Idle wheel life prediction method and system based on digital twinning

Abstract

The invention discloses an idle gear life prediction method and system based on digital twinning, and the method comprises the steps: determining the stress distribution information of an idle gear through solving a dynamic model of a gear system, and carrying out the crack and end face wear prediction analysis in an idle gear finite element model according to the stress condition of the idle gear; calculating the severity of the cracks of the idle wheel based on the crack prediction information of the finite element model of the idle wheel; according to end face wear prediction information of the idle wheel finite element model, the severity of idle wheel end face wear is calculated; and constructing an idle wheel degradation model according to the severity of the idle wheel crack and the severity of the idle wheel end face wear, and predicting the service life of the idle wheel by using the idle wheel degradation model. According to the method, prediction information of cracks and end face abrasion of the idle wheel is combined, the degradation model of the idle wheel is constructed, the performance degradation process of the idle wheel under the action of various factors can be comprehensively reflected, and the accuracy of predicting the remaining life of the idle wheel is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical system health monitoring and fault warning, and specifically to a method and system for predicting the life of an idler gear based on digital twin. Background Art

[0002] As a key component in the transmission system, the performance state of the idler gear directly affects the stability and reliability of the entire system. However, during actual operation, the idler gear gradually degrades due to factors such as wear and fatigue, and eventually fails. This makes the life prediction of the idler gear crucial for ensuring the normal operation of the system and reducing maintenance costs. Through accurate life prediction, a reasonable maintenance plan can be formulated to avoid system failures caused by premature component failure, and at the same time reduce unnecessary maintenance replacements, thereby reducing maintenance costs.

[0003] Currently, the existing methods for predicting the life of idler gears mainly include traditional life prediction methods, statistics-based prediction methods, and physics model-based prediction methods.

[0004] Traditional life prediction methods often rely on empirical formulas or statistical models. This method is often limited by the amount of data and expert experience and is difficult to accurately reflect the actual degradation process of the idler gear.

[0005] The statistics-based prediction method uses statistical principles to process and analyze the life data of the idler gear, and predicts the life of the idler gear by establishing a statistical model. Commonly used statistical methods include regression analysis, survival analysis, etc. Although this method improves the prediction accuracy to a certain extent, it is still limited by data quality and sample size. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for predicting the life of an idler gear based on digital twin, and use digital twin technology to digitally simulate and predict the degradation of the idler gear.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] A method for predicting the life of an idler gear based on digital twin, the steps of which include:

[0009] Construct a finite element model of the idler gear and a dynamic model of the gear system according to the parameters of the gear transmission system;

[0010] Determine the stress distribution information received by the idler gear by solving the dynamic model of the gear system, and perform crack and end face wear prediction and analysis on the idler gear finite element model according to the stress conditions received by the idler gear;

[0011] Calculate the severity of the cracks on the idler gear based on the crack prediction information of the idler gear finite element model;

[0012] Calculate the severity of the end face wear of the idle gear according to the prediction information of the end face wear of the idle gear finite element model;

[0013] Construct an idle gear degradation model based on the severity of the idle gear crack and the severity of the end face wear of the idle gear, and use the idle gear degradation model to predict the life of the idle gear.

[0014] Cracks are obvious signs of the fatigue of the idle gear material. As the number of cracks increases, the structural integrity of the idle gear gradually decreases, and the expansion of cracks will accelerate the failure process of the idle gear. End face wear is caused by the contact friction between the idle gear and other mechanical components. As the wear intensifies, the size and shape of the idle gear will change, altering the stress distribution, which may trigger new cracks or accelerate the expansion of existing cracks, thus shortening the service life of the idle gear.

[0015] The existence of cracks will change the local stress state of the idle gear, which may lead to increased wear. At the same time, end face wear may also cause stress concentration, promoting the formation and expansion of cracks. This makes it necessary to consider

[0016] According to the above technical solution, input the idle gear finite element model into the fracture analysis software, introduce initial cracks, and perform crack simulation analysis on the idle gear under the stress distribution information received by the gear; perform wear analysis on the idle gear using Romax based on the idle gear finite element model.

