when the system is in state 1, the probability that the system remains in state 1 after one time step is q 1,1 , the probability that the system transfers to state 2 is q 1,2 ,like Figure 1 As shown. Each state j corresponds to a value v j , all state values ​​can form a state value vector V = [v1, v2, ..., v j ], j = 1, 2, ..., represents the state index. When a system changes, it is considered a state change. This change is only related to the previous state and is independent of the remaining states. During the crack growth process in a corroded steel wire, each expansion builds upon the previous one and is independent of the previous crack state. Therefore, the crack growth process conforms to the Markov characteristic.

[0033] Furthermore, the basic variables in the Markov chain include:

[0034] The initial state distribution P(0) of the system, referred to as the initial state, is expressed as:

[0035] P(0)=[p1(0),p2(0),...,p j (0)] (1)

[0036] Among them, p j (0) represents the probability that the system is in state j at the initial time step;

[0037] The state transition probability matrix Q, referred to as the probability matrix, is used to represent the probability of each state remaining in the current state or transitioning to the next state. Its specific form will be described in detail in subsequent embodiments.

[0038] Based on the above V, P(0) and Q, the state mean v(t) of the system at any time step t can be expressed as:

[0039] v(t)=P(0)×Q t ×V (2)

[0040] Formula (2) can also be called a Markov chain.

[0041] Based on the above basic theories, Figure 2 This is a flowchart of a method for predicting the remaining fatigue life of a corroded steel wire based on a Markov chain provided by an embodiment of the present invention. The method is executed by an electronic device, such as Figure 2 As shown, the specific steps include:

[0042] S110. Calculate the probability distribution of the initial crack depth of the steel wire based on the corrosion degree of the steel wire; and calculate the critical crack depth of the steel wire based on the fracture toughness of the steel wire.

[0043] Since the initial crack depth of the corroded steel wire cannot be measured, this embodiment determines the probability distribution of the initial crack depth, which will be used to predict the probability distribution of the remaining service life of the steel wire.

[0044] In one embodiment, the probability distribution of the initial crack depth can be determined by the following steps:

[0045] Step 1: Obtain the initial mass and post-corrosion mass of the same type of brand-new steel wire in the corrosion test; and calculate the uniform corrosion depth of the steel wire based on the initial mass and post-corrosion mass. This step requires a corrosion test on a brand-new steel wire of the same type as the steel wire to be predicted. Suppose the mass of the brand-new steel wire is m and the mass of the corroded steel wire is m. c , then the uniform corrosion degree η of the steel wire is:

[0046]

[0047] Step 2: Calculate the uniform corrosion depth a of the steel wire according to the uniform corrosion degree. m :

[0048]

[0049] Where D represents the wire diameter.

[0050] Step 3: Equivalent the maximum pitting depth to the initial crack depth of the steel wire, and based on the relationship between the maximum pitting depth, uniform corrosion depth and pitting coefficient, and the type I distribution obeyed by the pitting coefficient, determine the type I distribution obeyed by the initial crack depth.

[0051] Specifically, as corrosion progresses, tiny pits gradually form on the steel wire, which can become fatigue crack sources under load. The pitting coefficient ξ describes the uniform corrosion depth a m and maximum pitting depth a p Relationship:

[0052]

[0053] The pitting coefficient ξ obeys the extreme value type I distribution F(ξ):

[0054]

[0055] Where α1 represents the scale parameter of the distribution, and β1 represents the location parameter of the distribution. The values ​​of α1 and β1 are related to the lateral area A of the steel wire to be predicted. In practical applications, the extreme value type I distribution obeyed by the pitting coefficient ξ of certain types of steel wire is known, and the size parameter α0 and location parameter β0 are related to the lateral area A0 of this type of steel wire. In this embodiment, α1 and β1 of the steel wire to be predicted can be determined with the help of the known α0 and β0 of the steel wire type:

[0056]

[0057] Since micro cracks will be generated from the pits, the maximum pitting depth a is set as p Equivalent to the initial crack depth:

[0058] X0=a p =a m ×ξ (8)

[0059] Among them, X0 represents the variable of initial crack depth.

