A method and apparatus for predicting surface fatigue damage of a locomotive wheel

Abstract

The embodiment of the present application belongs to the field of rail transit technology, and discloses a locomotive wheel surface fatigue damage prediction method and equipment. The corresponding method comprises the following steps: determining a first relationship parameter between a surface length and an internal length and a second relationship parameter between the internal length and an internal depth according to the surface length, the internal length and the internal depth of the pre-acquired crack of the locomotive wheel; generating a wheel-rail contact damage prediction model under the amplitude load condition according to the damage index distribution inside the wheel-rail contact spot under the static wheel load, the first relationship parameter and the second relationship parameter; generating a lateral damage index distribution of the locomotive wheel according to the wheel-rail contact damage prediction model; and predicting the surface fatigue damage condition of the locomotive wheel according to the lateral damage index distribution. The method provided by the present application can more accurately calculate the surface fatigue damage condition of the wheel under different operation lines, and obtain more reliable data.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, and in particular to the field of wheel damage prediction technology in rail transit, specifically to a method, device, equipment, medium, and product program for predicting fatigue damage on the surface of locomotive wheels. Background Technology

[0002] To study the occurrence mechanism of rolling contact fatigue on locomotive wheel surfaces, researchers have proposed numerous numerical simulation models for fatigue crack initiation. Existing wheel-rail rolling contact fatigue models are mainly divided into two categories: The first type of model can be combined with the multibody dynamics model of train-track interaction to determine the influence of various factors such as different operating lines, suspension parameters, and traction control on wheel rolling contact fatigue, such as stability diagrams and damage function models. However, these rolling contact fatigue prediction models can only determine whether the wheel will experience rolling contact fatigue based on the calculated damage index, and cannot predict the actual crack size.

[0003] The second type of model is based on the principles of fracture mechanics and uses the finite element method to model the cracks. It can establish a realistic wheel-rail profile and arbitrary crack size to study the crack propagation characteristics. However, the computational efficiency is very low and it cannot take into account the influence of actual operating conditions on fatigue cracks. Summary of the Invention

[0004] The purpose of this invention is to provide at least one method and device for predicting fatigue damage on the surface of locomotive wheels, so as to overcome the above-mentioned technical problems in the prior art, and to perform more accurate and detailed calculation and evaluation of fatigue damage on the surface of wheels under different operating lines and driver operating conditions, so as to obtain more reliable data.

[0005] To address the aforementioned technical problems, at least one embodiment of the present invention provides a method for predicting fatigue damage on the surface of locomotive wheels, comprising: Based on the pre-obtained surface length, internal length, and internal depth of the locomotive wheel crack on the locomotive wheel, a first relationship parameter between the surface length and the internal length, and a second relationship parameter between the internal length and the internal depth, are determined. Based on the damage index distribution inside the wheel-rail contact patch of the locomotive wheel under static wheel load, the first relational parameter, and the second relational parameter, a wheel-rail contact damage prediction model under amplitude load condition is generated. The distribution of lateral damage indicators for the locomotive wheels is generated based on the wheel-rail contact damage prediction model. The surface fatigue damage of the locomotive wheels is predicted based on the distribution of the lateral damage index.

[0006] In some embodiments, generating the lateral damage index distribution of the locomotive wheels based on the wheel-rail contact damage prediction model includes: The surface fatigue damage process of the locomotive wheel is simulated based on the wheel-rail contact damage prediction model. During the simulation, the lateral damage index distribution calculated in each integration step is added to the lateral damage index distribution of the previous integration step until the simulation process reaches the preset locomotive running distance, so as to generate the lateral damage index distribution of the locomotive wheels.

[0007] In some embodiments, a method for predicting fatigue damage on the surface of a locomotive wheel further includes: The transverse damage index distribution of the current integration step is generated based on the maximum value of the damage index distribution within the contact patch obtained in the current integration step along the longitudinal direction.

[0008] In some embodiments, a wheel-rail contact damage prediction model under wide-range load conditions is generated based on the damage index distribution within the wheel-rail contact patch of the locomotive wheel under static wheel load, the first relational parameter, and the second relational parameter, including: The initial model of the wheel-rail contact damage prediction model is generated based on the Basquin model; The structure of the wheel-rail contact damage prediction model is determined based on the initial model; The coefficients of the wheel-rail contact damage prediction model are determined based on the structure of the wheel-rail contact damage prediction model, the structure of the wheel-rail contact damage prediction model corresponding to the pre-selected standard crack, the first relationship parameter, and the second relationship parameter. The wheel-rail contact damage prediction model is generated based on the structure and coefficients of the wheel-rail contact damage prediction model.

[0009] In some embodiments, the damage index distribution of the standard crack is a fixed constant.

[0010] In some embodiments, a method for predicting fatigue damage on the surface of a locomotive wheel further includes: The damage index distribution inside the wheel-rail contact patch is determined based on the material shear yield strength of the locomotive wheel, the maximum normal contact pressure inside the wheel-rail contact patch, the longitudinal creep force distribution, the lateral creep force distribution, and the normal stress distribution of the locomotive wheel.