[0017] According to the above technical solution, the calculation steps for the severity of the idle gear crack include:

[0018] Statistically count the total number of idle gear cracks based on the crack prediction information of the idle gear finite element model;

[0019] Calculate the ratio of the total number of idle gear cracks to the maximum safe crack number of the idle gear, and then determine the loss degree of the idle gear crack number;

[0020] Statistically count the number of idle gear crack depths in each idle gear depth evaluation criterion, and calculate the proportion of the crack number corresponding to each idle gear depth evaluation criterion in the total number of idle gear cracks;

[0021] Calculate the overall severity of the idle gear crack depth according to the weight corresponding to each idle gear depth evaluation criterion and the proportion of the crack number corresponding to each idle gear depth evaluation criterion in the total number of idle gear cracks;

[0022] Calculate the severity of the idle gear crack according to the loss degree of the idle gear crack number and the overall severity of the idle gear crack depth.

[0023] According to the above technical solution, the idle gear crack depth evaluation criterion is:

[0024] When the idle gear crack h ≤ γ1 When it is, it is proved that the crack depth of the idle gear is within the safety standard I;

[0025] When the crack of the idle gear is γ 1 <h≤γ 2 When it is, it is proved that the crack depth of the idle gear is within the relatively dangerous standard II;

[0026] When the crack of the idle gear is h>γ 2 When it is, it is proved that the crack depth of the idle gear is in the severe danger III.

[0027] According to the above technical solution, the severity degree t of the crack of the idle gear l :

[0028]

[0029] In the formula, β 1 represents the weight corresponding to the degree of loss of the number of cracks of the idle gear, QTY t represents the degree of loss of the number of cracks of the idle gear at time t, β 2 represents the weight corresponding to the severity degree of the overall crack depth of the idle gear, l t represents the severity degree of the overall crack depth of the idle gear at time t, δ t represents the ratio of the total number of cracks of the idle gear at time t to the maximum safe crack number of the idle gear, ω 1 represents the influence weight of the safety standard I in the life loss of the idle gear, D 1 represents the proportion of the number of cracks corresponding to the safety standard I of the crack depth of the idle gear in the total number of cracks of the idle gear, ω 2 represents the influence weight of the relatively dangerous standard II in the life loss of the idle gear, D 2 represents the proportion of the number of cracks corresponding to the relatively dangerous standard II of the crack depth of the idle gear in the total number of cracks of the idle gear, ω 3 represents the influence weight of the severe danger III in the life loss of the idle gear, D 3 represents the proportion of the number of cracks corresponding to the severe danger III of the crack depth of the idle gear in the total number of cracks of the idle gear.

[0030] Among them, various weights in the calculation process of the severity degree of the crack of the idle gear can be calculated and obtained by methods such as the AHP analytic hierarchy process, the Delphi method, the weighting method, the principal component analysis, the CRITIC method, and machine learning.

[0031] According to the above technical solution, based on the finite element model of the idle gear, Romax is used to analyze the wear of the idle gear and calculate the wear amount of the idle gear;

[0032] The ratio of the calculated wear amount of the idle gear to the preset maximum safe wear amount of the idle gear is calculated, and then the severity degree of the end face wear of the idle gear is determined;

[0033] The severity of the wear on the end face of the idler gear:

[0034]

[0035] In the formula, VY t represents the severity of the wear on the end face of the idler gear at time t, and ρ t represents the ratio of the wear amount of the idler gear to the preset maximum safe wear amount of the idler gear.

[0036] Among them, the depth and wear volume of the idler gear wear are obtained by analyzing the idler gear wear according to Romax, the linear wear rate and volume wear rate are calculated, and then according to the linear wear rate lm t and the volume wear rate Vm t the wear amount mo of the idler gear is obtained t .

[0037] Linear wear rate:

[0038] Volume wear rate:

[0039] Wear amount of idler gear: mo t = μ 1 lm t + μ 2 Vm t ;

[0040] Among them, Δh t represents the wear depth of the idler gear at time t, v m represents the wear volume of the idler gear at time t, μ 1 represents the weight corresponding to the wear depth of the idler gear, and μ 2 represents the weight corresponding to the wear volume of the idler gear. Among them, when calculating the wear amount of the idler gear, the surface roughness of the idler gear can also be considered.

