Aviation Fastener Life Prediction Method and System Based on Stress Analysis

Abstract

The present invention provides a method and system for predicting the life of aviation fasteners based on stress analysis. First, a set of dynamic stress data of the target fastener in the service environment is collected. Then, the dynamic stress data is processed for three-dimensional stress field reconstruction to generate stress distribution characteristics including axial stress gradient, radial stress accumulation, and tangential stress fluctuation coefficient. Next, a pre-trained life prediction model is called to perform non-linear degradation analysis on the stress distribution characteristics to obtain the predicted remaining life value and the identification of the key failure area of the target fastener. Further, based on the correlation between the temperature fluctuation sequence and the material thermal expansion coefficient, the predicted remaining life value is compensated and corrected for the service environment. Finally, a set of fastener maintenance strategies including stress release path adjustment schemes and surface strengthening treatment schemes is generated according to the identification of the key failure area, realizing accurate prediction and effective maintenance of the life of aviation fasteners.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and more particularly, to a method and system for predicting the life of aviation fasteners based on stress analysis. Background Art

[0002] In the aviation field, the reliability and life of aviation fasteners are crucial for the safe operation of aircraft. With the rapid development of the aviation industry, aircraft are facing increasingly complex and harsh service environments, such as high temperature, high pressure, strong vibration, and frequent load changes. These factors greatly accelerate the fatigue damage and aging process of aviation fasteners, making it difficult to accurately predict their service life.

[0003] Currently, existing methods for predicting the life of aviation fasteners mainly rely on traditional empirical formulas or simple statistical analysis. These methods often only consider a limited number of influencing factors, such as load magnitude or cycle number, while ignoring the combined effects of other key factors in the service environment, such as temperature fluctuations and surface deformation. Due to the lack of a comprehensive analysis and accurate modeling of the stress state of fasteners in complex service environments, these methods are difficult to accurately capture the failure mechanisms and life degradation laws of fasteners, resulting in a large deviation between the predicted results and the actual life, and unable to meet the high-precision requirements of the aviation industry for fastener life prediction.

[0004] In addition, existing life prediction methods usually lack dynamic adaptability to the service environment and cannot correct the predicted results according to real-time environmental parameters. In practical applications, the service environment of aviation fasteners is constantly changing, and fluctuations in parameters such as temperature and load will significantly affect the stress state and life of fasteners. However, existing methods often ignore these dynamic changes, resulting in predicted results that cannot reflect the life of fasteners in the actual service environment.

[0005] In terms of maintenance strategies, existing methods are usually formulated based on fixed maintenance cycles or empirical judgments, lacking pertinence and flexibility. Since the critical failure areas and potential failure modes of fasteners cannot be accurately identified, these methods often cannot formulate effective maintenance strategies to extend the service life of fasteners, thereby increasing the maintenance cost and safety hazards of aircraft. Summary of the Invention

[0006] In view of the above-mentioned problems, in combination with the first aspect of the present invention, embodiments of the present invention provide a method for predicting the life of aviation fasteners based on stress analysis, the method comprising:

[0007] Collecting a set of dynamic stress data of a target fastener in a service environment, the set of dynamic stress data including a periodic load sequence, a temperature fluctuation sequence, and surface deformation monitoring data;

[0008] Perform three-dimensional stress field reconstruction processing on the dynamic stress data set to generate the stress distribution characteristics of the target fastener, where the stress distribution characteristics include axial stress gradient, radial stress accumulation, and tangential stress fluctuation coefficient;

[0009] Call the pre-trained life prediction model to perform non-linear degradation analysis processing on the stress distribution characteristics to generate the remaining life prediction value and critical failure area identification of the target fastener;

[0010] Perform service environment compensation and correction processing on the remaining life prediction value to generate a corrected remaining life prediction value, where the service environment compensation and correction processing is implemented based on the correlation between the temperature fluctuation sequence and the material thermal expansion coefficient;

[0011] Generate a fastener maintenance strategy set according to the critical failure area identification, where the fastener maintenance strategy set includes a stress release path adjustment plan and a surface strengthening treatment plan.

[0012] On the other hand, an embodiment of the present invention further provides an aircraft fastener life prediction system based on stress analysis, including a processor and a machine-readable storage medium. The machine-readable storage medium is connected to the processor. The machine-readable storage medium is used to store programs, instructions, or codes, and the processor is used to execute the programs, instructions, or codes in the machine-readable storage medium to implement the above method.

[0013] Based on the above aspects, the embodiment of the present invention first collects the dynamic stress data set of the target fastener in the service environment. On this basis, through three-dimensional stress field reconstruction processing, the stress distribution characteristics of the target fastener are accurately generated, including axial stress gradient, radial stress accumulation, and tangential stress fluctuation coefficient. Further, call the pre-trained life prediction model to perform non-linear degradation analysis processing on the stress distribution characteristics to generate the remaining life prediction value and critical failure area identification of the target fastener. This step not only realizes the quantitative prediction of the fastener life, but also accurately locates the potential failure area. Particularly importantly, this method also considers the influence of the service environment on the fastener life. By performing service environment compensation and correction processing on the remaining life prediction value, based on the correlation between the temperature fluctuation sequence and the material thermal expansion coefficient, the accuracy and reliability of the prediction result are effectively improved. Finally, according to the critical failure area identification, this method generates a fastener maintenance strategy set, including a stress release path adjustment plan and a surface strengthening treatment plan, which not only realizes the active prevention and control of the fastener failure risk, but also effectively extends the service life of the fastener through targeted maintenance measures, reducing the maintenance cost and safety hazards of the aircraft. Description of the Drawings

[0014] Figure 1It is a schematic flowchart of the execution process of the method for predicting the life of aviation fasteners based on stress analysis provided by an embodiment of the present invention.

[0015] Figure 2 It is a schematic diagram of exemplary hardware and software components of the system for predicting the life of aviation fasteners based on stress analysis provided by an embodiment of the present invention. Detailed implementation manners

[0016] The present invention will be specifically described below in conjunction with the accompanying drawings of the specification. Figure 1 It is a schematic flowchart of the method for predicting the life of aviation fasteners based on stress analysis provided by an embodiment of the present invention. The method for predicting the life of aviation fasteners based on stress analysis will be introduced in detail below.

[0017] Step S110: Collect a set of dynamic stress data of the target fastener in the service environment, where the set of dynamic stress data includes a periodic load sequence, a temperature fluctuation sequence, and surface deformation monitoring data.

[0018] In actual application scenarios such as aerospace, the service environment of the target fastener is very complex and changeable. Taking the target fastener at the connection part of the aircraft wing and fuselage as an example, it will be affected by various forces and environmental factors during the flight of the aircraft.

[0019] In order to collect the periodic load sequence, load sensors need to be reasonably arranged on the target fastener. These load sensors must have the characteristics of high precision, high reliability, and the ability to adapt to complex environments. For example, strain-type load sensors with good anti-interference ability are selected. During installation, it is necessary to ensure that the sensor is closely attached to the target fastener to accurately sense the load changes it bears. During different flight stages of the aircraft, such as takeoff, cruise, and landing, the load borne by the target fastener shows periodic changes. During the takeoff stage, due to the sudden increase in the acceleration and lift of the aircraft, the fastener will be subjected to large tensile and shear loads; during the cruise stage, the load is relatively stable but there will still be slight fluctuations; during the landing stage, it will be subjected to large impact loads. The sensor will collect data at preset time intervals, such as collecting data every millisecond, thus forming a periodic load sequence containing load values within multiple time periods. Each load value in this periodic load sequence corresponds to the actual load size borne by the target fastener at a specific moment, and these values are multi-dimensional, reflecting load components in different directions, such as axial load, radial load, and tangential load, etc.

[0020] For the acquisition of the temperature fluctuation sequence, a dedicated temperature sensor is used. Considering that the target fastener may be in different temperature environments, such as when the aircraft is flying at high altitude, the external temperature may be extremely low, while the fasteners near the engine will be affected by high temperature, so a sensor that can accurately measure within a wide temperature range should be selected. Temperature sensors can be installed on the surface, inside, and surrounding environment of the target fastener respectively to comprehensively obtain temperature information. During the flight of the aircraft, the external environmental temperature will change significantly with factors such as flight altitude and climate conditions. At the same time, the temperature of the target fastener itself will also fluctuate due to friction generated by the load it bears and heat conduction with the surrounding structure. The temperature sensor will continuously record these temperature changes. At regular intervals, such as recording the temperature value every two seconds, a temperature fluctuation sequence is formed. This temperature fluctuation sequence contains temperature data at different times, and these temperature data are also multi-dimensional, possibly including temperature values at different positions and information such as the rate of change of temperature over time.

[0021] Multiple methods can be used for the acquisition of surface deformation monitoring data, such as optical measurement method and strain gauge measurement method. The optical measurement method uses high-precision optical measurement equipment, such as a three-dimensional laser scanner, to scan the surface of the target fastener. During the scanning process, the laser beam irradiates the surface of the fastener, and the reflected light is received by the sensor. By analyzing the information of the reflected light, the three-dimensional shape and deformation information of the surface can be accurately obtained. The advantage of this method is that it can obtain large-area surface deformation data and will not cause damage to the target fastener. The strain gauge measurement method is to paste strain gauges on the key parts of the target fastener. A strain gauge is a sensitive element that can convert mechanical strain into an electrical signal. When the target fastener deforms, the strain gauge will generate corresponding strain, and its resistance value will also change. By measuring the change in resistance value and converting it according to the characteristic parameters of the strain gauge, the strain data of the surface can be obtained. During the flight of the aircraft, the target fastener will undergo surface deformation due to the action of various loads, such as tension, compression, and bending. By regularly collecting the data of the strain gauges, such as collecting once every half second, the surface deformation monitoring data can be obtained. These data are also multi-dimensional, including strain values at different positions and the change of strain over time, etc.

[0022] Step S120: Perform three-dimensional stress field reconstruction processing on the dynamic stress data set to generate the stress distribution characteristics of the target fastener, and the stress distribution characteristics include axial stress gradient, radial stress accumulation amount, and tangential stress fluctuation coefficient.

[0023] After obtaining the dynamic stress data set of the target fastener, in order to understand its stress distribution more deeply, these data need to be processed by three-dimensional stress field reconstruction.

