A method for conveniently identifying fatigue damage of a vehicle structure

Abstract

A kind of method for conveniently identifying the fatigue damage of automobile structure of user route, comprising the following steps:1, the model establishment of IRI identification user road grade;2, based on IRI and u, road condition is refined classification;3, the full channel load spectrum of refined classification road condition is collected as material spectrum on road simulation test bench, and its fatigue damage is calculated;4, the mileage and proportion corresponding to each road condition refined classification are collected and calculated by detection vehicle;5, the results obtained in steps 3, 4 are multiplied and then added, so that the total fatigue damage of automobile structure under the working condition of user route can be obtained.The present application only needs to obtain the material load spectrum of target vehicle under grade excitation in indoor test bench;The detection vehicle of user field does not need to be equipped with a large number of sensors, the configuration scheme is simplified, the failure rate is greatly reduced, the maintenance is convenient, the field operation time is greatly shortened;The road condition refined classification of two-dimensional variables of road grade and vehicle speed is established, the precision of fatigue damage identification can be controlled, and good precision is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automobile, more particularly to a method for conveniently identifying the fatigue damage of automobile structure along user route. BACKGROUND

[0002] Obtaining and identifying the fatigue damage of structure along user route is an important work link in the development of automobile product, is important data for evaluating the influence of user working condition on structure, is a prerequisite for calculating the intensification coefficient of intensified reliability test, and is an important condition for determining the mileage of reliability test and evaluating the reliability of parts.

[0003] At present, the existing method for identifying the fatigue damage of automobile structure along user route needs to collect full-channel load spectrum on site, and a large number of strain gauges, strain flowers, displacement sensors, acceleration sensors and a large number of data transmission lines arranged on the sample car are easy to be damaged, and the maintenance conditions are poor and the maintenance time is long on site, resulting in more labor hours and low work efficiency. Therefore, it is urgent to study a replacement method to conveniently and effectively obtain the fatigue damage along user route. SUMMARY

[0004] The present application provides a method for conveniently identifying the fatigue damage of automobile structure along user route to overcome the shortcomings of the existing method for identifying the fatigue damage of automobile structure along user route, which needs to collect full-channel load spectrum on site, and a large number of sensors need to be attached and data lines need to be arranged on the sample car, which are easy to be damaged, resulting in more labor hours and low work efficiency.

[0005] The present application adopts the following technical scheme:

[0006] A method for conveniently identifying the fatigue damage of automobile structure along user route comprises the following steps:

[0007] S1, model establishment for identifying the road surface grade of IRI;

[0008] S2, detailed classification of road conditions based on IRI and u;

[0009] S3, collecting full-channel load spectrum of detailed classification of road conditions on a road simulation bench as material spectrum and calculating the fatigue damage thereof;

[0010] S4, collecting and calculating the mileage and proportion corresponding to each detailed classification of road conditions by a detection vehicle;

[0011] S5, multiplying and adding the fatigue damage of the material spectrum of step S3 and the mileage and proportion corresponding to each detailed classification of road conditions calculated by the detection vehicle of step S4, so as to obtain the total fatigue damage of automobile structure under the working condition of user route.

[0012] In a preferred embodiment, the specific process of step S1 above is as follows: (1) convert the spatial spectrum into a time frequency domain spectrum, and then into a time domain excitation spectrum; (2) play the excitation spectrum on the road simulation test bench and obtain the initial load spectrum of the target vehicle, including sprung and unsprung acceleration and displacement; (3) calculate the absolute value of the sprung and unsprung relative velocity based on the sprung and unsprung acceleration and displacement of the initial load spectrum, and then integrate it; calculate the corresponding vehicle mileage L using the set vehicle speed and time, divide the integration result by the mileage, and finally obtain a series of IRI data; (4) based on the series of IRI data calculated from the initial load spectrum and the corresponding road surface roughness Gq(n0) data, perform IRI=a*(G q (n0)) b Fit the data to obtain the values ​​of a and b.

[0013] Specifically, the time-domain excitation spectrum of step (1) above is obtained through the following formula and method:

[0014] f = un, W = 2, Where: G q (n) represents the spatial power spectral density of the road excitation space; G q (f) represents the time-domain power spectral density of the road excitation, m 2 / Hz; G q (n0) is the road surface roughness coefficient; n0 is the reference spatial frequency; n is the spatial frequency, m -1 u represents vehicle speed in m / s; f represents time-domain frequency; different values ​​of Gq(n0) and u are selected and set to obtain the power spectral density G under different road surface grades and vehicle speeds. q (f) Using IST TWR software, the frequency domain spectrum is converted into a time domain signal. The time length should be 1 kilometer divided by the set vehicle speed to finally obtain the time domain excitation spectrum of the road simulation test bench.

