A simulation vehicle sea shipping scene endurance test system and method

Abstract

This invention relates to the field of vehicle testing technology and discloses a durability testing system and method for simulating a vehicle's maritime transport scenario. The system includes a load spectrum acquisition and construction module, a load spectrum editing and processing module, an iterative logic operation module, and a bench execution engine module. By acquiring the original triaxial acceleration load spectrum of the vehicle's axle head and wheel hub, and controlling eight hydraulic actuators to synchronously output excitation based on the bench drive loading spectrum, four hydraulic actuators output vertically coupled excitation, and the other four hydraulic actuators output laterally coupled excitation after being converted by a tripod. Vertical and lateral coupled excitations are applied to the test vehicle, overcoming the limitation of existing bench loading systems that can only achieve vertical excitation in a single direction. It reproduces the lateral sway load generated by ship rolling, ensuring the consistency between the excitation input and the actual maritime transport vibration direction, thereby avoiding damage mode distortion caused by torque attenuation at vehicle suspension and body connection points and tire fixing points.

Description

Technical Field

[0001] This invention relates to the field of vehicle testing technology, specifically to a durability testing system and method for simulating a vehicle's maritime transport scenario. Background Technology

[0002] Today, with economic development, cars have become a common household item. Typically, vehicles undergo durability testing during the development phase. This involves driving a real vehicle in various real-world testing environments or scenarios, especially in test scenarios with extreme environmental factors, to determine the vehicle's durability parameters under certain operating conditions.

[0003] Currently, due to the random coupled vibrations of different sea conditions involved in the vehicle sea transport process, the existing bench loading system can often only achieve vertical excitation in a single direction when conducting whole vehicle sea transport durability tests. It cannot simultaneously reproduce the lateral sway load generated by the ship's rolling motion. When there is a deviation between the excitation input and the vibration direction of the actual sea transport, it will cause distortion of the damage mode of torque attenuation at the vehicle suspension and body connection points and tire fixing points, and the accuracy of the test results cannot be guaranteed.

[0004] Therefore, a durability testing system and method simulating a vehicle maritime transport scenario are proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a durability testing system and method for simulating vehicle maritime transport scenarios, which solves the problem mentioned in the background art of distortion of damage modes due to torque decay at vehicle suspension and body connection points and tire fixing points, thus failing to guarantee the accuracy of test results.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a durability testing system and method for simulating a vehicle maritime transport scenario, the method comprising the following steps: S1. Collect the original triaxial acceleration load spectrum of vehicle axle head and wheel hub under different sea conditions in the maritime scenario; S2. Perform low-load enhancement editing on the original triaxial acceleration load spectrum to generate an accelerated rainflow frequency spectrum; S3. Perform time-domain reconstruction on the frequency spectrum of accelerated rainflow to generate a pre-iteration time-domain load spectrum; S4. Perform full damage verification based on the pre-iterative time-domain load spectrum and the measured vehicle SN curve, and retain the damage editing to generate the target iterative load spectrum; S5. A dual-axis decoupling iterative strategy of vertical first and horizontal last is adopted to iteratively correct the target iterative load spectrum and generate the bench drive loading spectrum. S6. The test vehicle in the prepared state is subjected to vertical and lateral coupled excitation based on the bench-driven loading spectrum control test system to complete the endurance test in the maritime scenario.

[0007] Preferably, the step S1 of collecting the original triaxial acceleration load spectrum of the vehicle axle head and wheel hub under different sea conditions in a maritime scenario includes the following steps: S11. Install a triaxial acceleration sensor at each of the four wheel axle heads and the corresponding wheel hub positions on the side of the test vehicle. S12. Collect the original triaxial acceleration load spectrum under severe wind and wave conditions and calm wind and wave conditions during the sea voyage. The severe wind and wave conditions are divided into two sub-conditions: sailing with the wind and sailing against the wind. S13. Calculate the total mileage of the entire sea route and the mileage ratio of each working condition, and use the total mileage and the mileage ratio of each working condition as the basis data for extrapolating the test duration.

[0008] Preferably, generating the accelerated rainflow frequency spectrum in step S2 includes the following steps: S21. The material fatigue limit of the corresponding structural component of the test vehicle. Based on this, the loads in the original triaxial acceleration load spectrum are divided into three categories: ineffective micro-loads, strengthening low-loads, and damaging high-loads, among which the stress amplitude of ineffective micro-loads is less than 40%. Strengthen the stress amplitude under low loads to 40%. Up to 80% Between these points, the stress amplitude under high load damage is greater than 80%. ; S22. Remove all invalid microloads from the original triaxial acceleration load spectrum, retain all enhanced low loads and damaged high loads, perform rainflow counting statistics on the retained loads, and generate an accelerated rainflow frequency spectrum.

[0009] Preferably, generating the pre-iteration time-domain load spectrum in S3 includes the following steps: S31. Extract the load amplitude, load mean, and corresponding cycle number parameters from the frequency spectrum of accelerated rainflow; S32. Based on the rainflow matrix inverse algorithm, the accelerated rainflow frequency spectrum is reconstructed into a pre-iterative time-domain load spectrum with a continuous time-domain distribution.