[0060] According to the definition of probability distribution function, we can get:

[0061]

[0062] in, represents the probability distribution of the value x0 of X0, and P(X0≤x0) represents the probability that X0 takes a value less than or equal to x0. Combining equations (6) and (9), we can obtain:

[0063]

[0064] Therefore, the initial crack depth obeys the scale parameter Positional parameters It should be noted that, in order to describe the definition of the probability distribution function (Equation (9)), X0 and x0 are used to represent the variable name and value of the initial crack depth, respectively. In subsequent embodiments, there is no need to distinguish the name and value of the variable. Therefore, unless otherwise specified, the subsequent initial crack depth refers to the value of the initial crack depth, and is uniformly represented by the symbol x0.

[0065] In summary, this embodiment derives the extreme type I distribution of the initial crack depth x0 based on the extreme value distribution of corrosion mass loss and pitting coefficient, quantifies the randomness of corrosion, and breaks the limitation of the traditional method of presetting the initial crack depth to a fixed value.

[0066] Optional, critical crack depth x c According to the fracture toughness Kc Calculation. Specifically, the crack depth x and shape factor Y of the steel wire satisfy the following formula:

[0067]

[0068] Where σ represents the stress on the steel wire. After combining equations (11) and (12), take an x ​​(denoted as x 输入 ) into formula (12), we can get a new x (denoted as x) from formula (11) 输出 ); gradually increase the crack depth x 输入 Repeat the above calculation. 输入 When x is small, 输入 <x 输出 ; With x 输入 As the value of x increases, the two gradually approach each other until x 输入 =x 输出 , at this time x 输入 is the critical crack depth x c .

[0069] S120. Randomly extract a plurality of initial crack depths from the probability distribution of the initial crack depths, and predict the remaining fatigue life of the steel wire for each initial crack depth.

[0070] Alternatively, the Monte Carlo method can be used to randomly select multiple values ​​from the type I distribution shown in formula (10) as the initial crack depth x0, and the remaining fatigue life of the steel wire can be calculated based on each x0.

[0071] In a specific embodiment, the following operations may be performed for each initial crack depth:

[0072] S1-1. Using the current initial crack depth and the critical crack depth as two boundary values, the crack depth of the steel wire is divided into multiple states.

[0073] This step constructs the Markov state space of crack depth, where the current initial crack depth refers to the initial crack depth currently being calculated.

[0074] Specifically, based on the Markov basic theory introduced at the beginning, the crack depth state (also called the wire state) is first divided. Optionally, the crack depth increment δx can be set from the current initial crack depth x0 to the critical crack depth x c The steel wire crack propagation process is divided into multiple intervals, where each interval corresponds to a crack depth state.

[0075] Specifically, assuming that the crack expands by δx, the steel wire transfers to the next state. The crack expansion process can be described by the following formula:

[0076] x i =x0+(i-1)×δx (13)

[0077] Where x i It indicates the crack depth when the steel wire is in state i, where i represents the state index of the crack depth.

[0078] When the crack grows to the critical size x c When the steel wire is considered broken, the crack state can be divided into the following categories: [x0,x0+δx], [x0+δx,x0+2×δx], …, [x0+(m-1)×δx,x c ], where m represents the number of states. The median of each interval is taken as the state representative value to form the state value vector V of the steel wire c :

[0079]

[0080] Optionally, the initial state of the steel wire (or the initial state of the crack depth) is represented as a one-dimensional vector of dimension M, where the first element of the vector is non-zero and the remaining elements are set to 0. Then the initial state P of the steel wire is c (0) is:

[0081] P c (0)=[1,0,...,0] (15)

[0082] S1-2. Use geometric distribution to describe the probability of crack depth extension, and determine the state transition matrix in Markov theory based on the geometric distribution characteristics and the crack depth increments between each state.

[0083] Specifically, the Paris formula describes the crack growth rate Satisfied relationship:

[0084]

[0085] Where N represents the number of stress cycles, dx represents the crack depth increment, dN represents the increment of the stress cycle number, and ΔK represents the stress intensity factor. C and m are material parameters that can be determined using the variable load notch method.