[0011] In some embodiments, the step of obtaining the surface length, internal length, and internal depth of the crack in the locomotive wheel includes: The locomotive wheels are subjected to ultrasonic inspection to obtain the surface length, internal length, and internal depth.

[0012] At least one embodiment of the present invention also provides a locomotive wheel surface fatigue damage prediction device, comprising: The relational parameter determination module is used to determine a first relational parameter between the surface length and the internal length and a second relational parameter between the internal length and the internal depth based on the pre-acquired surface length, internal length and internal depth of the crack in the locomotive wheel; The wheel-rail contact damage prediction model generation module is used to generate a wheel-rail contact damage prediction model under the amplitude load condition based on the damage index distribution inside the wheel-rail contact patch of the locomotive wheel under static wheel load, the first relational parameter, and the second relational parameter. The lateral damage index distribution generation module is used to generate the lateral damage index distribution of the locomotive wheels based on the wheel-rail contact damage prediction model. The surface fatigue damage prediction module is used to predict the surface fatigue damage of the locomotive wheel based on the distribution of the lateral damage index.

[0013] At least one embodiment of the present invention also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method for predicting fatigue damage on the surface of a locomotive wheel.

[0014] At least one embodiment of the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting fatigue damage on the surface of a locomotive wheel.

[0015] The present invention provides a method and apparatus for predicting fatigue damage on the surface of a locomotive wheel. The method includes: first, determining a first relationship parameter between the surface length and the internal length and a second relationship parameter between the internal length and the internal depth based on the pre-acquired length of the crack on the surface of the locomotive wheel, the length inside the locomotive wheel, and the internal depth of the crack; next, generating a wheel-rail contact damage prediction model under abscissa load conditions based on the damage index distribution inside the wheel-rail contact patch under static wheel load, the first relationship parameter, and the second relationship parameter; generating a lateral damage index distribution of the locomotive wheel based on the wheel-rail contact damage prediction model; and finally, predicting the surface fatigue damage of the locomotive wheel based on the lateral damage index distribution.

[0016] The method provided by this invention is based on measured wheel surface cracks and considers the influence of wheel-rail dynamic interaction on fatigue damage cracks during vehicle operation. It can predict the length and depth of wheel surface cracks under different train operation conditions and different operating track conditions. The unknown parameters in the model are calibrated using measured wheel surface cracks, allowing for a relatively accurate and near-realistic prediction of wheel surface cracks. This invention is applicable to the analysis of problems such as wheel life prediction, and can thus provide suggestions for train operation, track optimization, and other aspects. Attached Figure Description

[0017] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0018] Figure 1 This is a schematic flowchart of a method for predicting fatigue damage on the surface of a locomotive wheel, provided by an embodiment of the present invention. Figure 2 This is a flowchart illustrating step 200 provided in one embodiment of the present invention; Figure 3 This is a flowchart illustrating step 300 provided in one embodiment of the present invention; Figure 4 This is a flowchart illustrating a method for predicting fatigue damage on the surface of a locomotive wheel, provided by a specific embodiment of the present invention. Figure 5 This is a schematic diagram of surface cracks and internal cracks on a wheel provided in a specific embodiment of the present invention; Figure 6 This is a damage index FI distribution diagram inside the wheel-rail contact patch provided in a specific embodiment of the present invention; Figure 7 This is a damage index Di distribution diagram inside the wheel-rail contact patch provided in a specific embodiment of the present invention; Figure 8 This is a distribution diagram of the lateral damage index Dy of a wheel provided in a specific embodiment of the present invention; Figure 9 This is a schematic diagram of the predicted crack length along the transverse direction on the wheel surface provided by a specific embodiment of the present invention; Figure 10 This is a schematic diagram of the predicted length of internal cracks distributed laterally along a wheel, provided by a specific embodiment of the present invention; Figure 11 This is a schematic diagram of the predicted depth of internal cracks distributed laterally along a wheel, provided by a specific embodiment of the present invention. Figure 12 This is a schematic diagram of a locomotive wheel surface fatigue damage prediction device provided in an embodiment of the present invention; Figure 13This is a schematic diagram of the structure of an electronic device provided in another embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the embodiments of the present invention to facilitate a better understanding of the invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0020] In the following, the terms “comprising” or “may include” as used in various embodiments of the invention indicate the presence of the claimed function, operation, or element, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in various embodiments of the invention, the terms “comprising,” “having,” and their cognates are intended only to indicate a specific feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or the possibility of adding one or more combinations of features, numbers, steps, operations, elements, components, or combinations of the foregoing.

[0021] In various embodiments of the invention, the expression "or" or "at least one of B and / or C" includes any combination or all combinations of the words listed simultaneously. For example, the expression "B or C" or "at least one of B and / or C" may include B, may include C, or may include both B and C.