[0010] Preferably, generating the target iterative load spectrum in S4 includes the following steps: S41. Based on the measured SN curves of the corresponding structural components of the test vehicle, calculate the full fatigue damage value of the pre-iteration time-domain load spectrum; S42. Using 100% full fatigue damage value as the verification benchmark, the pre-iteration time-domain load spectrum is accelerated and compressed twice to generate the target iterative load spectrum, wherein the time-domain length of the target iterative load spectrum is shorter than the time-domain length of the pre-iteration time-domain load spectrum.

[0011] Preferably, generating the bench drive loading spectrum in step S5 includes the following steps: S51. A dual-axis decoupling iteration strategy is adopted, with vertical direction first and horizontal direction second. S52. Using the Z-axis acceleration signals of the four wheel axle heads in the target iterative load spectrum as the first iteration target and the Z-axis acceleration signal of the corresponding wheel hub as the first monitoring target, execute the vertical iteration process until the root mean square error of the vertical iteration is less than the preset first convergence threshold. S53. Keep the driving signal output of the vertical iteration unchanged, take the Y-axis acceleration signal of the four wheel axle heads in the target iteration load spectrum as the second iteration target, and take the Y-axis acceleration signal of the corresponding wheel hub as the second monitoring target, and execute the lateral iteration process until the root mean square error of the lateral iteration is less than the preset second convergence threshold. S54. The driving signals output after the vertical and horizontal iterations are completed are used as the bench drive loading spectrum.

[0012] Preferably, both the vertical iteration process and the horizontal iteration process adopt an iterative method combining the frequency domain and the time domain, and a single iteration includes the following steps: S531. Input the nth frequency domain drive signal to control the test system to load and then collect the auto-power spectral density of the nth actual response signal. S532. Based on frequency domain iterative correction logic, the n+1th frequency domain driving signal is generated by using the system transfer function matrix to correct the difference between the self-power spectral density of the actual response signal and the self-power spectral density of the target response signal, where the self-power spectral density of the target response signal is the self-power spectral density of the corresponding iterative target in the target iterative load spectrum. S533. Convert the (n+1)th frequency domain driving signal into a time domain driving signal, and according to the time domain iterative correction logic, update the (k+1)th time domain driving signal by superimposing the product of the iterative convergence coefficient and the difference between the target and actual response signals on the kth time domain driving signal, wherein the value range of the iterative convergence coefficient is 0.3-0.8. S524. Repeat S521 to S523 until the root mean square error of the current iteration satisfies the condition of being less than the convergence threshold, wherein the root mean square error is calculated by the ratio of the difference between the target response signal and the actual response signal to the target response signal.

[0013] Preferably, the durability test in the maritime scenario in step S6 includes the following steps: S61. The test vehicle is hoisted onto the cross-shaped iron floor of the test platform in a ready state, and the body of the test vehicle is adjusted to be parallel and symmetrical with the central axis of the test platform. S62. The four wheels of the test vehicle are fixed to the test platform by the L-shaped connecting plate and the circular wheel fixing clamp, and the front and rear ends of the test platform are fixed to the iron floor ring chain support by two longitudinal cables. S63. Install four jack safety brackets in the non-working area below the test platform, and adjust the top of the jack safety brackets to the position where a preset gap is reserved between them and the bottom surface of the test platform. S64. Based on the bench-driven loading spectrum control test system, eight hydraulic actuators synchronously output excitations. Four hydraulic actuators output vertical coupling excitations, and four hydraulic actuators output lateral coupling excitations after being converted by a tripod. Vertical and lateral coupling excitations are applied to the test vehicle until the extrapolated test duration is reached, and then the test is stopped, thus completing the endurance test in the maritime scenario.

[0014] Preferably, the system includes: The load spectrum acquisition and construction module uses the sensor deployment unit to generate triaxial acceleration measurement points at the axle head and wheel hub of the test vehicle. The working condition classification acquisition unit sets the acquisition specifications for severe wind and waves and stable working conditions, and the shipborne monitoring unit outputs the original triaxial acceleration load spectrum. The load spectrum editing and processing module receives the original triaxial acceleration load spectrum, removes invalid micro-loads and retains enhanced low loads and damaged high loads through the low load classification screening unit, performs load frequency statistics using the rainflow counting and statistics unit, and outputs the accelerated rainflow frequency spectrum through the time domain reconstruction unit.

[0015] Preferably, the system further includes: The iterative logic operation module receives the accelerated rainflow frequency spectrum, calculates the fatigue damage value by combining the full damage verification unit with the measured SN curve, corrects the driving signal by the frequency domain and time domain coupled iterative unit, and outputs the bench drive loading spectrum by the convergence judgment unit. The bench execution engine module receives the bench drive loading spectrum, constructs a physical test environment including a cross-groove iron floor and a jack safety bracket through the test platform construction unit, fixes the test vehicle in the preparation state to the test platform using the vehicle attitude locking unit, and controls eight hydraulic actuators to synchronously output vertical and lateral coupling excitations through the dual-axis coupling drive unit.