[0041] According to the above technical solution, the idler gear degradation model:

[0042] X(t) = X 0 + a 1 t l + a 2 t m + σ B B(t);

[0043] In the formula, X(t) represents the loss life of the idler gear at time t, and X 0 represents the loss life of the idler gear in the initial state, a 1 represents the weight of the first degradation trend term, t l represents the severity of the idler gear crack described by the first degradation trend term, a 2 represents the weight of the second degradation trend term, t mIt represents the second degradation trend term, which describes the severity of the end face wear of the idler gear, and σ B represents the degree of fluctuation during the degradation of the idler gear. B(t) represents the intrinsic variability of the random degradation process of the idler gear over time as a standard Brownian motion.

[0044] According to the above technical solution, the degree of fluctuation during the degradation of the idler gear is calculated using the maximum likelihood estimation method based on the measured idler gear loss life time series. Specifically:

[0045] Obtain M independently tested idler gears. The degradation amount of the i-th idler gear at time t 1 to time t n is obtained, and the difference ΔX in the degradation amount of the idler gear at adjacent times is calculated. i,t ;

[0046] Based on the differences ΔX in the degradation amount of the idler gear at all adjacent times i,t construct a degradation difference matrix, and transpose the degradation difference matrix to obtain a transposed degradation difference matrix ΔX i ; The transposed degradation difference matrix ΔX i follows a multivariate normal distribution.

[0047] Based on the transposed degradation difference matrix ΔX corresponding to M independently tested idler gears i construct the total idler gear degradation matrix S;

[0048] Calculate the maximum likelihood function of the total idler gear degradation matrix S using the multivariate normal distribution. The hyperparameter σ in the maximum likelihood function of the total idler gear degradation matrix S is the degree of fluctuation σ during the degradation of the idler gear in the idler gear degradation model. B ;

[0049] Obtain the degree of fluctuation σ during the degradation of the idler gear using the maximum likelihood function of the total idler gear degradation matrix S. B .

[0050] Including an embodiment, a digital twin-based idler gear life prediction system includes:

[0051] A finite element construction module constructs an idler gear finite element model according to the gear transmission system parameters and applies boundary conditions according to the gear material properties;

[0052] A dynamics model construction module constructs a gear system dynamics model according to the type of idler gear;

[0053] An idler gear degradation simulation module determines the stress distribution information received by the idler gear by solving the gear system dynamics model, and performs crack and end face wear prediction and analysis on the idler gear finite element model according to the stress condition of the idler gear;

[0054] The idler gear degradation assessment module calculates the severity of cracks in the idler gear based on the crack prediction information of the idler gear finite element model; and calculates the severity of end face wear of the idler gear according to the end face wear prediction information of the idler gear finite element model.

[0055] Idler gear life prediction: construct an idler gear degradation model based on the severity of cracks in the idler gear and the severity of end face wear of the idler gear, and use the idler gear degradation model to predict the life of the idler gear.

[0056] Another embodiment is also included. A storage medium is used to store computer-executable instructions. When the computer-executable instructions are executed, the above technical solution, a method for predicting the life of an idler gear based on digital twin, is implemented.

[0057] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention predicts the cracks in the idler gear through a finite element model, and can accurately capture the position, shape and expansion trend of the cracks. And calculate the severity of the cracks and the wear condition of the end face of the idler gear according to the prediction data of the cracks in the idler gear by the finite element model, which helps to timely identify potential safety hazards and provide a decision-making basis for maintenance or replacement. By calculating the severity of the end face wear, the performance degradation status of the idler gear can be evaluated, and measures can be taken in time to prevent further deterioration. Combining the prediction information of the idler gear cracks and end face wear, constructing a degradation model of the idler gear can comprehensively reflect the performance degradation process of the idler gear under the action of various factors, improve the accuracy of predicting the remaining life of the idler gear, and the life prediction result helps to formulate a reasonable maintenance plan, reduce the maintenance cost and improve the equipment utilization rate. Preventive maintenance based on prediction information can detect and solve potential problems in advance, avoid production interruption caused by sudden equipment failures, and can also extend the service life of the equipment and reduce the replacement cost. Description of the Drawings

[0058] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0059] Figure 1 is a flowchart of the steps of a method for predicting the life of an idler gear based on digital twin of the present invention. Detailed Embodiments

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0061] Embodiment 1

[0062] A method for predicting the life of an idle gear based on digital twin, the steps of which include:

[0063] S1. Construct a finite element model of the idle gear and a dynamic model of the gear system according to the parameters of the gear transmission system.