[0024] Step S121: Divide the periodic load sequence into multiple load subsequences according to time windows, and each load subsequence corresponds to a stress acquisition period.

[0025] In actual operation, the determination of the time window needs to consider various factors. For the target fasteners during the flight of an aircraft, the time window can be divided according to the flight phases. For example, the time period from the takeoff of the aircraft to reaching the cruising altitude can be taken as one time window, the cruising phase as another time window, and the landing phase as the third time window, etc. Taking the takeoff phase as an example, during the time from when the aircraft starts to accelerate on the runway until it leaves the ground, the periodic load sequence is intercepted according to this time period to obtain a load subsequence. Each load subsequence contains the load change information borne by the target fastener within this stress acquisition period. This information is multi-dimensional and reflects the variation of load components in different directions over time. Through this division method, the stress state of the target fastener within each stress acquisition period can be analyzed more meticulously.

[0026] Step S122: For each of the load subsequences, perform the following processing:

[0027] Step S1221: Construct the three-dimensional deformation topology of the target fastener according to the surface deformation monitoring data. The three-dimensional deformation topology includes the spatial distribution data of the axial displacement field, the radial displacement field, and the tangential displacement field.

[0028] The surface deformation monitoring data contains the deformation information of each position on the surface of the target fastener. To construct the three-dimensional deformation topology, it is first necessary to preprocess this surface deformation monitoring data. Since the collected surface deformation monitoring data may contain noise and errors, a filtering algorithm needs to be used to filter the surface deformation monitoring data to remove the influence of noise and errors. For example, the median filtering algorithm can be used to process the deformation data of each acquisition point, and the median of the data within its neighborhood is taken as the effective deformation data of this point.

[0029] After removing noise and errors, calculate the spatial distribution data of the axial displacement field, radial displacement field, and tangential displacement field based on the surface deformation monitoring data. For the axial displacement field, by analyzing the deformation information along the axial direction of the target fastener in the surface deformation monitoring data, calculate the axial displacement values at different positions. Specifically, select a reference point, and based on the axial position of this reference point, calculate the axial displacement of other positions relative to the reference point. For each acquisition point, according to its deformation data in the axial direction and combining the relative position relationship between this point and the reference point, calculate the axial displacement value of this point. Summarize the axial displacement values of all acquisition points, and the spatial distribution data of the axial displacement field can be obtained. This spatial distribution data is multi-dimensional, including the axial displacement values at different positions and the variation of axial displacement with position.

[0030] Similarly, for the radial displacement field, calculate the radial displacement values at different positions according to the deformation information along the radial direction of the target fastener in the surface deformation monitoring data. During the calculation process, it is necessary to consider the geometric shape and structural characteristics of the target fastener to accurately determine the radial direction. By analyzing and calculating the radial deformation data of each acquisition point, the spatial distribution data of the radial displacement field is obtained.

[0031] For the tangential displacement field, calculate the tangential displacement values at different positions according to the deformation information along the tangential direction of the target fastener in the surface deformation monitoring data. The tangential direction refers to the direction tangent to the surface of the target fastener. When calculating the tangential displacement, factors such as the curvature and rotation of the surface need to be considered. By processing and calculating the tangential deformation data of each acquisition point, the spatial distribution data of the tangential displacement field is obtained.

[0032] Finally, integrate the spatial distribution data of the axial displacement field, radial displacement field, and tangential displacement field to construct the three-dimensional deformation topology structure of the target fastener. This three-dimensional deformation topology structure can intuitively reflect the deformation conditions of the target fastener at different positions.

[0033] Step S1222: Perform a coupling analysis and processing on the three-dimensional deformation topology structure and the load subsequence to generate the stress field reconstruction result for the current time window. The stress field reconstruction result includes the spatial distribution matrices of the axial stress component, radial stress component, and tangential stress component.

[0034] When performing the coupling analysis and processing, it is necessary to comprehensively consider the mutual relationship between the three-dimensional deformation topology structure and the load subsequence.

[0035] Step S12221: Based on the correspondence between the axial displacement field and the axial load component in the load subsequence, establish an axial displacement-load mapping equation, and obtain the first distribution function of the axial stress component by solving the axial displacement-load mapping equation.

[0036] First, analyze the correspondence between the axial displacement field and the axial load component in the load subsequence. When the target fastener bears an axial load, its axial displacement will change accordingly. Through research on a large amount of experimental data and theoretical analysis, it can be found that there is a certain functional relationship between the axial displacement and the axial load. Taking the target fastener connecting the aircraft wing and the fuselage as an example, when the wing is subjected to an upward lift force, the target fastener will bear an axial tensile load and at the same time generate an axial displacement. By collecting and analyzing the axial displacement and axial load data under different working conditions, the internal law between them can be found.

[0037] Based on this correspondence, establish an axial displacement-load mapping equation. This axial displacement-load mapping equation describes the mathematical relationship between the axial displacement and the axial load, which takes into account factors such as the material properties, geometric shape, and structure of the target fastener. When establishing the axial displacement-load mapping equation, it is necessary to collect the axial displacement and axial load data under different working conditions and use the method of data fitting to determine the parameters of the equation. For example, the least squares method can be used to fit the data to obtain an equation that can accurately describe the relationship between the axial displacement and the axial load.

[0038] By solving the axial displacement-load mapping equation, the first distribution function of the axial stress component can be obtained. This first distribution function represents the distribution of the axial stress component at different positions of the target fastener within the current time window. When solving the equation, it is necessary to substitute the known axial displacement field data into the equation and calculate the axial stress component values at different positions according to the equation-solving algorithm. These values constitute the first distribution function of the axial stress component, which is a multi-dimensional function reflecting the variation of the axial stress component with position.

[0039] Step S12222: Construct a radial contact stress calculation model according to the correlation characteristics between the radial displacement field and the contact surface pressure. The radial contact stress calculation model includes dynamic correction parameters of the material Poisson's ratio and elastic modulus.

[0040] The radial displacement field reflects the deformation of the target fastener in the radial direction, while the contact surface pressure is the pressure acting on the contact surface of the target fastener. Through research on the principles of material mechanics and analysis of experimental data, it can be found that there is a certain correlation characteristic between the radial displacement field and the contact surface pressure.

[0041] When constructing a radial contact stress calculation model, it is first necessary to consider the Poisson's ratio and elastic modulus of the material. Poisson's ratio refers to the ratio of the absolute value of the transverse normal strain to the axial normal strain when the material is uniaxially tensioned or compressed, which reflects the transverse deformation ability of the material. The elastic modulus refers to the ratio of stress to strain within the elastic deformation range of the material, which reflects the ability of the material to resist elastic deformation. These two parameters will change with factors such as the stress state and temperature of the material.

[0042] In practical applications, the stress state and temperature environment of the target fastener are constantly changing. Therefore, it is necessary to dynamically correct the Poisson's ratio and elastic modulus of the material. For example, in a high-temperature environment, the elastic modulus of the material will decrease and the Poisson's ratio will increase. By analyzing the experimental data of the material under different working conditions, a relationship model between the Poisson's ratio and elastic modulus of the material and factors such as temperature and stress is established. When calculating the radial contact stress, according to the current temperature and stress state, this relationship model is used to dynamically correct the Poisson's ratio and elastic modulus of the material.

[0043] Based on the correlation characteristics between the radial displacement field and the contact surface pressure, as well as the dynamically corrected Poisson's ratio and elastic modulus of the material, a radial contact stress calculation model is constructed. This radial contact stress calculation model can be established using the finite element analysis method or the analytical method. The finite element analysis method is to discretize the target fastener into multiple small elements, and by analyzing the mechanical properties of each element, the stress distribution of the entire fastener is obtained. The analytical method is to establish a mathematical equation to describe the relationship between the radial contact stress and factors such as the radial displacement field and the contact surface pressure. Through this radial contact stress calculation model, the radial contact stress values at different positions can be calculated, and these values constitute the distribution data of the radial contact stress.

[0044] Step S12223: Combine the spatial change rate of the tangential displacement field with the surface friction coefficient to establish an iterative calculation process for the tangential friction stress. The iterative calculation process includes a feedback correction mechanism for the displacement increment and the tangential stress increment.

[0045] The spatial change rate of the tangential displacement field reflects the deformation change of the target fastener in the tangential direction, while the surface friction coefficient is a parameter that describes the surface friction characteristics of the target fastener. When the target fastener is subjected to a tangential load, the tangential displacement field will change, and at the same time, tangential friction stress will be generated.

[0046] When establishing the iterative calculation process for the tangential friction stress, it is first necessary to determine the relationship between the spatial change rate of the tangential displacement field and the surface friction coefficient. Through the analysis of experimental data and theoretical research, it can be found that there is a certain functional relationship between the tangential friction stress and the spatial change rate of the tangential displacement field and the surface friction coefficient.

[0047] During the iterative calculation process, first calculate the spatial change rate of the tangential displacement field based on the initial tangential displacement field data, and then combine the surface friction coefficient to calculate the initial tangential friction stress value. Next, according to the calculation result of the tangential friction stress, correct the tangential displacement field to obtain new tangential displacement field data. Then calculate the spatial change rate of the tangential displacement field based on the new tangential displacement field data, and combine the surface friction coefficient to calculate the new tangential friction stress value. This process is continuously repeated until the tangential friction stress value converges to a stable value.

[0048] During the iterative calculation process, the feedback correction mechanism of the displacement increment and the tangential stress increment plays an important role. When the tangential stress changes, it will cause the tangential displacement of the target fastener to change, and the change in the tangential displacement will in turn affect the magnitude of the tangential stress. Through the feedback correction mechanism of the displacement increment and the tangential stress increment, the calculation result of the tangential stress can be adjusted in a timely manner to improve the calculation accuracy. For example, when the tangential stress increases, it will cause the tangential displacement to increase, and the tangential stress is corrected according to the displacement increment to make the calculation result of the tangential stress more in line with the actual situation.

[0049] Step S12224: Perform spatial interpolation and fusion processing on the output results of the first distribution function, the radial contact stress calculation model, and the tangential friction stress iterative calculation process to generate three-dimensional stress field distribution data including axial, radial, and tangential stress components.

[0050] After obtaining the output results of the first distribution function of the axial stress component, the radial contact stress calculation model, and the tangential friction stress iterative calculation process, it is necessary to perform spatial interpolation and fusion processing on these results.