[0016] Compared with the prior art, the present invention provides a durability testing system and method for simulating vehicle maritime transport scenarios, which has the following beneficial effects: 1. In this invention, the original triaxial acceleration load spectrum of the vehicle axle head and wheel hub is collected, and eight hydraulic actuators are controlled to output excitation synchronously based on the bench drive loading spectrum. Four hydraulic actuators output vertical coupling excitation, and the other four hydraulic actuators output lateral coupling excitation after being converted by a tripod. Vertical and lateral coupling excitations are applied to the test vehicle, which solves the limitation of existing bench loading systems that can only achieve vertical excitation in a single direction. It reproduces the lateral sway load generated by ship rolling, ensures the consistency between the excitation input and the actual vibration direction of sea transport, thereby avoiding damage mode distortion due to torque attenuation at vehicle suspension and body connection points and tire fixing points, and ensuring the accuracy of test results.

[0017] 2. In this invention, by adopting a dual-axis decoupled iteration strategy of vertical first and lateral second, and using the Z-axis and Y-axis acceleration signals of the shaft head and hub as the iteration target and monitoring target respectively, the spectral characteristics and time-domain waveform of the drive signal can be corrected successively, ensuring a high degree of fit between the test bench response signal and the target iterative load spectrum, reducing the signal deviation in the iteration process, and performing lateral iteration after vertical iteration convergence can avoid mutual interference of dual-axis vibration, ensure the accuracy of iterative control, and improve the realism of test loading.

[0018] 3. In this invention, by arranging a jack safety bracket under the test platform and monitoring the reserved gap between the jack safety bracket and the test platform in real time, the risk of equipment collision caused by large displacement vibration can be avoided in a timely manner, ensuring the operational stability of the test platform. At the same time, during the load spectrum editing stage, the enhanced low load is retained and the invalid micro load is eliminated, which can shorten the test time while ensuring damage equivalence. Furthermore, the test vehicle is fixed by the cross-groove iron floor and the vehicle attitude locking unit, ensuring the reliability of the test clamping and improving the overall efficiency and safety of durability testing in maritime scenarios. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for simulating a vehicle's durability test in a maritime scenario, according to the present invention. Figure 2 This is a structural diagram of a vehicle durability testing system simulating a maritime transport scenario according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] For specific implementation examples, please refer to: Figures 1-2A durability testing system and method for simulating a vehicle maritime transport scenario, the method comprising the following steps: S1. Collect the original triaxial acceleration load spectrum of vehicle axle head and wheel hub under different sea conditions in the maritime scenario; S2. Perform low-load enhancement editing on the original triaxial acceleration load spectrum to generate an accelerated rainflow frequency spectrum; S3. Perform time-domain reconstruction on the frequency spectrum of accelerated rainflow to generate a pre-iteration time-domain load spectrum; S4. Perform full damage verification based on the pre-iterative time-domain load spectrum and the measured vehicle SN curve, and retain the damage editing to generate the target iterative load spectrum; S5. A dual-axis decoupling iterative strategy of vertical first and horizontal last is adopted to iteratively correct the target iterative load spectrum and generate the bench drive loading spectrum. S6. The test vehicle in the prepared state is subjected to vertical and lateral coupled excitation based on the bench-driven loading spectrum control test system to complete the endurance test in the maritime scenario.

[0022] The process of collecting the original triaxial acceleration load spectrum of vehicle axle head and wheel hub under different sea conditions in S1 includes the following steps: S11. Install a triaxial acceleration sensor at each of the four wheel axle heads and the corresponding wheel hub positions on the side of the test vehicle. S12. Collect the original triaxial acceleration load spectrum under severe wind and wave conditions and calm wind and wave conditions during the sea voyage. The severe wind and wave conditions are divided into two sub-conditions: sailing with the wind and sailing against the wind. S13. Calculate the total mileage of the entire sea route and the mileage ratio of each working condition, and use the total mileage and the mileage ratio of each working condition as the basis data for extrapolating the test duration.

[0023] The steps involved in generating the accelerated rainflow frequency spectrum in S2 are as follows: S21. The material fatigue limit of the corresponding structural component of the test vehicle. Based on this, the loads in the original triaxial acceleration load spectrum are divided into three categories: ineffective micro-loads, strengthening low-loads, and damaging high-loads, among which the stress amplitude of ineffective micro-loads is less than 40%. Strengthen the stress amplitude under low loads to 40%. Up to 80% Between these points, the stress amplitude under high load damage is greater than 80%. ; S22. Remove all invalid microloads from the original triaxial acceleration load spectrum, retain all enhanced low loads and damaged high loads, perform rainflow counting statistics on the retained loads, and generate an accelerated rainflow frequency spectrum.