[0015] In a preferred embodiment, the specific process of step S2 above is as follows: (1) Divide each road surface grade into K levels, where K is a power of 2; (2) Refine the vehicle speed u into equal intervals from u1 to u2. N N is greater than 1; draw a detailed road condition classification diagram composed of detailed road surface grade lines and speed lines, that is, a detailed road condition classification diagram with IRI and u as coordinate axes; (3) determine the level of grade refinement according to the calculation accuracy and efficiency requirements, and thus determine the values ​​of K and N.

[0016] Furthermore, the sensor configuration used by the probe vehicle in step S4 above includes GPS, accelerometer and displacement sensor.

[0017] In a preferred embodiment, the specific process of step S4 is as follows: (1) by detecting the GPS speed signal channel in the vehicle load spectrum, integrating into the mileage signal channel, the load spectrum is equally divided by mileage; (2) for the equally divided load spectrum, the IRI value and the average vehicle speed are calculated, denoted as (IRI x , u y ), and these data points are plotted on the road condition refinement classification map; (3) if the data points (IRI x , u y ) satisfy and (u j-1 +u j ) / 2 y <(u j +u j+1 ) / 2, it is determined that (IRI x , u y ) falls into the interval governed by (IRI i , u j ), and the damage corresponding to (IRI x , u y ) is determined as the damage corresponding to (IRI i , u j ); (4) the number of points falling into the interval governed by (IRI i , u j ) is calculated, so as to obtain the mileage corresponding to the refined road condition level; (5) based on the mileage falling into each interval, the proportion of each level of road condition is calculated.

[0018] In a preferred embodiment, step S5 is calculated by using the following formula: D=D ij *m ij , wherein D is the total damage of the identified user route, D ij is the damage corresponding to (IRI i , u j ), which is calculated from the target vehicle material spectrum; m ij is the mileage falling into the area governed by (IRI i , u j ) identified by the detection vehicle.

[0019] As can be seen from the above description of the present application, compared with the prior art, the present application has the following advantages:

[0020] 1. This invention provides a method for identifying fatigue damage to vehicle structures along user routes. It involves acquiring load spectra (material spectra) of all levels across the entire route from the target vehicle and calculating fatigue damage at each measuring point. A detection vehicle used to identify the user's road surface level calculates the mileage and proportion of road sections at different levels. Multiplying these two values ​​yields the total damage to the target vehicle along the user route. This invention only requires acquiring the material load spectra of the target vehicle under different excitation levels on an indoor test bench. The detection vehicle at the user's site does not require a large number of strain sensors or other sensors prone to problems or failures, avoiding the difficulties of on-site maintenance in remote user locations. It simplifies sensor configuration, greatly reduces the probability of malfunctions, facilitates maintenance, significantly shortens on-site operation time, and reduces personnel and travel expenses. This establishes a convenient and effective method for acquiring fatigue damage to vehicle structures along user routes. It is of great significance for quickly acquiring structural fatigue damage under multiple measuring points in remote user road conditions, calculating the relationship between enhanced reliability testing and user operating conditions, and comparing structural fatigue damage across multiple regions and routes.

[0021] 2. This invention divides all road surface grades into K levels, and further refines the vehicle speed u into equal intervals from u1 to u... N Then, a detailed road condition classification diagram consisting of refined pavement grade lines and speed lines is drawn, and the degree of refinement is determined according to the calculation accuracy and efficiency requirements. This method of refining road conditions based on IRI and u greatly improves accuracy, is controllable, and is highly operable. Attached Figure Description

[0022] Figure 1 This is a flowchart of the present invention.

[0023] Figure 2 This is a detailed road condition level map for the present invention.

[0024] Figure 3 This is a detailed road condition level map for Example 1.

[0025] Figure 4 for Figure 3 A magnified partial view. Detailed Implementation

[0026] Specific embodiments of the present invention will now be described with reference to the accompanying drawings. Many details are described below to provide a comprehensive understanding of the invention; however, those skilled in the art will not need these details to implement the invention. Well-known components, methods, and processes will not be described in detail below.

[0027] A convenient method for identifying fatigue damage to vehicle structures based on user routes, referring to Figure 1 The specific steps are as follows:

[0028] I. Model establishment for IRI to identify user road surface level.