[0086] According to the state classification in S1-1, the crack depth increases by δx each time the steel wire changes state. Substituting δx as dx into the Paris formula, the crack growth rate during each state transition can be obtained.

[0087]

[0088] Where E(δN) represents the average number of stress cycles δN that the steel wire withstands during each state transition, and E(ΔK) represents the average value of the stress intensity factor δN during each state transition.

[0089] Applying formula (17) to each state i, we have:

[0090]

[0091] Among them, E(δN i ) represents the number of stress cycles δN that the steel wire endures during the period when the crack depth extends from state i to the next state i The mean value of E(ΔK i ) represents the stress intensity factor ΔK under state i i The mean of .

[0092] Assuming that the steel wire is in state i (i.e. the crack depth is in state i), the probability of extending to the next state after one stress cycle is (1-q i,i+1 ), the probability of not expanding is q i,i+1 Therefore, the probability of crack growth can be described by geometric distribution, and the probability P(δN) that the crack depth will grow to the next state after r stress cycles is i =r) can be expressed as:

[0093] P(δN i =r) =q i,i+1 ×(1-q i,i+1 ) r-1 (19)

[0094] And E(δN i )satisfy:

[0095]

[0096] Substituting formula (20) into formula (18), we have:

[0097]

[0098] The ΔK in each depth interval i (ΔK i The specific method is the existing technology), and C, m are substituted into formula (21), and the values ​​of q can be obtained. i,i+1 .

[0099] Using (1-q i,i+1 ) and q i,i+1 , we can get the state transfer matrix Q in Markov theory c :

[0100]

[0101] Among them, Q c The element in the i-th row and b-th column in represents the probability that the crack depth extends from state i to state b at each time step.

[0102] It should be noted that the above process derives the calculation method of state transition probability through equations (16) to (20), namely equation (21). In practical applications, there is no need to derive equations (16) to (20) in each calculation. C, m and ΔK can be directly i Substitute into formula (21) to calculate each q i,i+1 , by each q i,i+1 Composition Q c .

[0103] S1-3. Substitute the state transfer matrix into the Markov chain to obtain a curve showing the change of crack depth over time.

[0104] Specifically, Q c 、V c and P c (0) is substituted into the Markov chain shown in formula (2), and the curve x(r) showing the crack depth changing with the stress number r can be obtained:

[0105] x(r)=P c (0)×Q c r ×V c (twenty three)

[0106] Here, x(r) can also be called the crack growth curve, and the increase in stress number r represents the passage of time.

[0107] S1-4. Substituting the critical crack depth into the curve, and predicting the remaining fatigue life of the steel wire at the current initial crack depth.

[0108] Specifically, when x is equal to the critical dimension x c When the steel wire breaks, r is the fatigue life. c Substituting x(r) into equation (23), the obtained r is the remaining fatigue life of the corroded steel wire at the current initial crack depth.

[0109] From the above steps, it can be seen that the interval length δx has a great influence on the prediction results. Therefore, this embodiment optimizes the value of δx through convergence analysis. Specifically, take multiple uniformly varying crack depth increments δx, execute the entire method for each δx separately, and predict the remaining fatigue life of the corroded steel wire; when the prediction result remains stable, take the corresponding δx as the optimal crack depth increment, which can be applied to subsequent predictions. Exemplarily, take δx = 0.1, 0.05, 0.01 respectively, and then calculate the remaining life according to the above method respectively. When the difference in fatigue life calculated several times in a row is within 1%, the prediction result is basically converged, and the δx at this time can be taken for subsequent calculations.

[0110] In summary, this embodiment utilizes the Markov state transition mechanism to discretize the crack propagation process into a state sequence, and calculates the state transition probability matrix through geometric distribution to achieve memoryless modeling of the dynamic evolution of crack depth; at the same time, through the convergence analysis of the interval length δx, it is ensured that the influence of state division on the stability of the prediction result is less than 1%, thereby realizing the prediction of the remaining life of the steel wire under a single initial crack depth.

[0111] S130. Perform probability fitting on the remaining fatigue life at the multiple initial crack depths to obtain a probability distribution of the remaining fatigue life of the steel wire.