[0064] S2. Determine the stress distribution information received by the idle gear by solving the dynamic model of the gear system, and perform crack and end face wear prediction and analysis on the idle gear finite element model according to the stress condition of the idle gear. That is, input the idle gear finite element model into the fracture analysis software, introduce the initial crack, and perform crack simulation analysis on the idle gear under the stress distribution information received by the gear; use Romax to analyze the wear of the idle gear on the basis of the idle gear finite element model.

[0065] S3. Based on the crack prediction information of the idle gear finite element model, calculate the severity of the idle gear crack, and the specific steps include:

[0066] S301. Statistically count the total number of cracks of the idle gear based on the crack prediction information of the idle gear finite element model;

[0067] S302. Calculate the ratio of the total number of cracks of the idle gear to the maximum safe crack number of the idle gear, and then determine the loss degree of the crack number of the idle gear;

[0068] S303. Statistically count the number of cracks of the idle gear whose depth is in each idle gear depth evaluation standard, and calculate the proportion of the crack number corresponding to each idle gear depth evaluation standard in the total number of cracks of the idle gear;

[0069] S304. Calculate the overall crack depth severity of the idle gear according to the weight corresponding to each idle gear depth evaluation standard and the proportion of the crack number corresponding to each idle gear depth evaluation standard in the total number of cracks of the idle gear;

[0070] S305. Calculate the severity t of the idle gear crack according to the loss degree of the idle gear crack number and the overall crack depth severity of the idle gear l ; the severity t of the idle gear crack l :

[0071]

[0072] In the formula, β 1 represents the weight corresponding to the loss degree of the idle gear crack number, QTY t represents the loss degree of the idle gear crack number at time t, β 2 represents the weight corresponding to the overall crack depth severity of the idle gear, l t represents the overall crack depth severity of the idle gear at time t, δ t represents the ratio of the total number of cracks of the idle gear at time t to the maximum safe crack number of the idle gear, ω1 Denote the influence weight of safety standard I on the wear loss of the idle gear as D 1 Denote the proportion of the number of cracks corresponding to safety standard I in the total number of cracks of the idle gear as ω 2 Denote the influence weight of relatively dangerous standard II on the wear loss of the idle gear as D 2 Denote the proportion of the number of cracks corresponding to relatively dangerous standard II in the total number of cracks of the idle gear as ω 3 Denote the influence weight of severely dangerous standard III on the wear loss of the idle gear as D 3 Denote the proportion of the number of cracks corresponding to severely dangerous standard III in the total number of cracks of the idle gear.

[0073] Among them, the evaluation criteria for the crack depth of the idle gear are as follows:

[0074] When the crack h of the idle gear ≤ γ 1 it proves that the crack depth of the idle gear is at safety standard I;

[0075] When the crack of the idle gear is γ 1 < h ≤ γ 2 it proves that the crack depth of the idle gear is at relatively dangerous standard II;

[0076] When the crack h of the idle gear > γ 2 it proves that the crack depth of the idle gear is at severely dangerous standard III.

[0077] S4. Calculate the severity of the end face wear of the idle gear according to the prediction information of the end face wear of the idle gear finite element model. Specifically:

[0078] Based on the idle gear finite element model, use Romax to analyze the wear of the idle gear and calculate the wear amount of the idle gear;

[0079] Calculate the ratio of the calculated wear amount of the idle gear to the preset maximum safe wear amount of the idle gear, and then determine the severity of the end face wear of the idle gear; the severity of the end face wear of the idle gear:

[0080]

[0081] In the formula, VY t Denote the severity of the end face wear of the idle gear at time t, ρ t Denote the ratio of the wear amount of the idle gear to the preset maximum safe wear amount of the idle gear.

[0082] Among them, the wear amount of the idle gear: mo t = 0.5 * lm t + 0.3 * Vm t + 0.2 * mz;

[0083] Linear wear rate:

[0084] Volume wear rate:

[0085] In the formula, Δht represents the wear depth of the idler at time t, v m represents the wear volume of the idler at time t, and mz represents the surface roughness of the idler.