[0051] Since these three results respectively describe the distribution of the axial, radial, and tangential stress components at different positions, but their spatial resolutions and ranges may be different. Therefore, it is necessary to perform spatial interpolation processing to unify them into the same spatial grid. Spatial interpolation refers to a method of estimating the values of other unknown points based on the values of known points. Methods such as linear interpolation and spline interpolation can be used for spatial interpolation. For example, for the first distribution function of the axial stress component, according to the stress values of its known points, the linear interpolation method is used to estimate the stress values at other positions to make its spatial resolution and range consistent with those of the radial contact stress and the tangential friction stress.

[0052] After completing the spatial interpolation process, the distribution data of the axial, radial, and tangential stress components are fused. The fusion method can adopt the weighted average method, and different weights are assigned according to the importance and reliability of different stress components. For example, for the axial stress component, since it has a greater impact on the load-bearing capacity of the target fastener, a higher weight can be assigned; for the radial stress component and the tangential stress component, appropriate weights can be assigned according to specific circumstances. Through the weighted average method, the distribution data of the axial, radial, and tangential stress components are fused into a three-dimensional stress field distribution data containing three stress components. This three-dimensional stress field distribution data can comprehensively reflect the stress distribution of the target fastener within the current time window.

[0053] Step S1223: Perform cumulative superposition processing on the stress field reconstruction results of consecutive multiple time windows, and calculate the axial stress gradient, the radial stress accumulation, and the tangential stress fluctuation coefficient; wherein, the axial stress gradient is the maximum change rate of the axial stress component along the length direction of the fastener, the radial stress accumulation is the integral of the radial stress component in the normal direction of the fastener contact surface, and the tangential stress fluctuation coefficient is the ratio of the standard deviation to the average value of the tangential stress component within a predetermined time interval.

[0054] After obtaining the stress field reconstruction results of each time window, in order to understand the stress change of the target fastener more comprehensively, it is necessary to perform cumulative superposition processing on the results of consecutive multiple time windows.

[0055] For the calculation of the axial stress gradient, first, the length direction of the target fastener needs to be determined. Taking the target fastener connecting the aircraft wing and the fuselage as an example, its length direction usually refers to the direction from one end of the wing to one end of the fuselage. In the stress field reconstruction results of each time window, extract the data of the axial stress component. Then, calculate the change rate of the axial stress component at different positions. The calculation method of the change rate is obtained by dividing the difference between the axial stress components at adjacent positions by the position difference. For example, for the axial stress component values at two adjacent positions, subtract the stress value at the previous position from the stress value at the latter position, and then divide by the distance between the two positions to obtain the axial stress change rate between these two positions.

[0056] Compare the axial stress change rates at all positions, and find the maximum change rate among them, which is the axial stress gradient. This axial stress gradient reflects the degree of change of the axial stress component along the length direction of the fastener. In the stress field reconstruction results of consecutive multiple time windows, calculate the axial stress gradient of each time window respectively, and then perform cumulative superposition on these gradients. The cumulative superposition method can adopt the summation method, adding the axial stress gradients of each time window to obtain the total axial stress gradient, which can more accurately reflect the change of the axial stress of the target fastener over a period of time.

[0057] For the calculation of radial stress accumulation, it is first necessary to determine the normal direction of the fastener contact surface. On the contact surface between the target fastener and other components, the normal direction refers to the direction perpendicular to the contact surface. In the stress field reconstruction results of each time window, the data of the radial stress component is extracted. Then, the radial stress component is integrated and calculated in the normal direction of the contact surface. The integration method can use the numerical integration method to divide the contact surface into multiple small areas, calculate the product of the radial stress component value of each area and the area of ​​the area, and then add the results of all areas to obtain the radial stress accumulation of the time window.

[0058] In the stress field reconstruction results of multiple consecutive time windows, the radial stress accumulation of each time window is calculated separately, and then these accumulations are cumulatively superimposed. The cumulative superposition method can also be used in a summation manner, adding the radial stress accumulation of each time window to obtain the total radial stress accumulation, which reflects the cumulative effect of radial stress over a period of time, which is of great significance for evaluating the contact fatigue damage of the target fastener.

[0059] For the calculation of the tangential stress fluctuation coefficient, it is first necessary to determine the predetermined time interval. The predetermined time interval can be set according to the actual situation. For example, the entire time period of an aircraft flight can be selected as the predetermined time interval. In the stress field reconstruction results of multiple consecutive time windows, the data of the tangential stress component are extracted. Then, the mean value and standard deviation of the tangential stress component in the predetermined time interval are calculated. The mean value is calculated by adding all the tangential stress component values ​​in the predetermined time interval and then dividing by the number of data. The standard deviation is calculated by first calculating the square of the difference between each tangential stress component value and the mean value, then adding these square values, dividing by the number of data, and finally taking the square root to obtain the standard deviation.

[0060] Divide the standard deviation by the mean to get the tangential stress fluctuation coefficient. The tangential stress fluctuation coefficient reflects the degree of fluctuation of the tangential stress component within a predetermined time interval, which is important for evaluating the surface wear of the target fastener. If the tangential stress fluctuation coefficient is large, it means that the tangential stress fluctuates violently, which may lead to increased wear on the surface of the target fastener. Because when the tangential stress fluctuates greatly, the friction force on the fastener surface will also change frequently, making the surface material more susceptible to wear.

[0061] Step S130: calling a pre-trained life prediction model to perform nonlinear degradation analysis on the stress distribution characteristics, and generating a remaining life prediction value and a critical failure area identifier of the target fastener.

[0062] After obtaining the stress distribution characteristics of the target fastener, the next step is to use the pre-trained life prediction model to deeply analyze these characteristics. The pre-trained life prediction model is trained based on a large amount of experimental data and actual operation data, and it can capture the complex non-linear relationship between the stress distribution characteristics, the life of the target fastener, and the failure area.

[0063] Step S131: Input the axial stress gradient into the first feature analysis layer of the life prediction model, and determine the distribution coordinates of the axial stress concentration area and the stress amplitude change curve through the stress concentration factor calculation module.

[0064] When the axial stress gradient is input into the first feature analysis layer of the life prediction model, the stress concentration factor calculation module in this first feature analysis layer will first analyze the axial stress gradient data in combination with the material properties of the target fastener, such as the elastic modulus and yield strength of the material, and its structural characteristics, such as geometric shape and dimensions. Taking a bolt connecting the aircraft wing and the fuselage as an example of the target fastener, the transition area between its head and the rod may cause stress concentration due to structural changes. The stress concentration factor calculation module will conduct a detailed analysis of the numerical values of the axial stress gradient at each position, and based on the principles of material mechanics and the laws summarized from a large amount of experimental data, determine which positions have more serious stress concentration.

[0065] When determining the distribution coordinates of the stress concentration area, refer to the three-dimensional model of the target fastener and the coordinate system used when collecting stress data. For example, according to the change of the axial stress gradient, the specific coordinate position of the stress concentration area in this coordinate system can be accurately located. For example, in a three-dimensional coordinate system established with the center of the bolt head as the origin, by analyzing the axial stress gradient, it is determined that there is a stress concentration area at a specific X, Y, Z coordinate position.

[0066] For the generation of the stress amplitude change curve, the module takes the length direction of the target fastener as the abscissa and the axial stress amplitude as the ordinate. It extracts the stress amplitude information at different positions from the axial stress gradient data, and then plots them in the coordinate system in sequence according to the position order. During the plotting process, considering the continuous change of the stress amplitude with the position, a suitable interpolation method is used to smooth the curve so that the curve can accurately reflect the change trend of the axial stress amplitude in the entire length direction of the fastener. The obtained stress amplitude change curve can visually show the stress amplitude size of the stress concentration area and its difference from the stress amplitude of the surrounding area.

[0067] Step S132: Input the radial stress accumulation amount into the second feature analysis layer of the life prediction model, perform contact fatigue damage accumulation calculation, and generate the prediction values of the fatigue crack initiation probability and propagation rate of the radial contact surface.

[0068] After inputting the radial stress accumulation amount into the second feature analysis layer of the life prediction model, the second feature analysis layer will perform contact fatigue damage accumulation calculation. Contact fatigue is one of the common failure forms of the target fastener during service, especially at the radial contact surface, where fatigue cracks are likely to occur due to repeated loading.

[0069] When performing contact fatigue damage accumulation calculation, the fatigue performance of the material of the target fastener is first considered. Different materials have different fatigue life curves, which describe the fatigue life of the material under different stress levels. By referring to the material handbook or conducting special fatigue tests, the fatigue life curve of the material used for the target fastener can be obtained. Then, combined with the radial stress accumulation amount data, according to the fatigue damage accumulation theory, the degree of fatigue damage that the radial contact surface has endured under the current stress state is calculated.

[0070] When calculating the probability of fatigue crack initiation, multiple factors are considered. In addition to the radial stress accumulation amount and the fatigue performance of the material, factors such as the surface roughness and microstructural organization of the target fastener are also considered. Areas with larger surface roughness are more likely to generate stress concentration, thus increasing the probability of fatigue crack initiation; uneven microstructural organization of the material may also lead to local stress concentration, affecting the initiation of fatigue cracks. By comprehensively considering these factors and using probability statistical methods, a fatigue crack initiation probability model is established to calculate the probability of fatigue crack initiation at each position of the radial contact surface.

[0071] For the calculation of the predicted value of the fatigue crack growth rate, existing fatigue crack growth theories are referred to. These theories describe the growth rate of fatigue cracks under different stress intensity factors. According to the radial stress accumulation amount data, the stress intensity factor under the current stress state is calculated, and then combined with the fatigue crack growth characteristic curve of the material, the growth rate of the fatigue crack is determined. During the calculation process, the influence of factors such as the geometric shape and size of the crack on the growth rate is also considered. For example, the growth rate of the crack will vary with different crack lengths and widths. Finally, the predicted values of the fatigue crack growth rate at each position of the radial contact surface are obtained.

[0072] Step S133: Input the tangential stress fluctuation coefficient into the third feature analysis layer of the life prediction model, and calculate the material wear thickness and surface roughness evolution data of the tangential friction surface based on the surface wear degradation model.

[0073] After inputting the tangential stress fluctuation coefficient into the third feature analysis layer of the life prediction model, the third feature analysis layer performs calculations based on the surface wear degradation model. Surface wear is a common problem of the target fastener under tangential load, which will affect the performance and service life of the fastener.