[0024] Generating the pre-iteration time-domain load spectrum in S3 includes the following steps: S31. Extract the load amplitude, load mean, and corresponding cycle number parameters from the frequency spectrum of accelerated rainflow; S32. Based on the rainflow matrix inverse algorithm, the accelerated rainflow frequency spectrum is reconstructed into a pre-iterative time-domain load spectrum with a continuous time-domain distribution, specifically including the following steps: S321. Read the rainflow matrix in the accelerated rainflow frequency spectrum and extract the parameter combination of each group of load amplitude, load mean and corresponding cycle number in the rainflow matrix; S322. According to the closure criterion of rainflow counting, the extracted parameter combination is converted into a load segment with a continuous time variable. The time length of the load segment is proportional to the corresponding number of cycles. S323. Randomly arrange and splice all load segments according to the actual occurrence probability of the maritime conditions to generate a continuous time-domain signal. Normalize the time-domain signal into a voltage time series that can be recognized by the test system to obtain the pre-iteration time-domain load spectrum. The relationship between the load amplitude and the number of cycles in the rainflow matrix is ​​characterized by the following formula: S22. Remove all invalid microloads from the original triaxial acceleration load spectrum, retain all enhanced low loads and damaged high loads, perform rainflow counting statistics on the retained loads, and generate an accelerated rainflow frequency spectrum.

[0025] Generating the pre-iteration time-domain load spectrum in S3 includes the following steps: S31. Extract the load amplitude, load mean, and corresponding cycle number parameters from the frequency spectrum of accelerated rainflow; S32. Based on the rainflow matrix inverse algorithm, the accelerated rainflow frequency spectrum is reconstructed into a pre-iterative time-domain load spectrum with a continuous time-domain distribution, specifically including the following steps: S321. Read the rainflow matrix in the accelerated rainflow frequency spectrum and extract the parameter combination of each group of load amplitude, load mean and corresponding cycle number in the rainflow matrix; S322. According to the closure criterion of rainflow counting, the extracted parameter combination is converted into a load segment with a continuous time variable. The time length of the load segment is proportional to the corresponding number of cycles. S323. Randomly arrange and splice all load segments according to the actual occurrence probability of the maritime conditions to generate a continuous time-domain signal. Normalize the time-domain signal into a voltage time series that can be recognized by the test system to obtain the pre-iteration time-domain load spectrum. The relationship between the load amplitude and the number of cycles in the rainflow matrix is ​​characterized by the following formula: ; In the formula, For the first The number of cycles for the level load spectral block. For the rainflow matrix operator, For the first time-domain load spectrum in the pre-iteration The original stress amplitude of each load cycle, For the first The average stress value of the load spectrum block; When reconstructing the time-domain signal, S324 determines the number of periods of a single frame of the time-domain waveform based on the number of loops. The specific formula is as follows: ; In the formula, For the first Duration of the time-domain waveform segment corresponding to the level load spectral block. For the first The number of cycles for the level load spectral block. This is the preset reconstructed reference frequency.

[0026] Generating the target iterative load spectrum in S4 includes the following steps: S41. Based on the measured SN curves of the corresponding structural components of the test vehicle, calculate the full fatigue damage value of the pre-iteration time-domain load spectrum, specifically including the following steps: S411. Obtain the measured SN curves of the corresponding structural components of the test vehicle, and extract the slope coefficient and material constants of the SN curves. The measured SN curves satisfy a power-law relationship, and the specific formula is as follows: Generating the target iterative load spectrum in S4 includes the following steps: S41. Based on the measured SN curves of the corresponding structural components of the test vehicle, calculate the full fatigue damage value of the pre-iteration time-domain load spectrum, specifically including the following steps: S411. Obtain the measured SN curves of the corresponding structural components of the test vehicle, and extract the slope coefficient and material constants of the SN curves. The measured SN curves satisfy a power-law relationship, and the specific formula is as follows: ; In the formula, This represents the number of fatigue cycles corresponding to the stress amplitude. The material constants for the SN curve, For stress amplitude in any scenario, The slope coefficient of the SN curve; S412. Perform rainflow counting on the pre-iteration time-domain load spectrum, extract the stress amplitude and mean stress of the i-th independent load cycle. When the measured SN curve is the calibration curve under a specific average stress, perform average stress correction on the stress amplitude based on the mean stress to obtain the equivalent stress amplitude. S413. Based on Miner's linear cumulative damage theory, calculate the damage value corresponding to the i-th load cycle. The specific formula is as follows: ; In the formula, For the first Damage value corresponding to each load cycle For the first The number of fatigue cycles corresponding to each load cycle. For the first The equivalent stress amplitude after average stress correction for each load cycle The slope coefficient of the SN curve. The material constants for the SN curve; S414. Summate the damage values ​​of all independent load cycles to obtain the full fatigue damage value of the pre-iteration time-domain load spectrum. The specific formula is as follows: ; In the formula, This represents the total fatigue damage value. This represents the total number of independent load cycles contained in the pre-iteration time-domain load spectrum. For the first Damage values ​​corresponding to each load cycle; S42. Using 100% full fatigue damage as the verification benchmark, the pre-iteration time-domain load spectrum is subjected to secondary acceleration and compression to generate the target iterative load spectrum, wherein the time-domain length of the target iterative load spectrum is shorter than the time-domain length of the pre-iteration time-domain load spectrum. Specifically, this includes the following steps: S421. Set the ratio of the target test duration to the original total sea transport time, and define the acceleration ratio. The specific formula is as follows: ; In the formula, To accelerate the multiplier, This refers to the total duration of the entire sea freight journey. The total time preset for bench testing; S422. Based on the ratio of the total fatigue damage value to the acceleration rate, determine the target damage threshold after the second accelerated compression, using the following formula: ; In the formula, The target damage threshold, This represents the full fatigue damage value of the pre-iteration time-domain load spectrum. To accelerate the multiplier; S423. Keeping the phase relationship and mean stress of each load cycle in the pre-iteration time-domain load spectrum unchanged, the stress amplitude of all load cycles is scaled synchronously proportionally, with a scaling factor of [value missing]. The specific formula is as follows: ; ; In the formula, This is the amplitude scaling factor. To accelerate the multiplier, The slope coefficient of the measured SN curve. For the scaled-up number Stress amplitude per load cycle, For the first time-domain load spectrum in the pre-iteration The original stress amplitude of each load cycle; S424. Extract the amplitude-scaled time-domain signal to a preset test duration, generate the target iterative load spectrum, and verify that its damage value meets the requirements. ,in This represents the full fatigue damage value after scaling the load spectrum. The target damage threshold.