[0086] S5. Construct an idler degradation model based on the severity of the idler crack and the severity of the idler end face wear, and use the idler degradation model to predict the idler life.

[0087] Among them, the idler degradation model:

[0088] X(t) = X 0 + a 1 tl + a 2 t m + σ B B(t);

[0089] In the formula, X(t) represents the loss life of the idler at time t, X 0 represents the loss life of the idler in the initial state, a 1 represents the weight of the first degradation trend term, t l represents the severity of the idler crack described by the first degradation trend term, a 2 represents the weight of the second degradation trend term, t m represents the severity of the idler end face wear described by the second degradation trend term, σ B represents the fluctuation degree during the idler degradation, and B(t) represents the inherent variability of the idler random degradation process over time as a standard Brownian motion. The fluctuation degree during the idler degradation is calculated by the maximum likelihood estimation method based on the measured idler loss life time series.

[0090] Embodiment 2

[0091] An idler life prediction system based on digital twin, including:

[0092] A finite element construction module, which constructs an idler finite element model according to the gear transmission system parameters and applies boundary conditions according to the gear material properties;

[0093] A dynamic model construction module, which constructs a gear system dynamic model according to the type of idler;

[0094] Idle gear degradation simulation module, which determines the stress distribution information received by the idle gear by solving the dynamic model of the gear system, and predicts and analyzes cracks and end face wear in the idle gear finite element model according to the stress conditions of the idle gear, that is, inputting the idle gear finite element model into the fracture analysis software, introducing initial cracks, and performing crack simulation analysis on the idle gear under the stress distribution information received by the gear; analyzing the wear of the idle gear using Romax based on the idle gear finite element model;

[0095] Idle gear degradation evaluation module, which calculates the severity of the cracks in the idle gear based on the crack prediction information of the idle gear finite element model; calculates the severity of the end face wear of the idle gear according to the end face wear prediction information of the idle gear finite element model;

[0096] Idle gear life prediction, which constructs an idle gear degradation model based on the severity of the cracks in the idle gear and the severity of the end face wear of the idle gear, and uses the idle gear degradation model to predict the life of the idle gear; Idle gear degradation model:

[0097] X(t) = X 0 +a 1 tl+a 2 t m +σ B B(t);

[0098] In the formula, X(t) represents the loss life of the idle gear at time t, X 0 represents the loss life of the idle gear in the initial state, a 1 represents the weight of the first degradation trend term, t l represents that the first degradation trend term describes the severity of the cracks in the idle gear, a 2 represents the weight of the second degradation trend term, t m represents that the second degradation trend term describes the severity of the end face wear of the idle gear, σ B represents the degree of fluctuation during the degradation of the idle gear, and B(t) represents the inherent variability of the random degradation process of the idle gear over time as a standard Brownian motion.

[0099] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0100] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for predicting idler wheel life based on digital twin, characterized in that: The steps of the idler gear life prediction method include: Construct an idler gear finite element model and a gear system dynamics model according to the gear transmission system parameters; Determine the stress distribution information of the idler gear by solving the gear system dynamics model, and perform crack and end face wear prediction analysis on the idler gear finite element model according to the stress condition of the idler gear; Based on the crack prediction information of the idler gear finite element model, calculate the severity of the idler gear crack; According to the end face wear prediction information of the idler gear finite element model, calculate the severity of the idler gear end face wear; Construct an idler gear degradation model according to the severity of the idler gear crack and the severity of the idler gear end face wear, and use the idler gear degradation model to predict the idler gear life.

2. The idler life prediction method based on digital twin according to claim 1 is characterized in that: Input the idler gear finite element model into the fracture analysis software, introduce an initial crack, and perform crack simulation analysis on the idler gear under the stress distribution information of the wheel; perform wear analysis on the idler gear using Romax based on the idler gear finite element model.

3. The idler life prediction method based on digital twin according to claim 1 is characterized in that: The steps for calculating the severity of the idler gear crack include: Based on the crack prediction information of the idler gear finite element model, count the total number of idler gear cracks; Calculate the ratio of the total number of idler gear cracks to the maximum safe crack number of the idler gear, and then determine the degree of idler gear crack number loss; Count the number of idler gear cracks with depths in each idler gear depth evaluation standard, and calculate the proportion of the crack number corresponding to each idler gear depth evaluation standard in the total number of idler gear cracks; Calculate the overall severity of the idler gear crack depth according to the weight corresponding to each idler gear depth evaluation standard and the proportion of the crack number corresponding to each idler gear depth evaluation standard in the total number of idler gear cracks; Calculate the severity of the idler gear crack according to the degree of idler gear crack number loss and the overall severity of the idler gear crack depth.