[0022] The expressions used in the various embodiments of the present invention (such as "first," "second," etc.) may modify various constituent elements in the various embodiments, but do not limit the corresponding constituent elements. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are only used for the purpose of distinguishing one element from other elements. For example, a first user device and a second user device refer to different user devices, although both are user devices. For example, a first element may be referred to as a second element without departing from the scope of the various embodiments of the present invention, and similarly, a second element may also be referred to as a first element.

[0023] It should be noted that if a description refers to "connecting" a component to another component or "connecting" it to another component, then the first component can be directly connected to the second component, and a third component can be "connected" between the first and second components. Conversely, when a component is "directly connected" to another component or "directly connected" to another component, it can be understood that there is no third component between the first and second components.

[0024] The terminology used in the various embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to limit the various embodiments of the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of the invention pertain. The terms (such as those defined in a generally used dictionary) are to be interpreted as having the same meaning as in the context of the relevant technical field and are not to be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of the invention.

[0025] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0026] Example 1: Railway transportation, as a vital tool in modern transportation, plays an irreplaceable role in economic development. Currently, railway lines include numerous small-radius curves, long gradients, and lines with overlapping curves and gradients, resulting in complex train operating conditions. Furthermore, with the increasing speed and heavy loads in railway transportation in recent years, wheel-rail wear has become a more prominent issue. High-power electric locomotives, in particular, are prone to rolling contact fatigue of the wheels during service, leading to continuous crack bands on the wheel tread surface. The propagation of these cracks causes material peeling and detachment, worsening the wheel-rail relationship, resulting in abnormal vibrations of the locomotive and track, and ultimately damaging locomotive and track components, severely impacting the safety and reliability of train operation. Simultaneously, it increases maintenance time and costs, reducing the economic and social benefits of railway transportation. To address these problems, this embodiment presents a method for predicting fatigue damage on the surface of locomotive wheels, which can be applied to electronic devices with communication, computing, and data storage capabilities. The specific process is as follows: Figure 1 As shown, it includes: Step 100: Determine a first relationship parameter between the surface length and the internal length and the internal depth of the crack on the locomotive wheel based on the pre-acquired surface length, internal length and internal depth of the crack on the locomotive wheel; Step 200: Generate a wheel-rail contact damage prediction model under wide load conditions based on the damage index distribution inside the wheel-rail contact patch of the locomotive wheel under static wheel load, the first relational parameter, and the second relational parameter. Step 300: Generate the lateral damage index distribution of the locomotive wheels based on the wheel-rail contact damage prediction model; Step 400: Predict the surface fatigue damage of the locomotive wheel based on the distribution of the lateral damage index.

[0027] An embodiment of the present invention provides a method for predicting fatigue damage on the surface of a locomotive wheel, comprising: first, determining a first relationship parameter between the surface length and the internal length and a second relationship parameter between the internal length and the internal depth based on the pre-acquired length of the crack on the surface of the locomotive wheel, the length inside the locomotive wheel, and the internal depth of the crack; next, generating a wheel-rail contact damage prediction model under abscissa load conditions based on the damage index distribution inside the wheel-rail contact patch under static wheel load, the first relationship parameter, and the second relationship parameter; generating a lateral damage index distribution of the locomotive wheel based on the wheel-rail contact damage prediction model; and finally, predicting the surface fatigue damage of the locomotive wheel based on the lateral damage index distribution.

[0028] To overcome the shortcomings of existing methods, the inventors of this invention, through long-term exploration, experimentation, and continuous innovation, have proposed a method for predicting fatigue damage on wheel surfaces. This method is based on the stability diagram and incorporates the Basquin formula for predicting metal fatigue life, linking the damage parameters in the stability diagram with the actual crack length. Compared to stability diagram theory, this method more accurately reflects the predicted crack results on the wheel surface; furthermore, it achieves faster computation speed compared to the finite element method. This method can be combined with a multibody dynamics model of train-track interaction, thus enabling more accurate and detailed calculation and evaluation of wheel surface fatigue damage under different operating lines and driver handling conditions, obtaining more reliable data. It is an excellent method for predicting fatigue damage on wheel surfaces.

[0029] In step 100, surface cracks on multiple wheels are inspected using methods such as ultrasound, and the crack lengths on the wheel surfaces are measured. ρ and the length of the crack inside the wheel. L and crack depth hOne crack was selected as a reference value, and its surface crack length was recorded as . ρ r This is used for subsequent simulation calculations. Next, the surface length is calculated. ρ The relationship parameter r between the internal length L and the internal depth h is given by the parameter θ.

[0030] Specifically, for step 200, a wheel-rail contact damage prediction model is established, and the damage index distribution inside the wheel-rail contact patch under static wheel load is solved. FI (x, y) ; using surface length ρ and reference crack (standard crack) length ρ r Based on the Basquin formula used to predict the fatigue life of metals, the parameters A and B in the formula are solved to establish a wheel-rail contact damage prediction model.