[0027] Generating the bench-driven loading spectrum in S5 includes the following steps: S51. A dual-axis decoupling iteration strategy is adopted, with vertical direction first and horizontal direction second. S52. Using the Z-axis acceleration signals of the four wheel axle heads in the target iterative load spectrum as the first iteration target and the Z-axis acceleration signal of the corresponding wheel hub as the first monitoring target, execute the vertical iteration process until the root mean square error of the vertical iteration is less than the preset first convergence threshold. S53. Keep the driving signal output of the vertical iteration unchanged, take the Y-axis acceleration signal of the four wheel axle heads in the target iteration load spectrum as the second iteration target, and take the Y-axis acceleration signal of the corresponding wheel hub as the second monitoring target, and execute the lateral iteration process until the root mean square error of the lateral iteration is less than the preset second convergence threshold. S54. The driving signals output after the vertical and horizontal iterations are completed are used as the bench drive loading spectrum.

[0028] Both the vertical and horizontal iteration processes employ a combination of frequency and time domain iterations. A single iteration includes the following steps: S531. Input the nth frequency domain drive signal to control the test system to load and then collect the auto-power spectral density of the nth actual response signal. S532. Based on frequency domain iterative correction logic, the (n+1)th frequency domain driving signal is generated using the system transfer function matrix to correct the difference between the self-power spectral density of the actual response signal and the self-power spectral density of the target response signal, wherein the self-power spectral density of the target response signal is the self-power spectral density of the corresponding iterative target in the target iterative load spectrum. Specifically, this includes the following steps: S5321. Calculate the ratio of the self-power spectral density of the target response signal to the self-power spectral density of the actual response signal, and generate the frequency domain correction factor spectrum. The formula for the frequency domain correction factor spectrum is as follows: ; In the formula, For the frequency domain correction factor spectrum, The self-power spectral density of the target response signal, For the first The self-power spectral density of the actual response signal For frequency variables; S5322. Call the pre-identified system transfer function matrix, solve for its inverse matrix, and obtain the system inverse transfer function matrix; The formula for the system inverse transfer function matrix is ​​as follows: ; In the formula, The inverse transfer function matrix of the system. The system transfer function matrix; S5323. Perform point-by-point multiplication of the nth frequency domain driving signal with the frequency domain correction factor spectrum, and then correct it through the system inverse transfer function matrix to generate the (n+1)th frequency domain driving signal. The formula for the (n+1)th frequency domain driving signal is as follows: ; In the formula, For the first Secondary frequency domain drive signal For the first Secondary frequency domain drive signal For the frequency domain correction factor spectrum, The inverse transfer function matrix of the system; S533. Convert the (n+1)th frequency domain driving signal into a time domain driving signal, and based on the time domain iterative correction logic, update and generate the (k+1)th time domain driving signal by superimposing the product of the iterative convergence coefficient and the difference between the target and actual response signals onto the k-th time domain driving signal. The value of the iterative convergence coefficient ranges from 0.3 to 0.8. Specifically, this includes the following steps: S5331. Convert the frequency domain driving signal into a time domain driving signal through inverse Fourier transform, and use it as the k-th driving signal. S5332. Based on the time-domain iterative correction logic, the k+1th time-domain driving signal is updated by superimposing the product of the iterative convergence coefficient and the difference between the target and actual response signals onto the kth time-domain driving signal. The specific formula is as follows: ; In the formula, For the first Sub-time domain drive signal For the first Sub-time domain drive signal The convergence coefficients are the iterative convergence coefficients. For the target response signal, For the first Secondary response signal; S524. Repeat steps S521 to S523 until the root mean square error of the current iteration is less than the convergence threshold. The root mean square error is calculated as the ratio of the difference between the target response signal and the actual response signal to the target response signal. This includes the following steps: S5241. Input the initial drive signal, collect the actual response signal after the test bench is loaded, and calculate the response deviation value by comparing it with the target response. The specific calculation formula is as follows: ; In the formula, In response to the deviation value, For the target response signal, This is the actual response signal; S5242. Substitute the driving signal into the iterative formula, repeatedly load and continuously correct the iterative deviation until the iterative convergence root mean square error reaches the target value, and the iteration ends. The specific calculation formula is as follows: ; In the formula, This is the error threshold, typically set at 5%-10%. In response to the deviation value, This is the target response signal.