[0029] (1) Time-domain spectrum compilation and generation for road simulation test bench: The spatial spectrum is converted into a time-frequency domain spectrum, and then into a time-domain excitation spectrum. Specifically, it is obtained through the following formulas and methods:

[0030] f = un, W = 2;

[0031] In the above formulas, G q (n) represents the spatial power spectral density of the road excitation space; G q (f) represents the time-domain power spectral density of the road excitation, m 2 / Hz; G q (n0) is the road surface roughness coefficient; n0 is the reference spatial frequency, 0.1m. -1 n is the spatial frequency, m -1 u is the vehicle speed, m / s; f is the time-domain frequency.

[0032] Choose and set different Gq(n0) and u, where Gq(n0) is 16*10. -6 64*10 -6 256*10 -6 …thus obtaining the power spectral density G under different road surface grades and vehicle speeds. q (f).

[0033] The frequency domain spectrum was converted into a time domain signal using IST TWR software. The time length was 1 kilometer divided by the set vehicle speed, and the time domain excitation spectrum of the road simulation test bench was finally obtained.

[0034] (2) Play the time-domain excitation spectrum of each road surface level and vehicle speed on the road simulation test bench, and obtain the initial load spectrum of the target vehicle (including sprung and unsprung acceleration and displacement).

[0035] (3) Based on the initial load spectrum, calculate the absolute value of the relative velocity between the spring and the unsprung surface, and then integrate it; calculate the corresponding mileage L (km), and finally obtain a series of IRI data.

[0036]

[0037] In the formula, IRI is the rating index, v u and v d These are the sprung absolute velocity and the unsprung absolute velocity, respectively, and L is the distance the prototype vehicle travels within time t.

[0038] (4) Based on the IRI results and data calculated from the initial load spectrum, for IRI=a*(G q (n0)) b Fit the data to obtain the values ​​of a and b.

[0039] II. Refine and classify road conditions based on IRI and u.

[0040] (1) Divide each road surface grade into K levels, where K is a power of 2; (2) Refine the vehicle speed u into equal intervals from u1 to u... N N is greater than 1; draw a detailed road condition classification diagram composed of detailed pavement grade lines and speed lines, i.e., a detailed road condition classification diagram with IRI and u as coordinate axes, such as... Figure 2 As shown; (3) Determine the level of refinement according to the calculation accuracy and efficiency requirements, and thus determine the values ​​of K and N.

[0041] Specifically, according to the "GB 7031-2005T Mechanical Vibration Road Surface Spectrum Measurement Data Report", the upper limits of road surface roughness grades A, B, C... are denoted as A+, B+, C+..., and the corresponding G values ​​for A+, B+, C+... are... q (n0) is 32, 128, 512...

[0042] To refine the pavement grade classification and improve accuracy, each pavement grade is further subdivided into K levels, where K is a power of 2, such as 2, 4, 8, 16, etc. The refined grade results are arranged as…A+(B0), B1, B2, B3…B K (B+)(C0), C1, C2, C3…C K (C+)(D0)...。 Let G be the interval between the subdivided levels. q If the ratio of (n0) is p, then p K =4, that is Therefore, A+'s G q When (n0) = 32, G of B1 q (n0) = 32*p, G of B+ q When (n0) = 128, C1's G q (n0) = 128*p, and so on.

[0043] Accordingly, the IRI of A+ is a*32 b B1's IRI = a * (32 * p) b B2's IRI = a*(32*p*p) b ...and so on. The IRIs at each level of refinement are numbered: A+ corresponds to IRI... K B+ corresponds to IRI 2K And so on. See the table below for details:

[0044]

[0045]

[0046] The vehicle speed is further subdivided into equal intervals, u1, u2…u N Draw a detailed road condition classification diagram consisting of detailed pavement grade lines and speed lines, i.e., a road condition classification diagram with IRI and u as the coordinate axes, such as... Figure 2 .

[0047] The degree of refinement is determined based on the required computational accuracy and efficiency, thereby determining the values ​​of K and N.

[0048] 3. Collect the full-channel load spectrum of detailed graded road conditions on the road simulation test bench as the material spectrum, and calculate its fatigue damage.

[0049] For (IRI) in the diagram i u j Following steps (1) and (2) in step one, the corresponding excitation spectrum is generated on the road simulation test bench, and the load spectrum of the target vehicle across all channels is obtained. The IRI is then calculated. i u j Structural fatigue damage during the process of material spectral damage, also known as material spectrum damage.