[0074] The surface wear degradation model comprehensively considers multiple factors, among which the tangential stress fluctuation coefficient is a key factor. The tangential stress fluctuation coefficient reflects the degree of fluctuation of the tangential stress. The greater the degree of fluctuation, the more frequent the change of the tangential friction force, resulting in more severe material wear. In addition, the model also considers factors such as the material hardness and surface friction coefficient of the target fastener. Areas with lower material hardness are more likely to be worn; when the surface friction coefficient is larger, the friction force will also increase, further accelerating the wear of the material.

[0075] When calculating the wear thickness of the material, the model first calculates the magnitude and change of the tangential friction force based on the tangential stress fluctuation coefficient and the surface friction coefficient. Then, combined with the wear characteristics of the material, such as the wear rate, the wear thickness of the material within a certain time is calculated. The wear rate refers to the wear amount of the material under the action of unit friction work, which is related to factors such as the type, hardness, and lubrication conditions of the material. By analyzing and summarizing a large amount of experimental data, the wear rate of the material used for the target fastener can be obtained. Based on the tangential friction force, wear rate, and time factor, the wear thickness of the material at each position of the tangential friction surface can be calculated.

[0076] For the calculation of the surface roughness evolution data, the influence of material wear on the surface roughness can be considered. During the material wear process, the microscopic unevenness of the surface will change, resulting in a change in the surface roughness. The model calculates the surface roughness at different time points based on the material wear thickness and the initial surface roughness. During the calculation process, the non-uniformity of wear is taken into account, that is, the wear degree at different positions may be different, resulting in different changes in the surface roughness. By analyzing the surface roughness evolution data, the change of the surface quality of the tangential friction surface can be understood, providing an important basis for evaluating the performance and life of the target fastener.

[0077] Step S134: Integrate the stress amplitude change curve, the fatigue crack initiation probability, and the material wear thickness to generate a comprehensive degradation index of the target fastener, and determine the remaining life prediction value according to the comparison result between the comprehensive degradation index and the preset life threshold.

[0078] After obtaining the data of the stress amplitude change curve, the fatigue crack initiation probability, and the material wear thickness, they need to be integrated to generate a comprehensive degradation index of the target fastener. The integration process needs to consider the influence degree of these three factors on the life of the target fastener.

[0079] First, the stress amplitude change curve, fatigue crack initiation probability and material wear thickness are normalized. The normalization process is to make the three data have the same magnitude and comparability. For example, the data range of the stress amplitude change curve may be different from the data range of the fatigue crack initiation probability and the material wear thickness. Through normalization, they can be unified into a suitable range. The normalization method can use linear transformation to map each data to an interval between 0 and 1.

[0080] Then, different weights are assigned to these three factors according to their importance to the life of the target fastener. The stress amplitude variation curve reflects the axial stress concentration and has an important impact on the load-bearing capacity of the fastener; the probability of fatigue crack initiation is directly related to whether the fastener will fail due to cracks; and the material wear thickness affects the surface performance and fit accuracy of the fastener. The weights of these three factors can be determined through expert experience, experimental data, or machine learning algorithms. For example, for some target fasteners that are subjected to high axial loads, the weight of the stress amplitude variation curve may be relatively large; while for some fasteners with higher surface accuracy requirements, the weight of the material wear thickness may be greater.

[0081] The normalized data is multiplied by the corresponding weights and then added to obtain the comprehensive degradation index of the target fastener. This comprehensive degradation index comprehensively considers the effects of multiple factors such as axial stress concentration, radial contact fatigue and tangential surface wear on the life of the fastener.

[0082] The preset life threshold is a standard value determined based on a large amount of experimental data and practical experience. It represents the comprehensive degradation index value of the target fastener when it reaches a critical state under normal use. When the comprehensive degradation index of the target fastener reaches or exceeds the threshold, it means that the performance of the fastener has declined to a dangerous level and its remaining life is about to end.

[0083] Compare the comprehensive degradation index of the target fastener with the preset life threshold. If the comprehensive degradation index is less than the preset life threshold, it means that the remaining life of the fastener is still relatively long; if the comprehensive degradation index is close to or equal to the preset life threshold, it means that the remaining life of the fastener is not much; if the comprehensive degradation index exceeds the preset life threshold, it means that the fastener has reached or exceeded its service life and needs to be replaced or repaired in time. Through this comparison result, the remaining life prediction value of the target fastener can be determined. For example, based on the difference between the comprehensive degradation index and the preset life threshold, combined with historical data and existing formulas, it is possible to estimate the time that the target fastener can still be used normally.

[0084] Step S135: Based on the spatial superposition result of the distribution coordinates, the predicted expansion rate, and the surface roughness evolution data, identify the geometric positions of the stress concentration areas, crack propagation paths, and high-wear-risk areas.

[0085] After obtaining the distribution coordinates of the stress concentration areas, the predicted fatigue crack propagation rate, and the surface roughness evolution data, perform spatial superposition processing on them. Spatial superposition refers to integrating these three data in the three-dimensional space of the target fastener to more intuitively analyze their mutual relationships.

[0086] First, mark the distribution coordinates of the stress concentration areas in the three-dimensional model of the target fastener. These coordinates clarify the specific positions of the stress concentration areas in the three-dimensional space. Then, based on the predicted fatigue crack propagation rate and combined with the initial position of the crack (which can be determined according to the fatigue crack initiation probability), simulate the crack propagation paths at different times. During the simulation, the influence of the stress concentration areas on crack propagation will be considered because the stress concentration areas will accelerate crack propagation. Mark the crack propagation paths in the three-dimensional model as well.

[0087] For the surface roughness evolution data, map it onto the surface of the target fastener in the form of colors or grayscales. Areas with larger surface roughness can be represented by darker colors or higher grayscales, and areas with smaller surface roughness can be represented by lighter colors or lower grayscales. In this way, the distribution of surface roughness can be visually seen in the three-dimensional model.

[0088] Through spatial superposition processing, the spatial relationships among the stress concentration areas, crack propagation paths, and high-wear-risk areas (i.e., areas with larger surface roughness) can be clearly seen. For example, the stress concentration areas may overlap with the crack propagation paths and high-wear-risk areas, indicating that the failure risks in these areas are higher. Based on the spatial superposition result, identify the geometric positions of the stress concentration areas, crack propagation paths, and high-wear-risk areas. Three-dimensional modeling software or specialized visualization tools can be used to highlight these areas with different colors or marks so that these key areas can be accurately located when formulating maintenance strategies later.

[0089] Step S140: Perform service environment compensation and correction processing on the predicted remaining life value to generate a corrected predicted remaining life value, and the service environment compensation and correction processing is realized based on the correlation between the temperature fluctuation sequence and the material thermal expansion coefficient.

[0090] After obtaining the predicted remaining life value of the target fastener, due to the influence of the service environment, this predicted remaining life value may need to be corrected. Temperature fluctuations in the service environment are an important factor, which will affect the stress state and life of the target fastener by influencing the coefficient of thermal expansion of the material.

[0091] Step S141: Extract the extreme temperature values and temperature change frequencies in the temperature fluctuation sequence, and calculate the dynamic adjustment amount of the coefficient of thermal expansion of the material with temperature change.

[0092] First, extract the extreme temperature values from the temperature fluctuation sequence. The extreme temperature values include the highest temperature value and the lowest temperature value. During the flight of an aircraft, the target fastener may experience different temperature environments. For example, the temperature is lower at high altitudes and higher near the engine. By comparing all the temperature values in the temperature fluctuation sequence, find the maximum and minimum values, which are the extreme temperature values.

[0093] The temperature change frequency refers to the number of temperature changes within a certain period of time. The temperature change frequency can be calculated by counting the number of turning points in the temperature fluctuation sequence. For example, within a certain time period, the points where the temperature changes from rising to falling or from falling to rising are the turning points. Count the number of these turning points and divide by the length of the time period to obtain the temperature change frequency.

[0094] The coefficient of thermal expansion of a material refers to the ability of the material to expand or contract when the temperature changes. It will change with the change of temperature. By referring to the material handbook or conducting special experiments, the relationship curve between the coefficient of thermal expansion of the material used for the target fastener and temperature can be obtained. According to the extracted extreme temperature values and temperature change frequencies, combined with this relationship curve, calculate the change of the coefficient of thermal expansion of the material at different temperatures. Then, according to the temperature change process, calculate the dynamic adjustment amount of the coefficient of thermal expansion with temperature change. For example, when the temperature rises from the lowest temperature to the highest temperature, the coefficient of thermal expansion of the material will increase accordingly. By comparing the coefficient of thermal expansion values at different temperatures, calculate the increase amount of the coefficient of thermal expansion, which is the dynamic adjustment amount.

[0095] Step S142: Perform thermal stress compensation calculation on the axial stress gradient according to the dynamic adjustment amount to generate a corrected axial stress gradient.

[0096] After obtaining the dynamic adjustment amount of the coefficient of thermal expansion of the material, it is necessary to perform thermal stress compensation calculation on the axial stress gradient. Thermal stress is the stress generated due to the expansion or contraction of the material caused by temperature change.

[0097] Step S1421: Obtain the initial coefficient of thermal expansion of the target fastener at the reference temperature and the dynamic adjustment amount, and establish a coefficient of thermal expansion - temperature correlation function.

[0098] First, determine the reference temperature of the target fastener. The reference temperature usually refers to the standard temperature adopted during material property testing or design. By referring to material data or experimental records, obtain the initial coefficient of thermal expansion of the target fastener at the reference temperature.

[0099] Combined with the dynamically adjusted amount of the coefficient of thermal expansion of the material calculated previously with respect to temperature change, establish a coefficient-of-thermal-expansion - temperature correlation function. This coefficient-of-thermal-expansion - temperature correlation function describes the relationship between the coefficient of thermal expansion and temperature. Methods such as polynomial fitting and linear regression can be used to determine the specific form and parameters of the function based on the known coefficient-of-thermal-expansion and temperature data. For example, by performing polynomial fitting on the coefficient-of-thermal-expansion data at multiple temperature points, a quadratic polynomial function can be obtained, which can accurately describe the variation of the coefficient of thermal expansion with temperature.

[0100] Step S1422: Calculate the axial thermal stress increment according to the coefficient-of-thermal-expansion - temperature correlation function. The axial thermal stress increment is the product of the temperature change amount and the change amount of the coefficient of thermal expansion.