[0029] The S6 process for completing a maritime scenario durability test includes the following steps: S61. The test vehicle is hoisted onto the cross-shaped iron floor of the test platform in a ready state, and the body of the test vehicle is adjusted to be parallel and symmetrical with the central axis of the test platform. S62. The four wheels of the test vehicle are fixed to the test platform by the L-shaped connecting plate and the circular wheel fixing clamp, and the front and rear ends of the test platform are fixed to the iron floor ring chain support by two longitudinal cables. S63. Install four jack safety brackets in the non-working area below the test platform, and adjust the top of the jack safety brackets to the position where a preset gap is reserved between them and the bottom surface of the test platform. S64. Based on the bench-driven loading spectrum control test system, eight hydraulic actuators synchronously output excitations. Four hydraulic actuators output vertical coupling excitations, and four hydraulic actuators output lateral coupling excitations after being converted by a tripod. Vertical and lateral coupling excitations are applied to the test vehicle until the extrapolated test duration is reached, and then the test is stopped, thus completing the endurance test in the maritime scenario.

[0030] The system includes: The load spectrum acquisition and construction module uses the sensor deployment unit to generate triaxial acceleration measurement points at the axle head and wheel hub of the test vehicle. The working condition classification acquisition unit sets the acquisition specifications for severe wind and waves and stable working conditions, and the shipborne monitoring unit outputs the original triaxial acceleration load spectrum. The load spectrum editing and processing module receives the original triaxial acceleration load spectrum, removes invalid micro-loads through the low-load classification screening unit and retains the enhanced low-load and damaged high-load, performs load frequency statistics using the rainflow counting and statistics unit, and outputs the accelerated rainflow frequency spectrum through the time domain reconstruction unit.

[0031] The system also includes: The iterative logic operation module receives the frequency spectrum of accelerated rainflow, calculates the fatigue damage value by combining the full damage verification unit with the measured SN curve, corrects the driving signal by the frequency domain and time domain coupled iterative unit, and outputs the bench drive loading spectrum by the convergence judgment unit. The bench execution engine module receives the bench drive loading spectrum, constructs a physical test environment including a cross-groove iron floor and a jack safety bracket through the test platform building unit, uses the vehicle attitude locking unit to fix the test vehicle in the preparation state to the test platform, and controls eight hydraulic actuators to synchronously output vertical and lateral coupling excitation through the dual-axis coupling drive unit.

[0032] The operation steps of a durability testing system and method simulating a vehicle maritime transport scenario are as follows: Step 1: Multi-condition load spectrum acquisition in maritime scenarios: A triaxial acceleration sensor was installed at each of the four wheel axle positions and the corresponding wheel hub positions on the test vehicle. Raw triaxial acceleration load spectra were collected during the sea voyage under both severe and calm sea conditions. The severe sea condition was further divided into two sub-conditions: sailing with the wind and sailing against the wind. The total mileage of the entire sea voyage and the mileage percentage for each condition were statistically analyzed. These total mileage and mileage percentages were used as the basis for extrapolating the subsequent test duration, ultimately outputting raw triaxial acceleration load spectra that include characteristics of multiple conditions.

[0033] Step 2: Load spectrum classification and accelerated rainflow frequency spectrum generation: The material fatigue limit of the corresponding structural components of the test vehicle Based on this, the loads in the original triaxial acceleration load spectrum are divided into three categories: stress amplitude less than 40%. Ineffective micro-load, stress amplitude within 40% Up to 80% Strengthening under low load, stress amplitude greater than 80% Damage-prone high loads. All invalid microloads in the original triaxial acceleration load spectrum are removed, and all reinforcing low loads and damage-prone high loads are retained. Rainflow counting statistics are performed on the retained loads to generate an accelerated rainflow frequency spectrum.

[0034] Step 3: Accelerate the time-domain reconstruction of the rainflow frequency spectrum: The load amplitude, load mean, and corresponding cycle number parameters are extracted from the accelerated rainflow frequency spectrum. Based on the rainflow matrix inverse algorithm, the accelerated rainflow frequency spectrum is reconstructed into a pre-iterative time-domain load spectrum with a continuous time-domain distribution.

[0035] Step 4: Full damage verification and target iterative load spectrum generation based on SN curves: Based on the measured SN curves of the corresponding structural components of the test vehicle, the full fatigue damage value of the pre-iteration time-domain load spectrum is calculated. Using the 100% full fatigue damage value as a verification benchmark, the pre-iteration time-domain load spectrum is subjected to secondary acceleration and compression to generate the target iterative load spectrum, wherein the time-domain length of the target iterative load spectrum is shorter than that of the pre-iteration time-domain load spectrum.