4. The idler life prediction method based on digital twin according to claim 3 is characterized in that: The idler gear crack depth evaluation standard is: When the idler gear crack h ≤ γ1, it proves that the idler gear crack depth is in safety standard I; When the idler gear crack γ1 < h ≤ γ2, it proves that the idler gear crack depth is in relatively dangerous standard II; When the idler gear crack h > γ2, it proves that the idler gear crack depth is in severe danger III.

5. The idler life prediction method based on digital twin according to claim 1 is characterized in that: The severity of the idler crack t l : Where β1 represents the weight corresponding to the number of idler cracks, QTY t represents the number of idler cracks at time t, β2 represents the weight corresponding to the severity of the overall depth of the idler crack, and l t Indicates the severity of the overall crack depth of the idler wheel at time t, δ t It represents the ratio of the total number of idler cracks to the maximum safe number of idler cracks at time t, ω1 represents the influence weight of safety standard I in the idler life loss, D1 represents the ratio of the number of cracks corresponding to the idler crack depth at safety standard I to the total number of idler cracks, ω2 represents the influence weight of relatively dangerous standard II in the idler life loss, D2 represents the ratio of the number of cracks corresponding to the relatively dangerous standard II in the idler crack depth to the total number of idler cracks, ω3 represents the influence weight of severe danger III in the idler life loss, D3 represents the ratio of the number of cracks corresponding to the severe danger III in the idler crack depth to the total number of idler cracks.

6. The idler life prediction method based on digital twin according to claim 1 is characterized in that: Perform wear analysis on the idler gear using Romax based on the idler gear finite element model, and calculate the idler gear wear amount; Calculate the ratio of the calculated idler gear wear amount to the preset maximum safe wear amount of the idler gear, and then determine the severity of the idler gear end face wear; The severity of the idler gear end face wear: Where VY t Indicates the severity of idler wheel end face wear at time t, ρ t Indicates the ratio of idler wheel wear to the preset maximum safe idler wheel wear.

7. The idler life prediction method based on digital twin according to claim 1 is characterized in that: The idler gear degradation model: X(t)=X0+a1t l +a2t m +σ B B(t); Where X(t) represents the idler wheel’s wear life at time t, X0 represents the idler wheel’s wear life at the initial state, a1 represents the weight of the first degradation trend term, and t l The first degradation trend term describes the severity of the idler crack, a2 represents the weight of the second degradation trend term, and t m The second degradation trend term describes the severity of idler end face wear, σ B represents the degree of fluctuation when the idler gear degrades, and B(t) represents the intrinsic variability of the random degradation process of the idler gear over time as a standard Brownian motion.

8. The idler life prediction method based on digital twin according to claim 1 is characterized in that: Calculate the fluctuation degree during idler gear degradation using the maximum likelihood estimation method according to the measured idler gear loss life time series.

9. An idler life prediction system based on digital twin, characterized in that: Includes: A finite element construction module that constructs an idler gear finite element model according to the gear transmission system parameters and applies boundary conditions according to the gear material properties; A dynamics model construction module that constructs a gear system dynamics model according to the idler gear type; An idler gear degradation simulation module that determines the stress distribution information of the idler gear by solving the gear system dynamics model, and performs crack and end face wear prediction analysis on the idler gear finite element model according to the stress condition of the idler gear; An idler gear degradation evaluation module that calculates the severity of the idler gear crack based on the crack prediction information of the idler gear finite element model; Calculate the severity of the idler gear end face wear according to the end face wear prediction information of the idler gear finite element model; Idler wheel life prediction, an idler wheel degradation model is constructed according to the severity of idler wheel cracks and the severity of idler wheel end face wear, and the idler wheel life is predicted using the idler wheel degradation model.

10. A storage medium for storing computer executable instructions, characterized in that: When executed, the computer executable instructions implement a method for predicting the life of an idler pulley based on digital twins as described in any one of claims 1 to 8.

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