Vehicle endurance reliability test data processing method and device and storage medium

Abstract

This disclosure discloses a method, apparatus, and storage medium for processing vehicle durability and reliability test data, relating to the field of vehicle engineering technology. The method for processing vehicle durability and reliability test data includes: determining multiple fault parameters based on vehicle test records, including the vehicle equivalent failure rate, which is determined based on fault severity, maintenance complexity, and functional impact; determining the weight of each fault parameter based on test objectives, user scenarios, and fault distribution, wherein the fault distribution is the mileage or time point at which the fault occurred; and determining the vehicle's quality inspection evaluation value based on the multiple fault parameters and their weights.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle engineering technology, and in particular to a method, apparatus and storage medium for processing vehicle durability and reliability test data. Background Technology

[0002] Vehicle reliability testing is a crucial step in ensuring the quality and performance of vehicles and their components. It is a core means of verifying the performance of a vehicle throughout its entire lifecycle, covering a full range of scenarios from extreme environments to daily use. By simulating extreme environments (high temperature, low temperature, vibration, corrosion, etc.) and long-term use scenarios, it exposes potential defects in vehicles in advance, ensuring their safe and stable operation throughout their entire lifecycle. Summary of the Invention

[0003] One objective of this disclosure is to improve the accuracy of vehicle durability and reliability test data processing and analysis.

[0004] According to one aspect of some embodiments of this disclosure, a method for processing vehicle durability and reliability test data is proposed, comprising: determining multiple fault parameters based on vehicle test records, the fault parameters including a vehicle equivalent failure rate, the vehicle equivalent failure rate being determined based on fault severity, maintenance complexity, and functional impact; determining the weight of each fault parameter based on test objectives, user scenarios, and fault distribution, wherein the fault distribution is the mileage or time point at which the fault occurred; and determining the vehicle's quality inspection evaluation value based on the multiple fault parameters and their weights.

[0005] In some embodiments, determining the weight of each fault parameter includes: determining a first weight of each fault parameter based on the test objective; determining a second weight of each fault parameter based on the user scenario; determining a third weight of each fault parameter based on the fault distribution; and determining the weight of each fault parameter based on the first weight, the second weight, the third weight, and the influencing factor.

[0006] In some embodiments, if the test objective is durability verification, the first failure record value and the average failure parameter have higher first weights among the failure parameters; if the test objective is maintainability verification, the maintenance effectiveness assessment value has higher first weight among the failure parameters; if the test objective is comprehensive acceptance, the first weights of each failure parameter are equal.

[0007] In some embodiments, if the user scenario is long-haul logistics, the second weight of the first fault record value and the average fault parameter is higher; if the user scenario is engineering or mining work, the second weight of the vehicle equivalent failure rate is higher; if the user scenario is urban delivery, the second weight of the maintenance efficiency assessment value is higher.

[0008] In some embodiments, if the number of failures is high from the start of the test to the first predetermined distance or the first predetermined time length, the third weight of the first failure record value in the failure parameters is higher; if the number of failures is high from the end of the test to the second predetermined distance or the second predetermined time length, the third weight of the vehicle equivalent failure rate in the failure parameters is higher.

[0009] In some embodiments, determining multiple fault parameters based on vehicle test records includes: determining the equivalent value of the fault based on fault severity, maintenance complexity, and functional impact; and determining the vehicle equivalent failure rate based on the equivalent value of the fault and the test length, wherein the test length includes at least one of test mileage or test duration.

[0010] In some embodiments, the fault parameters further include a maintenance performance evaluation value, which is determined based on the baseline maintenance time and actual maintenance time of the fault.

[0011] In some embodiments, determining multiple fault parameters based on vehicle test records includes: for each fault, obtaining the fault repair efficiency based on the fault's baseline repair time and actual repair time; and determining the average repair efficiency based on the repair efficiency of each fault and the number of faults, wherein the repair efficiency evaluation value includes the average repair efficiency.

[0012] In some embodiments, determining multiple fault parameters based on vehicle test records includes: determining the sum of baseline repair times for faults and the sum of actual repair times for faults; determining the percentage of total effective repair time for faults based on the sum of baseline repair times and the sum of actual repair times for faults, wherein the repair efficiency evaluation value includes the percentage of total effective repair time.

[0013] In some embodiments, the fault parameters further include at least one of the mean fault interval and the first fault record value, wherein the first fault record value is the mileage or time at which the first fault occurred.

[0014] In some embodiments, the method further includes: determining the weak link of the vehicle based on fault parameters, including at least one of the following: determining the weak mode, system or component based on the equivalent value of the fault; determining the fault that is difficult to repair based on the repair efficiency assessment value of the fault, wherein the repair efficiency assessment value of the fault is determined based on the baseline repair time and actual repair time of the fault; or determining the high-incidence stage of the fault based on the first fault record value and the fault distribution.