[0036] Step 5: Dual-axis decoupling iteration and bench-driven loading spectrum generation: A dual-axis decoupled iterative strategy, with vertical acceleration preceding lateral acceleration, is employed to correct the target iterative load spectrum. First, vertical iteration is performed using the Z-axis acceleration signals of the four wheel axle heads as the first iteration target and the corresponding Z-axis acceleration signals of the wheel hub as the first monitoring target, until the root mean square error (RMSE) is less than a first convergence threshold. Keeping the vertical output signals unchanged, lateral iteration is performed using the Y-axis acceleration signals of the axle heads as the second iteration target and the corresponding Y-axis acceleration signals of the wheel hub as the second monitoring target, until the RMS error is less than a second convergence threshold. The iteration employs a control method combining frequency and time domains, using the ratio of the target response to the actual response as the convergence criterion. Finally, the drive signals output from the two iterations are synthesized into the bench drive loading spectrum.

[0037] Step Six: Perform vertical and lateral coupled excitation durability tests: The test vehicle was hoisted onto the cross-groove iron floor of the test platform in a ready-to-use state. The vehicle body was adjusted to be parallel and symmetrically arranged with the central axis of the test platform. The four wheels of the test vehicle were fixed to the test platform using L-shaped connecting plates and circular wheel fixing clamps. The front and rear ends of the test platform were fixed to the ring chain supports of the iron floor using two longitudinal cables. Four jack safety brackets were set up in the non-working area below the test platform, and the tops of the jack safety brackets were adjusted to a position with a preset gap between them and the bottom surface of the test platform. Based on the bench-driven loading spectrum control test system, eight hydraulic actuators synchronously output excitations. Four hydraulic actuators output vertically coupled excitations, and four hydraulic actuators output laterally coupled excitations after being converted by a tripod. Vertical and lateral coupled excitations were applied to the test vehicle until the extrapolated test duration was reached, at which point the test stopped, completing the maritime scenario durability test.

[0038] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0039] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A durability test method simulating a vehicle maritime transport scenario, characterized in that: The method includes the following steps: S1. Collect the original triaxial acceleration load spectrum of vehicle axle head and wheel hub under different sea conditions in the maritime scenario; S2. Perform low-load enhancement editing on the original triaxial acceleration load spectrum to generate an accelerated rainflow frequency spectrum; S3. Perform time-domain reconstruction on the frequency spectrum of accelerated rainflow to generate a pre-iteration time-domain load spectrum; S4. Perform full damage verification based on the pre-iterative time-domain load spectrum and the measured vehicle SN curve, and retain the damage editing to generate the target iterative load spectrum; S5. A dual-axis decoupling iterative strategy of vertical first and horizontal last is adopted to iteratively correct the target iterative load spectrum and generate the bench drive loading spectrum. S6. The test vehicle in the prepared state is subjected to vertical and lateral coupled excitation based on the bench-driven loading spectrum control test system to complete the endurance test in the maritime scenario.

2. The method for simulating vehicle maritime transport durability testing according to claim 1, characterized in that: The process of collecting the original triaxial acceleration load spectrum of the vehicle axle head and wheel hub under different sea conditions in S1 includes the following steps: S11. Install a triaxial acceleration sensor at each of the four wheel axle heads and the corresponding wheel hub positions on the side of the test vehicle. S12. Collect the original triaxial acceleration load spectrum under severe wind and wave conditions and calm wind and wave conditions during the sea voyage. The severe wind and wave conditions are divided into two sub-conditions: sailing with the wind and sailing against the wind. S13. Calculate the total mileage of the entire sea route and the mileage percentage of each working condition, and use the total mileage and the mileage percentage of each working condition as the basis for extrapolating the test duration.

3. The method for simulating vehicle maritime transport durability testing according to claim 2, characterized in that: The process of generating the accelerated rainflow frequency spectrum in S2 includes the following steps: S21. The material fatigue limit of the corresponding structural component of the test vehicle. Based on this, the loads in the original triaxial acceleration load spectrum are divided into three categories: ineffective micro-loads, strengthening low-loads, and damaging high-loads, among which the stress amplitude of ineffective micro-loads is less than 40%. Strengthen the stress amplitude under low loads to 40%. Up to 80% Between these points, the stress amplitude under high load damage is greater than 80%. ; S22. Remove all invalid microloads from the original triaxial acceleration load spectrum, retain all enhanced low loads and damaged high loads, perform rainflow counting statistics on the retained loads, and generate an accelerated rainflow frequency spectrum.

4. The method for simulating a vehicle's durability test in a maritime scenario according to claim 3, characterized in that: The process of generating the pre-iteration time-domain load spectrum in S3 includes the following steps: S31. Extract the load amplitude, load mean, and corresponding cycle number parameters from the frequency spectrum of accelerated rainflow; S32. Based on the rainflow matrix inverse algorithm, the accelerated rainflow frequency spectrum is reconstructed into a pre-iterative time-domain load spectrum with a continuous time-domain distribution.

5. The method for simulating a vehicle's durability test in a maritime scenario according to claim 4, characterized in that: The generation of the target iterative load spectrum in S4 includes the following steps: S41. Based on the measured SN curves of the corresponding structural components of the test vehicle, calculate the full fatigue damage value of the pre-iteration time-domain load spectrum; S42. Using 100% full fatigue damage value as the verification benchmark, the pre-iteration time-domain load spectrum is accelerated and compressed twice to generate the target iterative load spectrum, wherein the time-domain length of the target iterative load spectrum is shorter than the time-domain length of the pre-iteration time-domain load spectrum.