[0101] After establishing the coefficient-of-thermal-expansion - temperature correlation function, calculate the temperature change amount according to the temperature change situation in the temperature fluctuation sequence. The temperature change amount refers to the increase or decrease value of the temperature within a certain time period. By comparing the starting temperature and the ending temperature of this time period, calculate the difference in temperature, which is the temperature change amount.

[0102] According to the coefficient-of-thermal-expansion - temperature correlation function, calculate the change amount of the coefficient of thermal expansion corresponding to the temperature change. The change amount of the coefficient of thermal expansion refers to the difference in the coefficient of thermal expansion before and after the temperature change. Multiply the temperature change amount by the change amount of the coefficient of thermal expansion to obtain the axial thermal stress increment. This axial thermal stress increment reflects the increase or decrease amount of the axial thermal stress generated due to temperature change.

[0103] Step S1423: Superimpose the axial thermal stress increment onto the calculation process of the axial stress gradient to generate an axial stress gradient correction value that includes the influence of thermal stress.

[0104] When calculating the axial stress gradient, it is usually based on the situation without considering thermal stress. Now, the axial thermal stress increment is superimposed on the calculation process of the axial stress gradient. When calculating the axial stress gradient, it is obtained by analyzing the change rate of the axial stress component at different positions. The axial thermal stress increment is superimposed on the axial stress component at the corresponding position according to its distribution in the target fastener. Then, the change rate of the axial stress component at different positions is recalculated to obtain the corrected value of the axial stress gradient including the influence of thermal stress. This corrected value of the axial stress gradient more accurately reflects the axial stress gradient of the target fastener considering thermal stress.

[0105] Step S1424: Perform stress relaxation effect compensation processing on the corrected value of the axial stress gradient, and the stress relaxation effect compensation processing is achieved based on the product factor of the material stress relaxation curve and the temperature holding time.

[0106] Stress relaxation refers to the phenomenon that the stress of a material gradually decreases with time under constant strain. In a high-temperature environment, stress relaxation may occur in the target fastener, which will affect the actual situation of the axial stress gradient.

[0107] First, obtain the stress relaxation curve of the material used for the target fastener. The stress relaxation curve describes the stress relaxation characteristics of the material at different temperatures and times. This curve can be obtained by referring to a material handbook or conducting specialized experiments.

[0108] Determine the temperature holding time according to the temperature fluctuation sequence. The temperature holding time refers to the time that the target fastener persists at a certain temperature. Combine the stress relaxation curve with the temperature holding time to obtain a product factor. This product factor reflects the degree of stress relaxation of the material under the current temperature and time conditions.

[0109] Multiply the corrected value of the axial stress gradient by this product factor to perform stress relaxation effect compensation processing on the corrected value of the axial stress gradient. After the compensation processing, the final corrected value of the axial stress gradient is obtained, and this corrected value of the axial stress gradient more accurately reflects the axial stress gradient of the target fastener considering thermal stress and stress relaxation effects.

[0110] Step S143: Based on the correlation between the temperature change frequency and the material creep characteristics, perform creep damage correction processing on the radial stress accumulation amount to generate a corrected radial stress accumulation amount.

[0111] Material creep refers to the phenomenon that a material will undergo slow plastic deformation under long-term constant load. The temperature change frequency will affect the creep characteristics of the material, thereby affecting the radial stress accumulation amount of the target fastener.

[0112] First, study the creep characteristics of the materials used for the target fasteners. By consulting material manuals, conducting creep tests, etc., understand the creep behavior of the materials at different temperature and stress levels, and obtain the creep curves of the materials. The creep curves describe the change of the strain of the materials over time.

[0113] According to the temperature change frequency in the temperature fluctuation sequence and in combination with the creep curves of the materials, analyze the influence of the temperature change frequency on the creep of the materials. When the temperature change frequency is relatively high, the creep process of the materials may be inhibited; while when the temperature change frequency is relatively low, the materials may have more time for creep deformation.

[0114] Based on the correlation between the temperature change frequency and the creep characteristics of the materials, correct the radial stress accumulation. When calculating the radial stress accumulation, it is usually carried out based on the situation without considering creep. Now considering the influence of creep, calculate the change amount of the radial stress caused by creep according to the temperature change frequency and the creep characteristics of the materials. Superimpose this change amount of the radial stress on the original radial stress accumulation to obtain the corrected radial stress accumulation. This corrected radial stress accumulation more accurately reflects the radial stress accumulation of the target fasteners under the condition of considering the temperature change frequency and the creep characteristics of the materials. Since creep will cause plastic deformation of the materials, thereby changing the stress distribution inside the fasteners, the corrected radial stress accumulation is of more important significance for evaluating the contact fatigue damage and life of the target fasteners.

[0115] Step S144: According to the material hardness change data at extreme temperatures, perform surface strength adaptation adjustment processing on the tangential stress fluctuation coefficient to generate a corrected tangential stress fluctuation coefficient.

[0116] Extreme temperatures will have a significant impact on the hardness of the materials, and the change of the material hardness will affect the surface strength and friction characteristics of the tangential friction surface of the target fasteners, and further affect the tangential stress fluctuation coefficient.

[0117] First, obtain the material hardness change data of the materials used for the target fasteners at extreme temperatures through experiments or by consulting relevant materials. For example, in a high-temperature environment, the thermal motion of atoms in the materials intensifies, and the binding force between atoms weakens, resulting in a decrease in the material hardness; while in a low-temperature environment, the crystal lattice structure of the materials may change, which will also cause a change in the hardness. The hardness of the material samples can be tested under different extreme temperature conditions, and the corresponding hardness values can be recorded to form a data set of the material hardness changing with temperature.

[0118] Then, analyze the influence of the change in material hardness on the surface strength and friction characteristics of the tangential friction surface. When the material hardness decreases, plastic deformation is more likely to occur on the tangential friction surface, and the surface roughness may increase, resulting in an increase in the fluctuation of the tangential friction force; conversely, when the material hardness increases, the wear resistance of the tangential friction surface is enhanced, and the fluctuation of the tangential friction force may decrease. According to the relationship between the change in material hardness and the fluctuation of the tangential friction force, establish a surface strength adaptation adjustment model.

[0119] In this surface strength adaptation adjustment model, correlate the tangential stress fluctuation coefficient with the material hardness change data. Adjust the original tangential stress fluctuation coefficient according to the material hardness value corresponding to the current extreme temperature. If the material hardness decreases, it indicates that the surface strength weakens, and the tangential stress fluctuation coefficient may need to be increased; if the material hardness increases, the surface strength is enhanced, and the tangential stress fluctuation coefficient may need to be decreased. Through this adjustment, the tangential stress fluctuation coefficient can more accurately reflect the actual stress fluctuation of the tangential friction surface of the target fastener under extreme temperatures, and obtain the corrected tangential stress fluctuation coefficient.

[0120] Step S145: Input the corrected axial stress gradient, radial stress accumulation, and tangential stress fluctuation coefficient into the life prediction model for recalculation to generate a remaining life prediction value after compensating for environmental factors.

[0121] After obtaining the corrected axial stress gradient, radial stress accumulation, and tangential stress fluctuation coefficient, input these corrected stress distribution characteristics into the pre-trained life prediction model again.

[0122] The life prediction model will process these corrected stress distribution characteristics according to the previous analysis process. In the first feature analysis layer, according to the corrected axial stress gradient, re-determine the distribution coordinates of the axial stress concentration area and the stress amplitude change curve through the stress concentration factor calculation module. Since the axial stress gradient has considered the influence of thermal stress and stress relaxation effects, the newly obtained stress concentration area and stress amplitude change curve will more accurately reflect the actual situation.

[0123] In the second feature analysis layer, perform contact fatigue damage accumulation calculation based on the corrected radial stress accumulation, and re-generate the fatigue crack initiation probability and propagation rate prediction values of the radial contact surface. After considering the influence of temperature change frequency and material creep characteristics on the radial stress accumulation, the fatigue crack initiation probability and propagation rate prediction values will be more in line with the actual service environment.

[0124] In the third feature analysis layer, based on the corrected tangential stress fluctuation coefficient, the material wear thickness of the tangential friction surface and the evolution data of the surface roughness are recalculated based on the surface wear degradation model. Since the tangential stress fluctuation coefficient has been adjusted according to the material hardness change under extreme temperatures, the newly calculated material wear thickness and the evolution data of the surface roughness will more accurately reflect the actual wear condition of the tangential friction surface.

[0125] Finally, fuse the recalculated stress amplitude change curve, fatigue crack initiation probability, and material wear thickness to generate a new comprehensive degradation index. Compare this new comprehensive degradation index with the preset life threshold, and determine the remaining life prediction value after compensating for environmental factors according to the comparison result. This remaining life prediction value takes into account the influence of temperature fluctuations in the service environment on the stress state and material properties of the target fastener, and is more accurate and reliable than the previously uncorrected remaining life prediction value.

[0126] Step S150: Generate a set of fastener maintenance strategies according to the key failure area identification of the fastener, and the set of fastener maintenance strategies includes a stress release path adjustment plan and a surface strengthening treatment plan.

[0127] After determining the key failure area identification of the target fastener, corresponding maintenance strategies need to be formulated based on these identifications to extend the service life of the target fastener and ensure its safe and reliable operation.

[0128] Step S151: Calculate the optimal stress release path for the identification of the stress concentration area, and the optimal stress release path is achieved by adjusting the load distribution ratio of adjacent fasteners.

[0129] After obtaining the identification of the stress concentration area, the optimal stress release path needs to be calculated. First, the mechanical relationship between the target fastener and adjacent fasteners needs to be analyzed. Adjacent fasteners are interrelated in the structure and jointly bear the external load. When stress concentration occurs in a certain area of the target fastener, part of the stress can be transferred from the stress concentration area by adjusting the load distribution ratio of adjacent fasteners, so as to achieve the purpose of stress release.

[0130] To calculate the optimal stress release path, a mechanical model needs to be established to describe the stress conditions of the target fastener and adjacent fasteners. This mechanical model will take into account factors such as the material properties, geometric shape, connection method of the fasteners, and the distribution of external loads. By using finite element analysis software or theoretical calculation methods, the mechanical model is solved to obtain the stress distribution of the target fastener and adjacent fasteners under different load distribution ratios.