[0031] Regarding step 300, the distribution of lateral damage indicators for locomotive wheels refers to the distribution of lateral damage caused by factors such as force, wear, and vibration during wheel operation. Lateral damage to the wheel refers to problems such as wear, peeling, and cracks appearing on the side or outer surface of the wheel. These damages can be caused by various factors, and common formation mechanisms include: Wheel-rail mismatch: If the track is uneven or there is a difference in geometry between the wheel and the track, uneven wear may occur on the contact surface between the wheel and the track, which may lead to lateral damage.

[0032] Wheel slippage: Relative slippage between the wheel and the track (especially during turning or braking) generates lateral stress, causing damage to the side of the wheel.

[0033] Lateral forces: Especially at track curves, turnouts, or more complex track structures, the wheels experience significant lateral forces. These forces act on the sides of the wheels, and prolonged exposure can lead to lateral damage.

[0034] Speed ​​and mass distribution: At higher speeds, the interaction between the wheels and the rails is stronger, and the problem of lateral damage to the wheels may be more prominent, especially during the operation of high-speed locomotives.

[0035] Suspension system problems: Instability or damage to the suspension system may lead to uneven contact between the wheels and the rails, which in turn can cause lateral damage.

[0036] Lateral damage to locomotive wheels can be assessed using a series of indicators. Preferably, the damage indicators include: Wheel wear: refers to the thickness or area of ​​wear on the surface of a wheel in the lateral direction, which can be obtained by measuring the outer diameter of the wheel or by using laser scanning.

[0037] Wheel peeling and cracking: Due to long-term lateral stress, material peeling or cracking may occur on the wheel surface. These damages will be assessed through non-destructive testing (such as ultrasonic testing, magnetic particle testing, etc.).

[0038] Wheel profile changes: Lateral damage often causes changes in the wheel profile; for example, the wheel contact surface may become uneven. Wheel profile changes can be reflected by measuring the rim height or diameter difference.

[0039] Lateral forces and torques: By measuring the lateral forces and torques between the wheel and the rail, potential damage to the wheel surface can be estimated. Excessive lateral forces can lead to excessive wear or localized damage to the wheel.

[0040] Vibration acceleration: Lateral damage often affects the vibration characteristics of a wheel, causing it to experience significant lateral vibration acceleration during operation. Therefore, monitoring the wheel's vibration acceleration can indirectly reflect the lateral damage to the wheel.

[0041] For step 400, firstly, based on the calculated transverse damage index... D y (y) and the crack reference value obtained from the test ρ r Solve for the predicted crack lengths distributed laterally on the wheel surface. ρ p ( y Next, based on the calculated surface crack length... ρ p ( y ), combined with parameters r and θ Solve for the internal length of the transversely distributed cracks on the wheel. L p ( y and crack depth h p ( y ).

[0042] Example 2: In some embodiments, the surface length of the crack ρ and internal length L relational parameters r and internal length L and crack depth h relational parameters θ The solution method is as follows: (1) In the formula, ρ i , Li and h i The dimensions are the surface length, internal length, and crack depth of each test crack. T This represents the total number of cracks on the wheel surface obtained from the test.

[0043] In some embodiments, a method for predicting fatigue damage on the surface of a locomotive wheel further includes: The damage index distribution inside the wheel-rail contact patch is determined based on the material shear yield strength of the locomotive wheel, the maximum normal contact pressure inside the wheel-rail contact patch, the longitudinal creep force distribution, the lateral creep force distribution, and the normal stress distribution of the locomotive wheel.

[0044] Specifically, the damage index distribution inside the wheel-rail contact patch under static wheel load is solved. By solving the damage index of each grid within the contact patch separately, the final damage index distribution is obtained: (2) In the formula, k e The shear yield strength of the wheel material. P 0 represents the maximum normal contact pressure within the contact patch. q x , q y These represent the longitudinal and transverse creep force distributions within each unit of the contact patch; p z Normal stress distribution within each element.

[0045] In some embodiments, see Figure 2 Step 200 includes: Step 201: Generate the initial model of the wheel-rail contact damage prediction model based on the Basquin model; The Basquin formula for predicting the fatigue life of metals is expressed as follows: (3) In the formula, S α For fatigue stress, N f For fatigue life, α and β These are the fatigue strength coefficient and the fatigue strength index, respectively.

[0046] Step 202: Determine the structure of the wheel-rail contact damage prediction model based on the initial model; Formula (3) was derived by combining Miner's Rule linear damage accumulation method, making it applicable to variable amplitude load conditions: (4) In the formula, D As a damage indicator, FI max This represents the maximum value of the internal damage index of the contact patch.

[0047] Step 203: Determine the coefficients of the wheel-rail contact damage prediction model based on the structure of the wheel-rail contact damage prediction model, the structure of the wheel-rail contact damage prediction model corresponding to the pre-selected standard crack, the first relational parameter, and the second relational parameter; parameter A and B The relationship between the fatigue strength coefficient and fatigue strength index in Basquin's formula is as follows: (5) Let the selected reference crack damage index D r If the value is 1, then each measurement yields the damage index of the crack on the wheel surface. D x Damage index compared to reference crack D r The relationship is as follows: (6) Among them, parameters B The minimum error can be obtained by solving for the value corresponding to the minimum error. B The value is obtained, and then the parameter is obtained by solving according to formula (5). A .