[0050] Fourth, the mileage and proportion corresponding to each detailed road condition level are collected and calculated using a detection vehicle.

[0051] For the exploration vehicle, a simple sensor configuration is used, including GPS, acceleration sensors, and displacement sensors, to collect load spectra of road conditions for remote users.

[0052] (1) By detecting the GPS speed signal channel in the vehicle load spectrum, integrating it into the mileage signal channel, the load spectrum is divided into equal mileage segments (every 1km).

[0053] (2) For load spectra at equivalent mileage, calculate the IRI value and average vehicle speed, denoted as (IRI). x u y ), and plot these data points on Figure 2 Detailed road condition classification map.

[0054] (3) If the data point (IRI) x u y ),satisfy and (u j-1 +u j ) / 2<u y <(u j +u j+1 If ) / 2, then (IRI) is determined. x u y ) fell into (IRI) i u j The area under its jurisdiction, (IRI) x u yThe corresponding damage was determined to be (IRI) i u j The corresponding damage, and so on.

[0055] (4) Calculate the fall-in (IRI) i u j The number of points in the area under its jurisdiction is used to obtain the mileage corresponding to the refined road condition level.

[0056] (5) Based on the mileage of each interval, the proportion of each road condition level is calculated.

[0057] 5. Multiply the fatigue damage data from step three by the mileage and proportion corresponding to each road condition refinement level calculated by the detection vehicle in step four, and then add them together to obtain the total fatigue damage of the vehicle structure under the user's route conditions. The following formula is used for calculation:

[0058] D = D ij *m ij In the formula, D represents the total damage to the identified user route. ij For the corresponding (IRI) i u j The damage was calculated from the target vehicle's material spectrum; m ij Identification of landings (IRI) by the detection vehicle i u j The mileage within the jurisdiction.

[0059] The present invention will be further described below with reference to Example 1.

[0060] Example 1

[0061] A convenient method for identifying fatigue damage in vehicle structures based on user routes, comprising the following steps:

[0062] 1. Road simulation test bench time-domain spectrum compilation and generation: The spatial spectrum is converted into a time-frequency domain spectrum, and then into a time-domain excitation spectrum; specifically, it is obtained through the following formulas and methods:

[0063]

[0064] f = un, W = 2;

[0065]

[0066] In the formula, G q (n) represents the spatial power spectral density of the road excitation space; G q (f) represents the time-domain power spectral density of the road excitation, m 2 / Hz; G q (n0) is the road surface roughness coefficient; n0 is the reference spatial frequency, 0.1m. -1n is the spatial frequency, m -1 u is the vehicle speed, m / s; f is the time-domain frequency.

[0067] Choose and set different Gq(n0) and u, where G q (n0) is taken as 16*10 -6 64*10 -6 256*10 -6 …vehicle speeds of 20 km / h, 30 km / h, 40 km / h… are used to obtain the power spectral density G under different road surface grades and vehicle speeds. q (f). For example, Class A 30km / h, G q (n0) is 16. Substituting this into the calculation of the road excitation power spectral density, we get:

[0068]

[0069] Using the nCode software, integrating Gq(f) with respect to f yields RMS = 3.64 * 10 -3 m.

[0070] The frequency domain signal was converted into a time domain signal using IST TWR software. The time length was set to 1km divided by the set vehicle speed of 30km / h, which is 120s. Finally, the time domain excitation spectrum of the road simulation test bench was obtained.

[0071] 2. Play the time-domain excitation spectrum of each road surface level and vehicle speed on the road simulation test bench, and obtain the initial load spectrum of the target vehicle (including sprung and unsprung acceleration and displacement).

[0072] 3. Based on the sprung and unsprung accelerations and displacements from the initial load spectrum, calculate the absolute values ​​of the relative velocities between the sprung and unsprung sections, and then integrate them. Using the set vehicle speed and time, calculate the corresponding vehicle mileage L (km). Divide the integration result by the mileage to obtain a series of IRI data. For example, the IRI for a Class A vehicle at 30 km / h is 1.64.

[0073]

[0074] In the formula, IRI is the rating index, v u and v d These are the sprung absolute velocity and the unsprung absolute velocity, respectively, and L is the distance the prototype vehicle travels within time t.

[0075] 4. Based on the series of IRI results calculated from the initial load spectrum and the corresponding Gq(n0) data, for IRI=a*(G q (n0)) b Fitting the data yields values ​​of a = 0.4 and b = 0.5. Therefore, IRI = 0.4 * (G q (n0))0.5 .