[0112] By performing S1-1 through S1-4 for each initial crack depth in S120, the remaining fatigue life corresponding to each initial crack depth can be obtained. By fitting these fatigue lives with multiple probabilistic estimates and performing a KS test, the optimal distribution of the remaining fatigue life of the steel wire can be obtained.

[0113] Furthermore, for different levels of varying stress amplitude, the Monte Carlo method can be used to randomly sample x0 and recalculate the remaining fatigue life. By plotting the remaining fatigue life and stress amplitude in a double logarithmic coordinate system and performing a linear fit, the SN curve (i.e., fatigue life curve) of the corroded steel wire can be obtained to describe the relationship between stress level (S) and fatigue life (N).

[0114] In summary, this embodiment provides a method for predicting the remaining fatigue life of corroded steel wire based on a Markov chain. First, based on the degree of corrosion of the steel wire, the probability distribution of the initial crack depth of the steel wire is calculated, and the critical crack depth is calculated based on the fracture toughness. Then, the initial crack depth and the critical crack depth are used as two boundary values ​​to divide the crack depth into several states, and the state transition probability matrix is ​​calculated based on the geometric distribution. Finally, the crack propagation curve is obtained based on the Markov chain. On this basis, the Monte Carlo method is used to fit the distribution model of fatigue life. This method has the following technical improvements and

[0115] Beneficial effects:

[0116] 1. Markov chain-based stochastic modeling: This is the first application of Markov chains to the prediction of the remaining fatigue life of corroded steel wire. The stochastic process of crack propagation is dynamically described through the state transition probability matrix, breaking through the limitations of traditional deterministic models.

[0117] 2. Probabilistic analysis of initial cracks: This method introduces an extreme value type I distribution to characterize the randomness of the initial crack depth. Combining this with the statistical relationship between corrosion severity and pitting coefficient, it quantifies the uncertainty of the spatial distribution of corrosion pits, overcoming the limitation of traditional methods that set the initial crack depth to a fixed value.

[0118] 3. Optimize the stability of the model through dynamic convergence analysis: An adaptive convergence analysis method based on the interval length δx is proposed. By iteratively adjusting the discretization step size of the crack state division (such as δx = 0.1, 0.05 and 0.01), the stability of the fatigue life prediction results is verified and the quality of life prediction is improved.

[0119] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the device includes a processor 60, a memory 61, an input device 62 and an output device 63; the number of processors 60 in the device can be one or more. Figure 3 In the embodiment, a processor 60 is used as an example; the processor 60, the memory 61, the input device 62 and the output device 63 in the device can be connected by a bus or other means. Figure 3 The bus connection is taken as an example.

[0120] The memory 61, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the Markov chain-based method for predicting the remaining fatigue life of corroded steel wire in the embodiments of the present invention. The processor 60 executes the software programs, instructions, and modules stored in the memory 61 to perform various functional applications and data processing of the device, thereby implementing the aforementioned Markov chain-based method for predicting the remaining fatigue life of corroded steel wire.

[0121] The memory 61 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the terminal. Furthermore, the memory 61 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some instances, the memory 61 may further include memory remotely located relative to the processor 60, and these remote memories may be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0122] The input device 62 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 63 may include a display device such as a display screen.

[0123] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for predicting the remaining fatigue life of a corroded steel wire based on a Markov chain according to any embodiment is implemented.

[0124] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or components, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by an instruction execution system, device or device or used in combination with it.

[0125] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0126] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0127] Computer program code for performing the operations of the present invention can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the remaining fatigue life of corroded steel wire based on Markov chain, characterized in that: include: According to the corrosion degree of the steel wire, the probability distribution of the initial crack depth of the steel wire is calculated; according to the fracture toughness of the steel wire, the critical crack depth of the steel wire is calculated; A plurality of initial crack depths are randomly selected from the probability distribution, and the following operations are performed for each initial crack depth: S1-1, dividing the crack depth of the steel wire into multiple states using the current initial crack depth and the critical crack depth as two boundary values; S1-2. Use geometric distribution to describe the probability of crack depth extension, and determine the state transition matrix in Markov theory based on the geometric distribution characteristics and the crack depth increments between each state; S1-3, substituting the state transfer matrix into the Markov chain to obtain a curve showing crack depth changing with time; S1-4, substituting the critical crack depth into the curve to predict the remaining fatigue life of the steel wire at the current initial crack depth; Probabilistic fitting is performed on the remaining fatigue life at the multiple initial crack depths to obtain a probability distribution of the remaining fatigue life of the steel wire.