[0048] (7) In the formula, T This represents the total number of cracks on the wheel surface obtained from the test.

[0049] Step 204: Generate the wheel-rail contact damage prediction model based on the structure and coefficients of the wheel-rail contact damage prediction model.

[0050] In some embodiments, see Figure 3 Step 300 includes: Step 301: Simulate the surface fatigue damage process of the locomotive wheel based on the wheel-rail contact damage prediction model; Step 302: During the simulation, the lateral damage index distribution calculated in each integration step is added to the lateral damage index distribution of the previous integration step until the simulation process reaches the preset locomotive running distance, so as to generate the lateral damage index distribution of the locomotive wheels.

[0051] A vehicle-track coupled dynamics model is established for simulation calculations. At each integration step during the calculation process, the distribution of wheel lateral damage indices is output. Diy ( y The damage index calculated in each integration step is superimposed with the result of the previous integration step until the vehicle dynamics model travels a predetermined distance. The loop calculation then ends, and the final wheel lateral distribution is output. D y ( y ).

[0052] Specifically, each integration step outputs the contact patch damage index distribution. FI i ( x , y ).

[0053] Combined with the calculated parameters A and B Solve for the distribution of damage indicators D i ( x , y The expression is as follows: (8) Finally, the internal structure of the contact patch obtained from the current integration step will be calculated. D i ( x , y The maximum value along the longitudinal direction is used to obtain the lateral damage index distribution of the wheel. D yi ( y ).

[0054] In some embodiments, a method for predicting fatigue damage on the surface of a locomotive wheel further includes: The transverse damage index distribution of the current integration step is generated based on the maximum value of the damage index distribution within the contact patch obtained in the current integration step along the longitudinal direction.

[0055] In some embodiments, the step of obtaining the surface length, internal length, and internal depth of the crack in the locomotive wheel includes: The locomotive wheels are subjected to ultrasonic inspection to obtain the surface length, internal length, and internal depth.

[0056] An ultrasonic probe emits high-frequency sound waves (2~10 MHz) towards the wheel. When the sound waves propagate through the material and encounter defects (such as cracks), they are reflected. By analyzing the amplitude and time of the reflected signal, the location and size of the defect can be determined.

[0057] In some embodiments, the predicted crack length distributed laterally on the wheel surface is... ρ p ( ySolve using the following formula: (9) The length of the internal cracks distributed laterally on the wheel L p ( y and crack depth h p ( y Solve using the following formula: (10) An embodiment of the present invention provides a method for predicting fatigue damage on the surface of a locomotive wheel, comprising: first, determining a first relationship parameter between the surface length and the internal length, and a second relationship parameter between the internal length and the internal depth, based on pre-acquired crack lengths on the surface of the locomotive wheel, lengths inside the locomotive wheel, and internal depth; next, generating a wheel-rail contact damage prediction model under abbreviated load conditions based on the damage index distribution inside the wheel-rail contact patch under static wheel load, the first relationship parameter, and the second relationship parameter; generating a lateral damage index distribution of the locomotive wheel based on the wheel-rail contact damage prediction model; and finally, predicting the surface fatigue damage of the locomotive wheel based on the lateral damage index distribution. Compared with the prior art, the beneficial effects of the present invention are: 1. This invention is based on the stability diagram theory and the Basquin formula for predicting the fatigue life of metals. Furthermore, it calibrates relevant parameters in the model based on measured crack lengths on the wheel surface, establishing a wheel-rail contact damage prediction model that combines traditional theory with experimental results. The existing formulas are also derived and improved, resulting in more accurate and realistic calculations than existing models.

[0058] 2. Current stability diagrams and damage function models widely used for wheel damage prediction can only assess whether rolling contact fatigue will occur, but cannot quantitatively predict fatigue cracks. This method can solve for the predicted values ​​of crack length, internal crack length, and crack depth distributed laterally on the wheel surface through simulation calculations.

[0059] Example 3: To further illustrate the solution, this invention also provides a specific implementation method for predicting fatigue damage on the surface of locomotive wheels, see [link to implementation details]. Figure 4 Specifically, it includes the following:

[0060] S1: Multiple wheels were inspected for surface cracks using methods such as ultrasonic testing, and the surface length of the wheel cracks was measured. ρ Internal length of wheel crack L and crack depth h Select one crack as a reference value, and denote its surface length as . ρ rThis is used for subsequent simulation calculations.

[0061] See Figure 5 The surface length, internal length, and depth of the wheel cracks were collected from on-site measurements. One crack was selected as a reference value, and its surface length was denoted as... ρ r =3.0 mm. Based on all the measured crack-related parameters, the mean value of the crack relationship parameters is calculated according to formula (1) to obtain the parameter values. r =2.67, θ =35°.