[0076] 5. According to the "GB 7031-2005T Mechanical Vibration Road Surface Spectrum Measurement Data Report", the upper limits of the road surface roughness grades A, B, C... are denoted as A+, B+, C+..., and the corresponding G values ​​for A+, B+, C+... are... q (n0) is 32, 128, 512...

[0077] 6. This implementation case focuses on a specific route with relatively good road conditions. Only three levels (A, B, and C) need to be considered, with the corresponding G ratings being A+, B+, and C+. q (n0) is 32, 128, 512.

[0078] To balance the need for both calculation accuracy and efficiency, the level of refinement was determined. Each pavement level was subdivided into 4 levels, and the refined level results were arranged as follows: A0, A1, A2, A3, A4(A+)(B0), B1, B2, B3, B4(B+)(C0), C1, C2, C3, C4(C+)(D0).

[0079] Denote the G between the subdivided levels q The ratio of (n0) is p, p K =4,

[0080] The table below shows the detailed classification results for each road surface.

[0081]

[0082] Based on the required computational accuracy and efficiency, and according to the road conditions of the analyzed object, vehicle speeds are refined at equal intervals, from low to high: 20 km / h, 30 km / h, 40 km / h, and 50 km / h. A two-dimensional schematic diagram consisting of refinement level lines and vehicle speed lines is drawn, i.e., a road condition refinement level diagram with coordinate axes IRI and u, as shown below. Figure 3 As shown.

[0083] 7. Select the road condition point (2.69, 30) in the schematic diagram, i.e., when IRI = 2.69 m / km and u = 30 km / h. Following steps 1 and 2, generate the corresponding time-domain excitation spectrum on the road simulation test bench and obtain the load spectrum of the target vehicle across the entire channel. Calculate the structural fatigue damage at the measuring point (2.69, 30), i.e., the material spectrum damage value, 9.32E-06. Similarly, calculate the structural fatigue damage at the measuring point (1.90, 40), i.e., the material spectrum damage value, 2.04E-06.

[0084] 8. For the probe vehicle, a simple sensor configuration is adopted, including GPS, accelerometer, and displacement sensor, to collect load spectra of road conditions from remote users. The selected sensors are easy to maintain.

[0085] 9. By detecting the GPS speed signal channel in the vehicle load spectrum, the speed signal channel is integrated into the mileage signal channel, which is 7 kilometers long. The load spectrum is then divided into 7 segments with equal mileage of 1 km each.

[0086] 10. Reference Figure 4 Among them, the area governed by the road condition level point (2.69, 30) in the figure is, And (20+30) / 2<u y <(30+40) / 2, that is, 2.46<IRI x <2.93 and 25<u y <35. Similarly, the region encompassed by point (1.90, 40) is 1.74 < IRI. x <2.07 and 35<u y <45. The areas covered by other road condition points follow the same principle.

[0087] 11. For each segment of the user route with a load spectrum of equal mileage (1km), calculate the IRI value and average vehicle speed, denoted as (IRI). x u y Plot these data points into... Figure 3 (IRI) x u y There are 4 data points that fall within the region of (2.69, 30). Figure 4 Thus, the mileage corresponding to this refined road condition level is 4km. At this time, the load spectrum damage corresponding to these data points falling into the area of ​​(2.69, 30) is equal to the damage 9.32E-06 corresponding to the center point, i.e. the road condition level point (2.69, 30).

[0088] Similarly, there are 3 points that fall within the interval (1.90, 40), such as... Figure 4 Thus, the corresponding mileage is 3km. The load spectrum damage corresponding to these data points falling within the area of ​​(1.90, 40) is equal to 2.04E-06.

[0089] 12. Based on the mileage falling into each interval, the proportion of each road condition level is calculated. For example, the mileage ratio of the two road condition levels (2.69, 30) and (1.90, 40) is 4:3.

[0090] 13. The total damage value of the load spectrum of the road segment falling within the area of ​​(2.69, 30) is 4*9.32E-06=3.73E-5. The total damage value of the load spectrum of the road segment falling within the area of ​​(1.90, 40) is 3*2.04E-06=6.12E-6.

[0091] 14. The structural damage over 7 kilometers of the entire route is 3.73E-5 + 6.12E-6 = 4.34E-5. This allows for the rapid identification of structural damage to the route.

[0092] 15. For the verification purposes of this implementation case, the target vehicle was driven along a 7km user route to the analysis object to obtain the actual strain data at the corresponding measuring points. The actual fatigue damage result was 4.21E-5. It can be seen that the ratio of the identified damage to the actual target damage is 4.34E-5 / 4.11E-5 = 1.05, which meets the predetermined range requirement of 0.5 to 2.