6. The method for simulating a vehicle's durability test in a maritime scenario according to claim 5, characterized in that: The process of generating the bench drive loading spectrum in S5 includes the following steps: S51. A dual-axis decoupling iteration strategy is adopted, with vertical direction first and horizontal direction second. S52. Using the Z-axis acceleration signals of the four wheel axle heads in the target iterative load spectrum as the first iteration target and the Z-axis acceleration signal of the corresponding wheel hub as the first monitoring target, execute the vertical iteration process until the root mean square error of the vertical iteration is less than the preset first convergence threshold. S53. Keep the driving signal output of the vertical iteration unchanged, take the Y-axis acceleration signal of the four wheel axle heads in the target iteration load spectrum as the second iteration target, and take the Y-axis acceleration signal of the corresponding wheel hub as the second monitoring target, and execute the lateral iteration process until the root mean square error of the lateral iteration is less than the preset second convergence threshold. S54. The driving signals output after the vertical and horizontal iterations are completed are used as the bench drive loading spectrum.

7. The method for simulating a vehicle's durability test in a maritime scenario according to claim 6, characterized in that: Both the vertical and horizontal iteration processes employ a combination of frequency and time domain iterations. A single iteration includes the following steps: S531. Input the nth frequency domain drive signal to control the test system to load and then collect the auto-power spectral density of the nth actual response signal. S532. Based on frequency domain iterative correction logic, the n+1th frequency domain driving signal is generated by using the system transfer function matrix to correct the difference between the self-power spectral density of the actual response signal and the self-power spectral density of the target response signal, where the self-power spectral density of the target response signal is the self-power spectral density of the corresponding iterative target in the target iterative load spectrum. S533. Convert the (n+1)th frequency domain driving signal into a time domain driving signal, and according to the time domain iterative correction logic, update the (k+1)th time domain driving signal by superimposing the product of the iterative convergence coefficient and the difference between the target and actual response signals on the kth time domain driving signal, wherein the value range of the iterative convergence coefficient is 0.3-0.

8. S524. Repeat S521 to S523 until the root mean square error of the current iteration meets the condition of being less than the convergence threshold, wherein the root mean square error is calculated by the ratio of the difference between the target response signal and the actual response signal to the target response signal.

8. The method for simulating a vehicle's durability test in a maritime scenario according to claim 6, characterized in that: The S6 step of completing the maritime scenario durability test includes the following steps: S61. The test vehicle is hoisted onto the cross-shaped iron floor of the test platform in a ready state, and the body of the test vehicle is adjusted to be parallel and symmetrical with the central axis of the test platform. S62. The four wheels of the test vehicle are fixed to the test platform by the L-shaped connecting plate and the circular wheel fixing clamp, and the front and rear ends of the test platform are fixed to the iron floor ring chain support by two longitudinal cables. S63. Install four jack safety brackets in the non-working area below the test platform, and adjust the top of the jack safety brackets to the position where a preset gap is reserved between them and the bottom surface of the test platform. S64. Based on the bench-driven loading spectrum control test system, eight hydraulic actuators synchronously output excitations. Four hydraulic actuators output vertical coupling excitations, and four hydraulic actuators output lateral coupling excitations after being converted by a tripod. Vertical and lateral coupling excitations are applied to the test vehicle until the extrapolated test duration is reached, and then the test is stopped, thus completing the endurance test in the maritime scenario.

9. A durability testing system simulating a vehicle maritime transport scenario, used to implement the durability testing method simulating a vehicle maritime transport scenario as described in any one of claims 1-8, characterized in that: The system includes: The load spectrum acquisition and construction module uses the sensor deployment unit to generate triaxial acceleration measurement points at the axle head and wheel hub of the test vehicle. The working condition classification acquisition unit sets the acquisition specifications for severe wind and waves and stable working conditions, and the shipborne monitoring unit outputs the original triaxial acceleration load spectrum. The load spectrum editing and processing module receives the original triaxial acceleration load spectrum, removes invalid micro-loads and retains enhanced low loads and damaged high loads through the low load classification screening unit, performs load frequency statistics using the rainflow counting and statistics unit, and outputs the accelerated rainflow frequency spectrum through the time domain reconstruction unit.

10. A durability testing system for simulating a vehicle maritime transport scenario according to claim 9, characterized in that: The system also includes: The iterative logic operation module receives the accelerated rainflow frequency spectrum, calculates the fatigue damage value by combining the full damage verification unit with the measured SN curve, corrects the driving signal by the frequency domain and time domain coupled iterative unit, and outputs the bench drive loading spectrum by the convergence judgment unit. The bench execution engine module receives the bench drive loading spectrum, constructs a physical test environment including a cross-groove iron floor and a jack safety bracket through the test platform construction unit, fixes the test vehicle in the preparation state to the test platform using the vehicle attitude locking unit, and controls eight hydraulic actuators to synchronously output vertical and lateral coupling excitations through the dual-axis coupling drive unit.

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