[0131] Then, based on the location and stress amplitude of the stress concentration area, an optimization goal is set. The optimization goal can be to reduce the stress amplitude in the stress concentration area to a safe range, or to make the stress distribution of the entire target fastener more uniform. By continuously adjusting the load distribution ratio of adjacent fasteners, the mechanical model is calculated multiple times, and the stress distribution results under different schemes are compared to find the optimal load distribution ratio that meets the optimization goal.

[0132] According to the optimal load distribution ratio, the optimal stress release path is determined. The optimal stress release path describes how to guide the stress from the stress concentration area to other areas by adjusting the pre-tightening force or connection method of adjacent fasteners, etc., to achieve effective stress release. For example, by increasing the pre-tightening force of a certain adjacent fastener, it can bear more load, thereby reducing the burden on the stress concentration area of the target fastener.

[0133] Step S152: According to the identifier of the crack propagation path, a surface strengthening treatment plan is constructed, and the surface strengthening treatment plan includes the selection of the laser shock strengthening area and the optimal configuration of the shock energy parameters.

[0134] To construct a surface strengthening treatment plan based on the identifier of the crack propagation path, the geometric features of the crack propagation path need to be extracted first.

[0135] Step S1521: Extract the geometric features of the crack propagation path, and calculate the path curvature radius and the propagation direction angle.

[0136] By analyzing the identifier data of the crack propagation path, its geometric features are extracted. The path curvature radius reflects the degree of bending of the crack propagation path. The path curvature radius can be obtained by selecting multiple points on the crack propagation path and calculating the slope change between adjacent points. For example, select a point at a certain distance interval on the crack propagation path, calculate the slope of the line connecting adjacent two points, and then calculate the curvature radius according to the slope change. The propagation direction angle represents the direction of crack propagation. The propagation direction angle can be obtained by calculating the angle between the tangent direction of a certain point on the crack propagation path and a reference direction (such as the axis of the target fastener).

[0137] Step S1522: Select the coverage density of the laser shock spots according to the curvature radius, and the coverage density is inversely proportional to the curvature radius.

[0138] Laser shock peening is a technique that generates shock waves on the material surface through high-energy laser pulses, thereby increasing the surface hardness and residual compressive stress of the material. When selecting the coverage density of laser shock spots, the curvature radius of the crack propagation path needs to be considered. The smaller the curvature radius, the greater the degree of bending of the crack propagation path, and the more serious the stress concentration in this area may be, requiring a higher coverage density of laser shock spots to strengthen the surface. On the contrary, the larger the curvature radius, the relatively gentler the crack propagation path, and the coverage density of laser shock spots can be appropriately reduced. By establishing an inverse relationship model between the coverage density and the curvature radius, the appropriate coverage density of laser shock spots is determined according to the calculated curvature radius. For example, if the curvature radius is small, increase the number of laser shock spots per unit area; if the curvature radius is large, reduce the number of laser shock spots.

[0139] Step S1523: Adjust the application direction of the laser shock energy based on the expansion direction angle so that the shock energy direction forms a predetermined angle with the crack propagation direction.

[0140] In order to more effectively suppress crack propagation, it is necessary to adjust the application direction of the laser shock energy according to the expansion direction angle. The selection of the predetermined angle is based on experimental and theoretical research. Generally speaking, making the shock energy direction form a suitable angle with the crack propagation direction can maximize the residual compressive stress on the material surface, thereby hindering the further propagation of cracks. By adjusting the angle of the laser shock equipment, the application direction of the laser shock energy is made to meet the requirements of the predetermined angle with the crack propagation direction. For example, if the crack propagation direction is inclined, the angle of the laser shock equipment is adjusted accordingly so that the laser beam irradiates the material surface at a suitable angle.

[0141] Step S1524: Dynamically adjust the laser shock pulse duration according to the material surface hardness test data to ensure that the shock energy is below the critical value of the material yield strength.

[0142] When performing laser shock peening, it is necessary to dynamically adjust the laser shock pulse duration according to the material surface hardness test data. Different material surface hardnesses have different abilities to withstand laser shock energy. By testing the surface hardness of the target fastener material, the hardness data at different positions are obtained. If the material surface hardness is low, it indicates that its yield strength may also be low, and the laser shock pulse duration needs to be shortened to avoid excessive shock energy causing plastic deformation or even damage to the material; if the material surface hardness is high, the laser shock pulse duration can be appropriately extended to improve the strengthening effect. By establishing a relationship model between the laser shock pulse duration and the material surface hardness, the laser shock pulse duration is dynamically adjusted according to the actual hardness test data to ensure that the shock energy is always below the critical value of the material yield strength.

[0143] Step S1525: Generate a reinforcement parameter configuration table including coverage density, impact direction, and pulse duration.

[0144] Sort out parameters such as the laser shock spot coverage density determined according to the geometric characteristics of the crack propagation path, the laser shock energy application direction adjusted according to the expansion direction angle, and the laser shock pulse duration dynamically adjusted according to the material surface hardness test data to generate a reinforcement parameter configuration table. This reinforcement parameter configuration table details the parameters required for laser shock peening at different positions, providing accurate guidance for actual surface strengthening treatment operations. Operators can, based on this reinforcement parameter configuration table, accurately set the parameters of the laser shock equipment in the area related to the crack propagation path of the target fastener for effective surface strengthening treatment.

[0145] Step S153: Based on the identification of the high wear-risk area, generate a lubricant coating strategy, and the lubricant coating strategy dynamically adjusts the coating frequency and coating thickness according to the wear rate prediction value.

[0146] Generate a lubricant coating strategy based on the identification of the high wear-risk area. First, predict the wear rate of the high wear-risk area according to the material wear thickness and surface roughness evolution data of the tangential friction surface calculated previously.

[0147] Then, dynamically adjust the coating frequency of the lubricant according to the wear rate prediction value. If the wear rate is high, it indicates that the wear situation in this area is relatively serious, and the coating frequency of the lubricant needs to be increased to supplement the lubricant in a timely manner and reduce friction and wear. For example, when the wear rate exceeds a certain threshold, the coating frequency is increased from coating once every certain operating time to coating at a shorter time interval. On the contrary, if the wear rate is low, the coating frequency can be appropriately reduced to save the lubricant cost.

[0148] At the same time, adjust the coating thickness of the lubricant according to the wear rate prediction value. Areas with a high wear rate require a thicker lubricant layer to provide better lubrication and protection; while areas with a low wear rate can have an appropriately reduced coating thickness. By establishing a relationship model between the coating thickness and the wear rate, determine the appropriate coating thickness according to the actual wear rate prediction value.

[0149] In addition, the characteristics of the lubricant and the working environment of the target fastener also need to be considered. Different types of lubricants have different performance characteristics, such as lubrication performance, high-temperature resistance performance, corrosion resistance, etc. Select a suitable lubricant according to the working environment of the target fastener, such as temperature, humidity, chemical media, etc. For example, in a high-temperature environment, a lubricant with good high-temperature resistance performance needs to be selected; in an environment with chemical corrosion media, a lubricant with corrosion resistance performance needs to be selected.

[0150] The finally formed lubricant coating strategy will specify information such as the coating frequency, coating thickness, and type of lubricant used under different wear rates in high-risk wear areas to ensure effective reduction of wear in high-risk wear areas and extension of the service life of the target fastener.

[0151] Step S154: Prioritize the optimal stress release path, the surface strengthening treatment plan, and the lubricant coating strategy to generate a set of maintenance strategies including the execution timing and implementation parameters.

[0152] After obtaining the optimal stress release path, the surface strengthening treatment plan, and the lubricant coating strategy, it is necessary to prioritize them. The prioritization needs to consider multiple factors, such as the failure risk degree of the target fastener, the difficulty of maintenance operations, and the maintenance cost.

[0153] For areas and situations with a higher failure risk, the corresponding maintenance strategies should have a higher priority. For example, if the stress amplitude in the stress concentration area has approached the yield strength of the material, then the strategy of adjusting the stress release path should be executed first to avoid serious failure accidents. For high-risk wear areas, if the wear rate is relatively low, the priority of the lubricant coating strategy can be appropriately reduced.

[0154] At the same time, the difficulty of maintenance operations is also an important consideration. If the implementation of a certain maintenance strategy requires complex equipment and technologies, or requires a long downtime, then its priority may need to be adjusted according to the actual situation. For example, laser shock peening in the surface strengthening treatment plan requires professional laser equipment and technicians, and the operation is relatively complex, and it may need to be arranged at an appropriate time.

[0155] The maintenance cost is also an important basis for prioritization. Some maintenance strategies may require higher costs, such as using special lubricants or conducting large-scale surface strengthening treatments. When prioritizing, it is necessary to comprehensively consider the cost-benefit to ensure that the maintenance cost is minimized while ensuring the maintenance effect.

[0156] According to the prioritization results, determine the execution timing of the maintenance strategies. Arrange the maintenance strategies with higher priority to be executed first, and those with lower priority to be executed later. At the same time, clarify the implementation parameters of each maintenance strategy. For the optimal stress release path, clarify the load distribution adjustment amount and adjustment method of adjacent fasteners; for the surface strengthening treatment plan, detail the selection of the laser shock peening area, the configuration of shock energy parameters, etc.; for the lubricant coating strategy, determine the coating frequency, coating thickness, and lubricant type, etc.

[0157] The finally generated set of maintenance strategies includes the optimal stress release path, the surface strengthening treatment plan, the execution timing of the lubricant coating strategy, and detailed implementation parameters, providing comprehensive and systematic guidance for the maintenance work of the target fastener, helping to improve the maintenance efficiency and effect, and ensuring the safe and reliable operation of the target fastener.

[0158] Step S210: Collect the actual wear amount and crack propagation length of the target fastener within a preset verification period.

[0159] To verify the accuracy of the life prediction model and the effectiveness of the maintenance strategy, it is necessary to collect actual data of the target fastener within a preset verification period. The preset verification period can be determined according to the usage and design life of the target fastener. For example, for some critical aviation fasteners, a certain number of flights or a certain operating time can be set as a verification period.