[0093] The above are merely specific embodiments of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial modifications made to the present invention using this concept shall be considered as infringing upon the protection scope of the present invention.

Claims

1. A method for conveniently identifying fatigue damage in a vehicle structure based on user routes, characterized in that, Includes the following steps: S1、 IRI Model establishment for identifying user road surface level; the specific process is as follows: (1) Convert the spatial spectrum into the time frequency domain spectrum, and then into the time domain excitation spectrum; the time domain excitation spectrum is obtained through the following formula and method: , f = un, W =2, In the formula: G q ( n ) represents the spatial power spectral density of the road surface excitation; Gq ( f ) represents the time-domain power spectral density of the road surface excitation, m 2 / Hz; G q ( n 0) represents the road surface roughness coefficient; n 0 Reference spatial frequency; n For spatial frequency, m -1 ; u Vehicle speed, in m / s; f For time-domain frequency; select and set different Gq ( n 0) u To obtain the power spectral density under different road surface grades and different vehicle speeds. G q ( f (1) Using IST TWR software, the frequency domain spectrum is converted into a time domain signal. The time length should be 1 kilometer divided by the set vehicle speed to finally obtain the time domain excitation spectrum of the road simulation test bench; (2) Play the excitation spectrum on the road simulation test bench and obtain the initial load spectrum of the target vehicle, including the sprung and unsprung acceleration and displacement; (3) Based on the sprung and unsprung acceleration and displacement of the initial load spectrum, calculate the absolute value of the sprung and unsprung relative velocity, and then integrate it; Calculate the corresponding vehicle mileage L using the set vehicle speed and time, divide the integration result by the mileage, and finally obtain a series of results. IRI Data; (4) A series of IRI data and corresponding pavement roughness calculated based on the initial load spectrum. G q ( n 0) Data, for Fitting, obtaining a , b value; S2, based on IRI and u The road conditions are further subdivided and classified; the specific process is as follows: (1) Each road surface grade is divided into K levels, where K is a power of 2; (2) The vehicle speed is... u Perform equal-interval refinement u 1 to u N N is greater than 1; draw a detailed road condition classification diagram consisting of detailed pavement grade lines and speed lines, i.e., the coordinate axes are... IRI , u (3) Determine the level of detail based on the calculation accuracy and efficiency requirements, and thus determine the values ​​of K and N; S3. Collect the full-channel load spectrum of the refined graded road conditions on the road simulation test bench as the material spectrum, and calculate its fatigue damage. S4. Collect and calculate the mileage and proportion corresponding to each road condition refinement level through the detection vehicle; S5. Multiply the fatigue damage from the material spectrum in step S3 with the mileage and proportion corresponding to each road condition refinement level calculated by the detection vehicle in step S4, and then add them together to obtain the total fatigue damage of the vehicle structure under the user's route conditions.

2. The method for conveniently identifying fatigue damage to a vehicle structure based on user routes as described in claim 1, characterized in that: The probe vehicle in step S4 uses a sensor configuration including GPS, accelerometer, and displacement sensor.

3. The method for conveniently identifying fatigue damage to a vehicle structure based on user routes as described in claim 2, characterized in that, The specific process of step S4 is as follows: (1) By detecting the vehicle speed signal channel of GPS in the vehicle load spectrum, integrating it into the mileage signal channel, and performing equal mileage segmentation on the load spectrum; (2) For the load spectrum with equal mileage, calculate IRI The numerical value and average vehicle speed are denoted as ( IRI x , u y ), and plot these data points on the detailed road condition classification map; (3) if the data points ( IRI x , u y ),satisfy ,and Then determine ( IRI x , u y )fall into( IRI i , u j The area under the jurisdiction of ) IRI x , u y The corresponding damage is determined to be ( ) IRI i , u j (4) Calculate the damage corresponding to falling into ( IRI i , u j (5) Calculate the proportion of each level of road condition based on the number of points in the area under its jurisdiction.

4. The method for conveniently identifying vehicle structural fatigue damage based on user routes as described in claim 3, characterized in that, The calculation in step S5 is performed using the following formula: In the formula, D To identify the total damage to the user's route, D ij for( IRI i , u j The corresponding damage is calculated from the target vehicle's material spectrum; m ij For the detection vehicle to identify the landing ( IRI i , u j The mileage within the jurisdiction.

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