[0062] S2: Calculate the surface length of the crack based on all crack-related parameters measured in S1. ρ and the internal length of the crack L relational parameters r and the internal length of the crack L and crack depth h relational parameters θ .

[0063] S3: Establish a wheel-rail contact damage prediction model and solve for the damage index distribution inside the wheel-rail contact patch under static wheel load. FI ( x , y The surface length of the wheel crack obtained from the test in S1 is used. ρ and reference crack length ρ r Based on the Basquin formula used to predict the fatigue life of metals, the parameters in the formula are solved. A and B .

[0064] Taking a 25t axle load locomotive as an example, the damage index distribution inside the wheel-rail contact patch under static wheel load is solved. FI ( x , y ),like Figure 6 As shown. Based on the wheel surface crack parameters obtained from the test and the formula described in the text, the parameters were calculated. A =1.04·10 -6 , B =-1.06.

[0065] S4: Establish a vehicle-track coupled dynamics model for simulation calculations. Output the wheel lateral damage index distribution at each integration step during the calculation process. D iy ( yThe damage index calculated in each integration step is superimposed with the result of the previous integration step until the vehicle dynamics model travels a predetermined distance. The loop calculation then ends, and the final wheel lateral distribution is output. D y ( y ).

[0066] Specifically, each integration step in the calculation process solves for the distribution of damage indices. D i ( x , y ),like Figure 7 As shown. The contact patch inside is calculated using the current integration step. D i ( x , y The distribution of wheel lateral damage index is obtained by taking the maximum value along the longitudinal direction. D yi ( y ).

[0067] The damage index calculated in each integration step is superimposed with the result of the previous integration step until the vehicle dynamics model has traveled 1000 m. Then, the loop calculation ends and the final distribution of wheel lateral damage index is output. D y ( y The result is as follows Figure 8 As shown.

[0068] S5: Wheel lateral distribution calculated based on S4 D y ( y ), and the crack reference value obtained from the test in S1. ρ r Solve for the predicted crack lengths distributed laterally on the wheel surface. ρ p ( y ).

[0069] S6: Surface length of the crack calculated from S5 ρ p ( y Combined with the parameters calculated in S2 r and θ Solve for the internal length of the transversely distributed cracks on the wheel. L p ( y and crack depth h p ( y ).

[0070] For steps S5 and S6, the predicted crack lengths distributed laterally on the wheel surface are obtained. ρ p ( y ), length of internal cracks distributed laterally on the wheel L p ( y and crack depth h p ( y ), respectively as Figure 9 , Figure 10 and Figure 11 As shown.

[0071] In summary, the specific embodiments of the present invention provide a method for predicting fatigue damage on the surface of locomotive wheels, comprising: first, determining the wear influencing factors of heavy-load rails based on the wear type, rail material, track structure parameters, train speed, and environmental factors; then, conducting wear simulation experiments on heavy-load rails based on a preset orthogonal array and the wear influencing factors; and finally, predicting the wear condition of heavy-load rails based on the experimental results of the wear simulation experiments.

[0072] This invention uses orthogonal experimental design to select representative schemes from a comprehensive set of experimental schemes, and combines this with data analysis methods to obtain heavy-load track wear data and train safety indicators under the combined effects of multiple factors.

[0073] Example 4: Another embodiment of the present invention relates to a locomotive wheel surface fatigue damage prediction device. The implementation details of this locomotive wheel surface fatigue damage prediction device are described below. The following details are provided for ease of understanding and are not essential for implementing this solution. A schematic diagram of the locomotive wheel surface fatigue damage prediction device in this embodiment can be seen as follows: Figure 12 As shown, there are a relation parameter determination module 801, a wheel-rail contact damage prediction model generation module 802, a transverse damage index distribution generation module 803, and a surface fatigue damage prediction module 804.

[0074] The relational parameter determination module 801 is used to determine a first relational parameter between the surface length and the internal length and a second relational parameter between the internal length and the internal depth based on the pre-acquired surface length, internal length and internal depth of the crack in the locomotive wheel; The wheel-rail contact damage prediction model generation module 802 is used to generate a wheel-rail contact damage prediction model under the amplitude load condition based on the damage index distribution inside the wheel-rail contact patch of the locomotive wheel under static wheel load, the first relational parameter, and the second relational parameter. The lateral damage index distribution generation module 803 is used to generate the lateral damage index distribution of the locomotive wheel based on the wheel-rail contact damage prediction model. The surface fatigue damage prediction module 804 is used to predict the surface fatigue damage of the locomotive wheel based on the distribution of the lateral damage index.

[0075] In some embodiments, the transverse damage index distribution generation module 803 includes: The fatigue damage simulation unit is used to simulate the surface fatigue damage process of the locomotive wheel based on the wheel-rail contact damage prediction model. The integration result accumulation unit is used to add the lateral damage index distribution calculated in each integration step to the lateral damage index distribution of the previous integration step during the simulation process, until the simulation process reaches the preset locomotive running distance, so as to generate the lateral damage index distribution of the locomotive wheels.