[0160] For the collection of the actual wear amount, various methods can be used. One method is to directly measure the dimensional changes of the tangential friction surface of the target fastener using measuring tools. At the beginning of the verification period, use high-precision calipers, micrometers and other measuring tools to measure the key dimensions of the tangential friction surface, such as diameter, thickness, etc. At the end of the verification period, measure these dimensions again, and obtain the actual wear amount by comparing the difference between the two measurement results. Another method is to use surface topography measurement techniques, such as three-dimensional laser scanning measurement. Scan the tangential friction surface before and after the verification period respectively to obtain surface topography data, and calculate the actual wear amount by analyzing the differences between the two scan data.

[0161] For the collection of the crack propagation length, non-destructive testing techniques can be used. For example, the ultrasonic testing method can be adopted. By emitting ultrasonic waves to the target fastener and based on the propagation characteristics and reflection conditions of ultrasonic waves in the material, detect whether there are cracks and the location and length of the cracks. Conduct tests at the beginning and end of the verification period respectively, and compare the two test results to obtain the crack propagation length. Magnetic particle testing, penetrant testing and other methods can also be used, and these methods have high sensitivity for detecting surface cracks. By accurately collecting the actual wear amount and crack propagation length data, it provides a reliable basis for subsequent error analysis and model optimization.

[0162] Step S220: Conduct deviation analysis on the actual wear amount and the predicted wear thickness to generate a first error correction coefficient.

[0163] After obtaining the actual wear amount of the target fastener and the predicted wear thickness calculated by the previous life prediction model, deviation analysis is performed. First, calculate the difference between the actual wear amount and the predicted wear thickness. If the actual wear amount is greater than the predicted wear thickness, it indicates that the predicted value is on the low side and there may be a certain error; if the actual wear amount is less than the predicted wear thickness, it indicates that the predicted value is on the high side.

[0164] Then, according to the magnitude and direction of the difference, a first error correction coefficient is generated. The correction coefficient can be generated in the form of a proportional coefficient. For example, if the proportion of the difference between the actual wear amount and the predicted wear thickness to the predicted wear thickness is large, it indicates a large error, and the first error correction coefficient will be adjusted accordingly to a larger value to make a greater correction to the predicted wear thickness in subsequent model calculations. The specific proportional relationship can be determined based on a large amount of experimental data and experience. Through this deviation analysis and the generation of the first error correction coefficient, the prediction result of the wear thickness in the life prediction model can be adjusted to improve the accuracy of the model.

[0165] Step S230: Perform a time-domain comparison process on the crack propagation length and the predicted propagation rate to generate a second error correction coefficient.

[0166] Perform a time-domain comparison process on the actually collected crack propagation length and the propagation rate predicted by the life prediction model. First, calculate the theoretically predicted crack propagation length within the preset verification period according to the predicted propagation rate and the time length of the preset verification period. Then, compare the actual crack propagation length with the theoretical crack propagation length and analyze the difference between the two.

[0167] If the actual crack propagation length is greater than the theoretical crack propagation length, it indicates that the predicted propagation rate is on the low side and there may be an error; if the actual crack propagation length is less than the theoretical crack propagation length, it indicates that the predicted propagation rate is on the high side. According to the difference between the two, a second error correction coefficient is generated. Similarly, the proportional coefficient method can be used to determine the magnitude of the second error correction coefficient according to the proportion of the difference between the actual crack propagation length and the theoretical crack propagation length to the theoretical crack propagation length. This correction coefficient will be used to adjust the prediction result of the crack propagation rate in the life prediction model so that the model can more accurately predict the crack propagation situation.

[0168] Step S240: Adjust the weight parameters of the life prediction model according to the first error correction coefficient and the second error correction coefficient to generate an optimized life prediction model.

[0169] After obtaining the first error correction coefficient and the second error correction coefficient, the weight parameters of the life prediction model need to be adjusted according to these two correction coefficients. The weight parameters in the life prediction model determine the influence degree of each input feature on the output result.

[0170] First, analyze the relationships between the first error correction coefficient and the second error correction coefficient and the model weight parameters. For example, if the first error correction coefficient indicates a large prediction error in the wear thickness, then the weight parameters of the input features related to the wear thickness may need to be adjusted. A step-by-step adjustment method can be adopted to appropriately increase or decrease the weight parameters according to the magnitude and direction of the correction coefficient.

[0171] When adjusting the weight parameters, the overall stability and accuracy of the model should be considered. One should not over-adjust based on just one correction coefficient, so as not to affect the model's ability to process other input features. Through multiple experiments and validations, a suitable weight parameter adjustment scheme can be found such that the model can achieve better prediction results when considering factors such as wear thickness and crack growth rate. After each adjustment of the weight parameters, use a set of validation data to validate the model and observe the degree of agreement between the predicted remaining life values, fatigue crack initiation probabilities, material wear thickness, etc. output by the model and the actual situation. If the prediction accuracy of the model improves after adjustment, then continue to fine-tune according to the new error correction coefficient; if the accuracy decreases, then the adjustment strategy needs to be re-adjusted, such as reducing the adjustment amplitude or changing the adjustment direction.

[0172] After multiple adjustments and validations, a set of optimal weight parameters is found such that the prediction errors of the model for the actual wear amount and crack growth length can both be controlled within a small range. When this set of optimal weight parameters is determined, an optimized life prediction model is generated. This optimized model comprehensively considers the previous error information and can more accurately reflect the performance degradation of the target fastener in the actual service environment, thereby providing a more reliable basis for the life prediction of the target fastener.

[0173] Step S250: Apply the optimized life prediction model to the life prediction tasks of subsequent batches of fasteners.

[0174] After obtaining the optimized life prediction model, apply it to the life prediction tasks of subsequent batches of target fasteners. First, for the target fasteners in subsequent batches, collect their dynamic stress data sets in the service environment in the same way as before, including periodic load sequences, temperature fluctuation sequences, and surface deformation monitoring data. Ensure the accuracy and integrity of the data during the collection process, using the same high-precision sensors and reliable data collection methods as before.

[0175] Then, perform three-dimensional stress field reconstruction processing on the collected dynamic stress data set. According to the steps described in detail before, divide the periodic load sequence into multiple load subsequences according to time windows. For each load subsequence, construct the three-dimensional deformation topology structure of the target fastener based on the surface deformation monitoring data, perform coupling analysis processing on the three-dimensional deformation topology structure and the load subsequence, generate the stress field reconstruction result of the current time window, and then perform cumulative superposition processing on the stress field reconstruction results of multiple consecutive time windows to calculate stress distribution characteristics such as axial stress gradient, radial stress accumulation, and tangential stress fluctuation coefficient.

[0176] Next, input these stress distribution characteristics into the optimized life prediction model. The model will perform non-linear degradation analysis processing on these input stress distribution characteristics. In the first feature analysis layer, according to the input axial stress gradient, determine the distribution coordinates of the axial stress concentration area and the stress amplitude change curve through the stress concentration factor calculation module; in the second feature analysis layer, perform contact fatigue damage accumulation calculation based on the radial stress accumulation to generate the fatigue crack initiation probability and propagation rate prediction values of the radial contact surface; in the third feature analysis layer, based on the tangential stress fluctuation coefficient, calculate the material wear thickness and surface roughness evolution data of the tangential friction surface based on the surface wear degradation model.

[0177] After that, fuse the stress amplitude change curve, fatigue crack initiation probability, and material wear thickness to generate the comprehensive degradation index of the target fasteners in the subsequent batches. Compare this comprehensive degradation index with the preset life threshold, and determine the remaining life prediction value according to the comparison result. At the same time, based on the spatial superposition result of the distribution coordinates of the stress concentration area, the fatigue crack propagation rate prediction value, and the surface roughness evolution data, identify the geometric positions of the stress concentration area, crack propagation path, and wear high-risk area of the target fasteners in the subsequent batches.

[0178] Then, perform service environment compensation and correction processing on the remaining life prediction value. According to the previous method, extract the extreme temperature values and temperature change frequencies in the temperature fluctuation sequence, calculate the dynamic adjustment amount of the material thermal expansion coefficient with temperature change, perform thermal stress compensation calculation and stress relaxation effect compensation processing on the axial stress gradient, perform creep damage correction processing on the radial stress accumulation based on the correlation between temperature change frequency and material creep characteristics, perform surface strength adaptation adjustment processing on the tangential stress fluctuation coefficient according to the material hardness change data under extreme temperature, and finally input the corrected axial stress gradient, radial stress accumulation, and tangential stress fluctuation coefficient into the optimized life prediction model for recalculation to generate the remaining life prediction value after compensating for environmental factors.

[0179] Finally, a set of maintenance strategies for the target fasteners of the subsequent batches is generated based on the identification of critical failure regions. For the identification of stress concentration regions, the optimal stress release path is calculated, and stress release is achieved by adjusting the load distribution ratio of adjacent fasteners; according to the identification of crack propagation paths, a surface strengthening treatment plan is constructed, including the selection of laser shock strengthening regions and the optimal configuration of shock energy parameters; based on the identification of high-risk wear regions, a lubricant coating strategy is generated, and the coating frequency and coating thickness are dynamically adjusted according to the predicted wear rate; the optimal stress release path, surface strengthening treatment plan, and lubricant coating strategy are sorted according to priority to generate a set of maintenance strategies including execution time sequences and implementation parameters.

[0180] By applying the optimized life prediction model to the life prediction tasks of the target fasteners of the subsequent batches, the remaining lives of these fasteners can be predicted more accurately, critical failure regions can be detected in a timely manner, and reasonable and effective maintenance strategies can be formulated. This can improve the use safety and reliability of the target fasteners, reduce the accident risks caused by fastener failures, and at the same time optimize the maintenance plan, reducing unnecessary maintenance costs and downtime. During the application process, the actual data of the target fasteners of the subsequent batches can also be continuously collected to further verify and optimize the optimized model, forming an ever-improving cycle, making the prediction accuracy of the model and the effectiveness of the maintenance strategies continuously improve.

[0181] Figure 2 FIG. shows a schematic diagram of exemplary hardware and software components of an aircraft fastener life prediction system 100 based on stress analysis that can implement the ideas of the present application provided by some embodiments of the present application. For example, the processor 120 can be used on the aircraft fastener life prediction system 100 based on stress analysis and is used to execute the functions in the present application.