2. The method according to claim 1, characterized in that The method of calculating the probability distribution of the initial crack depth of the steel wire according to the corrosion degree of the steel wire includes: Obtain the initial quality and post-corrosion quality of the same type of new steel wire in the corrosion test; Calculating the uniform corrosion depth of the steel wire based on the initial mass and the mass after corrosion; The maximum pitting depth is equivalent to the initial crack depth of the steel wire, and based on the relationship among the maximum pitting depth, uniform corrosion depth and pitting coefficient, as well as the type I distribution obeyed by the pitting coefficient, the type I distribution obeyed by the initial crack depth is determined.

3. The method according to claim 1, characterized in that S1-1 includes: According to the set crack depth increment δx, the crack depth from the current initial crack depth x0 to the critical crack depth x c The crack depth range is divided into M intervals, where M is a natural number and each interval corresponds to a crack depth state; Select a representative value from each interval, and arrange the representative values ​​in sequence to form the state value vector in the Markov chain; The initial state of the steel wire crack depth is represented as a one-dimensional vector with a dimension of M, where the first element of the vector is non-zero and the remaining elements are set to 0.

4. The method according to claim 3, characterized in that Also includes: Take multiple uniformly varying crack depth increments δx and perform operations S1-1 to S1-4 respectively to predict the remaining fatigue life; When the prediction results remain stable, the corresponding δx is taken as the optimal crack depth increment and used in subsequent predictions.

5. The method according to claim 1, wherein S1-2 includes: The probability of crack depth extension is described by geometric distribution. When the crack depth is in state i, the probability of extending to the next state after one stress cycle is expressed as (1-q i,i+1 ), the probability of not extending to the next state is expressed as q i,i+1 ; Calculate q according to formula (21) i,i+1 : Among them, C and m represent the steel wire material parameters in the Paris formula, E(ΔK i ) represents the stress intensity factor ΔK during the crack depth extension from state i to the next state i The mean of .

6. The method according to claim 5, characterized in that In the calculation of q according to formula (21) i,i+1 Previously, it also included: Substituting δx as the crack depth increment into the Paris formula, we can obtain the crack growth rate during the period from state i to the next state: Among them, E(δN i ) represents the number of stress cycles δN that the steel wire endures during the period when the crack depth extends from state i to the next state i The mean of The probability of crack depth extension is described by geometric distribution, and the probability P(δN) of crack depth extending from state i to the next state after r stress cycles is expressed as i =r) is expressed as: P(δN i =r)=q i,i+1 ×(1-q i,i+1 ) r-1 (19), Then E(δN i )satisfy: Substituting equation (20) into equation (18), we obtain equation (21).

7. The method according to claim 1, characterized in that When the crack depth is at state i, the probability of extending to the next state after one stress cycle is (1-q i,i+1 ), the probability of not extending to the next state is q i,i+1 ; Accordingly, S1-3 includes: Using (1-q i,i+1 ) and q i,i+1 , generating the state transfer matrix Q in Markov theory c : Utilize Q c and the initial state of crack depth P c (0), determine the curve x(r) of crack depth x versus stress number r: x(r)=P c (0)×Q c r ×V c , Among them, V c represents the state value vector in Markov theory, and the increase in stress number r represents the passage of time.

8. The method according to claim 1, characterized in that The curve of crack depth changing with time is represented by a curve x(r) showing crack depth x changing with stress number r; Accordingly, S1-4 include: The critical crack depth x c Substituting x(r) as x, the obtained r is the remaining fatigue life of the steel wire at the current initial crack depth.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the Markov chain-based method for predicting the remaining fatigue life of a corroded steel wire according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the program is executed by a processor, the method for predicting the remaining fatigue life of a corroded steel wire based on a Markov chain according to any one of claims 1 to 8 is implemented.

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