[0076] In some embodiments, a locomotive wheel surface fatigue damage prediction device further includes: The current integration lateral damage index distribution generation module is used to generate the lateral damage index distribution of the current integration step based on the maximum value of the damage index distribution inside the contact patch along the longitudinal direction obtained in the current integration step.

[0077] In some embodiments, the wheel-rail contact damage prediction model generation module 802 includes: The initial model generation unit is used to generate the initial model of the wheel-rail contact damage prediction model based on the Basquin model. The structure determination module is used to determine the structure of the wheel-rail contact damage prediction model based on the initial model. The coefficient determination unit is used to determine the coefficients of the wheel-rail contact damage prediction model based on the structure of the wheel-rail contact damage prediction model, the structure of the wheel-rail contact damage prediction model corresponding to the pre-selected standard crack, the first relationship parameter, and the second relationship parameter. The wheel-rail contact damage prediction model generation unit is used to generate the wheel-rail contact damage prediction model based on the structure and coefficients of the wheel-rail contact damage prediction model.

[0078] In some embodiments, the damage index distribution of the standard crack is a fixed constant.

[0079] In some embodiments, a locomotive wheel surface fatigue damage prediction device further includes: The damage index distribution determination module is used to determine the damage index distribution inside the wheel-rail contact patch based on the material shear yield strength of the locomotive wheel, the maximum normal contact pressure inside the wheel-rail contact patch, the longitudinal creep force distribution, the lateral creep force distribution, and the normal stress distribution of the locomotive wheel.

[0080] In some embodiments, the step of obtaining the surface length, internal length, and internal depth of the crack in the locomotive wheel includes: The locomotive wheels are subjected to ultrasonic inspection to obtain the surface length, internal length, and internal depth.

[0081] This invention provides a method for predicting fatigue damage on the surface of a locomotive wheel, comprising: first, determining a first relationship parameter between the surface length and the internal length, and a second relationship parameter between the internal length and the internal depth, based on pre-acquired crack lengths on the surface of the locomotive wheel, lengths inside the locomotive wheel, and internal depth; next, generating a wheel-rail contact damage prediction model under abscissa load conditions based on the damage index distribution inside the wheel-rail contact patch under static wheel load, the first relationship parameter, and the second relationship parameter; generating a lateral damage index distribution of the locomotive wheel based on the wheel-rail contact damage prediction model; and finally, predicting the surface fatigue damage of the locomotive wheel based on the lateral damage index distribution.

[0082] First, this invention is based on the stability diagram theory and the Basquin formula for predicting the fatigue life of metals. Simultaneously, it calibrates relevant parameters in the model based on measured crack lengths on the wheel surface, establishing a wheel-rail contact damage prediction model that combines traditional theory with experimental results. Furthermore, existing formulas have been derived and improved, resulting in more accurate and realistic calculation results compared to existing models.

[0083] Secondly, the stability diagrams and damage function models currently widely used for wheel damage prediction can only assess whether rolling contact fatigue will occur in the wheel, but cannot quantitatively predict fatigue cracks. This method, however, can use simulation calculations to determine the predicted values ​​of crack length, internal crack length, and crack depth distributed laterally on the wheel surface.

[0084] Finally, compared with wheel damage prediction models based on the finite element method, the model provided by this invention has higher computational efficiency and can be combined with vehicle dynamics simulation models to predict wheel surface fatigue damage under different operating routes, operating conditions and other conditions, and has a wider range of applications.

[0085] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by this invention; however, this does not mean that other units are absent from this embodiment.

[0086] Example 5: Another embodiment of the present invention relates to an electronic device, such as Figure 13 As shown, the electronic device specifically includes the following: Processor 1201, memory 1202, communications interface 1203, and bus 1204; The processor 1201, memory 1202, and communication interface 1203 communicate with each other via bus 1204; the communication interface 1203 is used to realize information transmission between server-side devices and user-side devices and other related devices. The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, it implements all the steps in the locomotive wheel surface fatigue damage prediction method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: Step 100: Determine a first relationship parameter between the surface length and the internal length and the internal depth of the crack on the locomotive wheel based on the pre-acquired surface length, internal length and internal depth of the crack on the locomotive wheel; Step 200: Generate a wheel-rail contact damage prediction model under wide load conditions based on the damage index distribution inside the wheel-rail contact patch of the locomotive wheel under static wheel load, the first relational parameter, and the second relational parameter. Step 300: Generate the lateral damage index distribution of the locomotive wheels based on the wheel-rail contact damage prediction model; Step 400: Predict the surface fatigue damage of the locomotive wheel based on the distribution of the lateral damage index.