[0182] The aircraft fastener life prediction system 100 based on stress analysis can be a general-purpose server or a special-purpose server, both of which can be used to implement the aircraft fastener life prediction method based on stress analysis of the present application. Although only one server is shown in the present application, for convenience, the functions described in the present application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0183] For example, an aircraft fastener life prediction system 100 based on stress analysis may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as disks, ROMs, or RAMs, or any combination thereof. Exemplarily, the aircraft fastener life prediction system 100 based on stress analysis may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of the present application can be implemented according to these program instructions. The aircraft fastener life prediction system 100 based on stress analysis further includes an input / output (I / O) interface 150 between the computer and other input / output devices.

[0184] For ease of explanation, only one processor is described in the aircraft fastener life prediction system 100 based on stress analysis. However, it should be noted that the aircraft fastener life prediction system 100 in the present application may also include multiple processors. Therefore, the steps performed by one processor described in the present application may also be jointly performed or separately performed by multiple processors. For example, if the processor of the aircraft fastener life prediction system 100 performs step A and step B, it should be understood that step A and step B may also be jointly performed by two different processors or separately performed in one processor. For example, the first processor performs step A, the second processor performs step B, or the first processor and the second processor jointly perform steps A and B.

[0185] In addition, an embodiment of the present invention further provides a readable storage medium, in which computer-executable instructions are preset. When the processor executes the computer-executable instructions, the above-mentioned aircraft fastener life prediction method based on stress analysis is implemented.

[0186] It should be noted that, in order to simplify the presentation of the disclosure of the present invention and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, sometimes multiple features are merged into one embodiment, drawing, or description thereof.

Claims

1. A method for predicting the life of aviation fasteners based on stress analysis, characterized in that: The method comprises: Collecting a dynamic stress data set of a target fastener under a service environment, wherein the dynamic stress data set includes a periodic load sequence, a temperature fluctuation sequence, and surface deformation monitoring data; Performing three-dimensional stress field reconstruction processing on the dynamic stress data set to generate stress distribution characteristics of the target fastener, wherein the stress distribution characteristics include axial stress gradient, radial stress accumulation and tangential stress fluctuation coefficient; Calling a pre-trained life prediction model to perform nonlinear degradation analysis on the stress distribution characteristics to generate a remaining life prediction value and a critical failure area identifier of the target fastener; Performing a service environment compensation correction process on the remaining life prediction value to generate a corrected remaining life prediction value, wherein the service environment compensation correction process is implemented based on the correlation relationship between the temperature fluctuation sequence and the thermal expansion coefficient of the material; Generate a fastener maintenance strategy set according to the key failure area identifier, wherein the fastener maintenance strategy set includes a stress release path adjustment scheme and a surface strengthening treatment scheme; The calling of the pre-trained life prediction model to perform nonlinear degradation analysis on the stress distribution characteristics to generate a remaining life prediction value and a key failure area identifier of the target fastener includes: The axial stress gradient is input into the first characteristic analysis layer of the life prediction model, and the distribution coordinates and stress amplitude variation curve of the axial stress concentration area are determined by a stress concentration factor calculation module; Inputting the radial stress accumulation into the second characteristic analysis layer of the life prediction model, performing contact fatigue damage accumulation calculation, and generating fatigue crack initiation probability and propagation rate prediction values ​​of the radial contact surface; Inputting the tangential stress fluctuation coefficient into the third characteristic analysis layer of the life prediction model, and calculating the material wear thickness and surface roughness evolution data of the tangential friction surface based on the surface wear degradation model; The stress amplitude variation curve, the fatigue crack initiation probability and the material wear thickness are integrated to generate a comprehensive degradation index of the target fastener, and a remaining life prediction value is determined according to a comparison result between the comprehensive degradation index and a preset life threshold; Based on the spatial superposition results of the distribution coordinates, the predicted value of the expansion rate and the surface roughness evolution data, the geometric positions of stress concentration areas, crack expansion paths and high-risk wear areas are identified.

2. The method for predicting the life of aviation fasteners based on stress analysis according to claim 1, characterized in that: The performing three-dimensional stress field reconstruction processing on the dynamic stress data set to generate stress distribution characteristics of the target fastener includes: Dividing the periodic load sequence into a plurality of load subsequences according to a time window, each load subsequence corresponding to a stress acquisition period; For each of the load subsequences, the following processing is performed: Constructing a three-dimensional deformation topological structure of the target fastener according to the surface deformation monitoring data, wherein the three-dimensional deformation topological structure includes spatial distribution data of an axial displacement field, a radial displacement field, and a tangential displacement field; The three-dimensional deformation topological structure is coupled with the load subsequence for analysis and processing to generate a stress field reconstruction result of the current time window, wherein the stress field reconstruction result includes a spatial distribution matrix of an axial stress component, a radial stress component, and a tangential stress component; The stress field reconstruction results of multiple consecutive time windows are cumulatively superimposed to calculate the axial stress gradient, radial stress accumulation and tangential stress fluctuation coefficient; wherein, The axial stress gradient is the maximum rate of change of the axial stress component along the length direction of the fastener. The radial stress accumulation is the integral of the radial stress component in the normal direction of the fastener contact surface. The tangential stress fluctuation coefficient is the ratio of the standard deviation of the tangential stress component within a predetermined time interval to the average value.

3. The method for predicting the life of aviation fasteners based on stress analysis according to claim 2, characterized in that: The coupling analysis and processing of the three-dimensional deformation topological structure and the load subsequence to generate a stress field reconstruction result of the current time window includes: Based on the corresponding relationship between the axial displacement field and the axial load component in the load subsequence, an axial displacement-load mapping equation is established, and a first distribution function of the axial stress component is obtained by solving the axial displacement-load mapping equation; According to the correlation characteristics between the radial displacement field and the contact surface pressure, a radial contact stress calculation model is constructed, wherein the radial contact stress calculation model includes dynamic correction parameters of the material Poisson's ratio and the elastic modulus; In combination with the spatial variation rate of the tangential displacement field and the surface friction coefficient, an iterative calculation process of the tangential friction stress is established, wherein the iterative calculation process includes a feedback correction mechanism of the displacement increment and the tangential stress increment; The output results of the first distribution function, the radial contact stress calculation model and the tangential friction stress iterative calculation process are subjected to spatial interpolation fusion processing to generate three-dimensional stress field distribution data including axial, radial and tangential stress components.

4. The method for predicting the life of aviation fasteners based on stress analysis according to claim 1, characterized in that: The performing service environment compensation correction processing on the remaining life prediction value to generate a corrected remaining life prediction value includes: Extracting the extreme temperature values ​​and the temperature change frequency in the temperature fluctuation sequence, and calculating the dynamic adjustment amount of the thermal expansion coefficient of the material as the temperature changes; Performing thermal stress compensation calculation on the axial stress gradient according to the dynamic adjustment amount to generate a corrected axial stress gradient; Based on the correlation between the temperature change frequency and the creep property of the material, performing creep damage correction processing on the radial stress accumulation amount to generate a corrected radial stress accumulation amount; According to the material hardness change data under extreme temperature, the tangential stress fluctuation coefficient is subjected to surface strength adaptation adjustment processing to generate a corrected tangential stress fluctuation coefficient; The corrected axial stress gradient, radial stress accumulation and tangential stress fluctuation coefficient are input into the life prediction model for recalculation to generate a remaining life prediction value after compensating for environmental factors.

5. The method for predicting the life of aviation fasteners based on stress analysis according to claim 4, characterized in that: The performing thermal stress compensation calculation on the axial stress gradient according to the dynamic adjustment amount to generate a corrected axial stress gradient includes: Obtaining the initial thermal expansion coefficient of the target fastener at a reference temperature and the dynamic adjustment amount, and establishing a thermal expansion coefficient-temperature correlation function; Calculating the axial thermal stress increment according to the thermal expansion coefficient-temperature correlation function, wherein the axial thermal stress increment is the product of the temperature change and the thermal expansion coefficient change; superimposing the axial thermal stress increment to the calculation process of the axial stress gradient to generate an axial stress gradient correction value including the thermal stress effect; A stress relaxation effect compensation process is performed on the axial stress gradient correction value, and the stress relaxation effect compensation process is implemented based on a multiplication factor of a material stress relaxation curve and a temperature holding time.

6. The method for predicting the life of aviation fasteners based on stress analysis according to claim 1, characterized in that: Generating a set of fastener maintenance strategies according to the key failure area identifiers includes: Calculating an optimal stress release path based on the identification of the stress concentration area, wherein the optimal stress release path is achieved by adjusting the load distribution ratio of adjacent fasteners; According to the identification of the crack propagation path, a surface strengthening treatment scheme is constructed, wherein the surface strengthening treatment scheme includes the selection of the laser shock strengthening area and the optimization configuration of the shock energy parameters; Based on the identification of the high-risk wear area, a lubricant coating strategy is generated, wherein the lubricant coating strategy dynamically adjusts the coating frequency and coating thickness according to the wear rate prediction value; The optimal stress release path, the surface strengthening treatment scheme and the lubricant coating strategy are prioritized to generate a maintenance strategy set including an execution sequence and implementation parameters.

7. The method for predicting the life of aviation fasteners based on stress analysis according to claim 6, characterized in that: The surface strengthening treatment scheme comprises: Extracting geometric features of the crack propagation path, and calculating the path curvature radius and propagation direction angle; Selecting a coverage density of the laser impact spot according to the curvature radius, wherein the coverage density is inversely proportional to the curvature radius; Adjusting the application direction of the laser impact energy based on the expansion direction angle so that the impact energy direction and the crack expansion direction form a predetermined angle; Dynamically adjust the laser shock pulse duration according to the material surface hardness test data to ensure that the impact energy is below the critical value of the material yield strength; Generate a reinforcement parameter configuration table including cover density, impact direction and pulse duration.

8. The method for predicting the life of aviation fasteners based on stress analysis according to claim 1, characterized in that: The method further comprises: Collecting the actual wear amount and crack extension length of the target fastener within a preset verification period; Performing deviation analysis on the actual wear amount and the predicted wear thickness to generate a first error correction coefficient; Performing a time domain comparison process on the crack extension length and the predicted extension rate to generate a second error correction coefficient; adjusting the weight parameter of the life prediction model according to the first error correction coefficient and the second error correction coefficient to generate an optimized life prediction model; The optimized life prediction model is applied to subsequent batches of fastener life prediction tasks.

9. A stress analysis-based aviation fastener life prediction system, characterized in that: The invention comprises a processor and a memory, wherein the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the method for predicting the life of aviation fasteners based on stress analysis as described in any one of claims 1 to 8.

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