[0087] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0088] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0089] Example 6: Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the steps in the above-described locomotive wheel surface fatigue damage prediction method embodiment, the steps including: Step 100: Determine a first relationship parameter between the surface length and the internal length and the internal depth of the crack on the locomotive wheel based on the pre-acquired surface length, internal length and internal depth of the crack on the locomotive wheel; Step 200: Generate a wheel-rail contact damage prediction model under wide load conditions based on the damage index distribution inside the wheel-rail contact patch of the locomotive wheel under static wheel load, the first relational parameter, and the second relational parameter. Step 300: Generate the lateral damage index distribution of the locomotive wheels based on the wheel-rail contact damage prediction model; Step 400: Predict the surface fatigue damage of the locomotive wheel based on the distribution of the lateral damage index.

[0090] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, hardware + program embodiments are relatively simple in description because they are fundamentally similar to method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0091] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0092] While this invention provides method operation steps as shown in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual device or client product execution, the method can be executed in the order shown in the embodiments or drawings or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0093] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method of predicting surface fatigue damage of a locomotive wheel, characterized by, include: Based on the pre-obtained surface length, internal length, and internal depth of the locomotive wheel crack on the locomotive wheel, a first relationship parameter between the surface length and the internal length, and a second relationship parameter between the internal length and the internal depth, are determined. Based on the damage index distribution inside the wheel-rail contact patch of the locomotive wheel under static wheel load, the first relational parameter, and the second relational parameter, a wheel-rail contact damage prediction model under amplitude load condition is generated. The distribution of lateral damage indicators for the locomotive wheels is generated based on the wheel-rail contact damage prediction model. The surface fatigue damage of the locomotive wheels is predicted based on the distribution of the lateral damage index.

2. The method of predicting surface fatigue damage of a locomotive wheel as defined in claim 1, wherein, The distribution of lateral damage indices for the locomotive wheels is generated based on the wheel-rail contact damage prediction model, including: The surface fatigue damage process of the locomotive wheel is simulated based on the wheel-rail contact damage prediction model. During the simulation, the lateral damage index distribution calculated in each integration step is added to the lateral damage index distribution of the previous integration step until the simulation process reaches the preset locomotive running distance, so as to generate the lateral damage index distribution of the locomotive wheels.

3. The method of claim 2, wherein, Also includes: The transverse damage index distribution of the current integration step is generated based on the maximum value of the damage index distribution within the contact patch obtained in the current integration step along the longitudinal direction.

4. The method of predicting surface fatigue damage of a locomotive wheel of claim 1, wherein, Based on the damage index distribution within the wheel-rail contact patch of the locomotive wheel under static wheel load, the first relational parameter, and the second relational parameter, a wheel-rail contact damage prediction model under wide-range load conditions is generated, including: The initial model of the wheel-rail contact damage prediction model is generated based on the Basquin model; The structure of the wheel-rail contact damage prediction model is determined based on the initial model; The coefficients of the wheel-rail contact damage prediction model are determined based on the structure of the wheel-rail contact damage prediction model, the structure of the wheel-rail contact damage prediction model corresponding to the pre-selected standard crack, the first relationship parameter, and the second relationship parameter. The wheel-rail contact damage prediction model is generated based on the structure and coefficients of the wheel-rail contact damage prediction model.

5. The method of predicting surface fatigue damage of a locomotive wheel of claim 4, wherein, The damage index distribution of the standard crack is a fixed constant.

6. The locomotive wheel surface fatigue damage prediction method of claim 1, wherein, Also includes: The damage index distribution inside the wheel-rail contact patch is determined based on the material shear yield strength of the locomotive wheel, the maximum normal contact pressure inside the wheel-rail contact patch, the longitudinal creep force distribution, the lateral creep force distribution, and the normal stress distribution of the locomotive wheel.

7. The method of claim 1 to 6, wherein The steps for obtaining the surface length, internal length, and internal depth of the crack in the locomotive wheel include: The locomotive wheels are subjected to ultrasonic inspection to obtain the surface length, internal length, and internal depth.

8. A device for predicting surface fatigue damage of a locomotive wheel, characterized by, include: The relational parameter determination module is used to determine a first relational parameter between the surface length and the internal length and a second relational parameter between the internal length and the internal depth based on the pre-acquired surface length, internal length and internal depth of the crack in the locomotive wheel; The wheel-rail contact damage prediction model generation module is used to generate a wheel-rail contact damage prediction model under the amplitude load condition based on the damage index distribution inside the wheel-rail contact patch of the locomotive wheel under static wheel load, the first relational parameter, and the second relational parameter. The lateral damage index distribution generation module is used to generate the lateral damage index distribution of the locomotive wheels based on the wheel-rail contact damage prediction model. The surface fatigue damage prediction module is used to predict the surface fatigue damage of the locomotive wheel based on the distribution of the lateral damage index.

9. An electronic device, comprising: include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the locomotive wheel surface fatigue damage prediction method as described in any one of claims 1 to 7.

10. A computer readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for predicting fatigue damage on the surface of locomotive wheels as described in any one of claims 1 to 7.

Images