[0015] According to one aspect of some embodiments of this disclosure, a vehicle durability and reliability test data processing apparatus is proposed, comprising: a fault parameter determination unit configured to determine multiple fault parameters based on vehicle test records, the fault parameters including a vehicle equivalent failure rate, the vehicle equivalent failure rate being determined based on fault severity, maintenance complexity, and functional impact; a weight determination unit configured to determine the weight of each fault parameter based on one or more of the test objective, user scenario, or fault distribution, wherein the fault distribution is the mileage or time point at which the fault occurred; and a result determination unit configured to determine the vehicle's quality inspection evaluation value based on the multiple fault parameters and their weights.

[0016] In some embodiments, the device further includes: a fault analysis unit configured to determine the weak points of the vehicle based on fault parameters, including at least one of the following: determining the weak mode, system, or component based on the equivalent value of the fault, wherein the equivalent value of the fault is determined based on the severity of the fault, maintenance complexity, and functional impact; determining faults that are difficult to repair based on the maintenance effectiveness assessment value of the fault, wherein the maintenance effectiveness assessment value of the fault is based on the baseline maintenance time and actual maintenance time of the fault; or determining the high-incidence stage of the fault based on the first fault record value and fault distribution.

[0017] According to one aspect of some embodiments of this disclosure, a vehicle durability and reliability test data processing apparatus is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to execute any of the vehicle durability and reliability test data processing methods described above based on instructions stored in the memory.

[0018] According to one aspect of some embodiments of the present disclosure, a computer-readable storage medium is provided that stores computer instructions which, when executed by a processor, implement any of the durability and reliability test data processing methods described above.

[0019] According to one aspect of some embodiments of this disclosure, a computer program product is proposed, including a computer program or instructions that, when executed by a processor, implement any of the durability and reliability test data processing methods described above.

[0020] Based on the embodiments shown above, the quantification of fault differences during vehicle testing is realized. The quantified fault differences are intelligently integrated with multiple indicators such as maintenance efficiency assessment, which improves the comprehensive assessment capability of vehicle quality and the accuracy of vehicle quality test results. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this disclosure and form part of this disclosure, illustrate exemplary embodiments of this disclosure and are used to explain this disclosure, but do not constitute an undue limitation of this disclosure.

[0022] Figure 1 This is a flowchart of some embodiments of the vehicle durability and reliability test data processing method disclosed herein.

[0023] Figure 2 Flowcharts are shown for some other embodiments of the vehicle durability and reliability test data processing method disclosed herein.

[0024] Figure 3 These are schematic diagrams of some embodiments of the vehicle durability and reliability test data processing apparatus disclosed herein.

[0025] Figure 4 These are schematic diagrams of other embodiments of the vehicle durability and reliability test data processing apparatus disclosed herein.

[0026] Figure 5 These are schematic diagrams of some further embodiments of the vehicle durability and reliability test data processing apparatus disclosed herein. Detailed Implementation

[0027] The technical solutions of this disclosure will be further described in detail below with reference to the accompanying drawings and embodiments.

[0028] In relevant vehicle testing, MTBF (Mean Time Between Failure), failure rate, or repair time are often calculated separately. The weights of each indicator in the evaluation system are based on static, empirical settings, resulting in low accuracy in assessing overall vehicle reliability. Standards such as QC / T 900 "Methods for Quality Inspection and Evaluation of Complete Automobile Products" mainly specify basic calculation methods, but they do not address issues such as fault variability, indicator fusion, and dynamic weighting, and lack a systematic and comprehensive approach.

[0029] To address the aforementioned issues, this disclosure proposes a vehicle durability and reliability test data processing method, apparatus, and storage medium for comprehensive evaluation after various vehicle tests, particularly for new energy commercial vehicles, following the completion of the overall vehicle reliability test. It can dynamically generate fault parameter weights that match the test results, improving the matching degree between vehicle evaluation results and tests, and enhancing the accuracy of overall vehicle reliability evaluation.

[0030] Flowcharts of some embodiments of the vehicle durability and reliability test data processing method disclosed herein are as follows: Figure 1 As shown.

[0031] In step S13, multiple fault parameters are determined based on the vehicle test records. These parameters include the vehicle equivalent failure rate. The vehicle equivalent failure rate is determined based on the severity of the fault, the complexity of maintenance, and the functional impact, thus providing a multi-dimensional assessment of the vehicle equivalent failure rate and improving its accuracy and reference value in vehicle fault evaluation.

[0032] In some embodiments, vehicle test records can be collected as basic data to execute step S13. The vehicle test records include information on the types, times, and mileage of various events that occurred during the test. In some embodiments, they also include vehicle parameter information, test setting information, etc., providing sufficient data for subsequent analysis and helping to improve the accuracy of the analysis results.

[0033] In some embodiments, such as Figure 2 As shown in step 210, the vehicle test record is obtained. The vehicle test record includes fault records, maintenance records, test length, and vehicle parameter information (such as vehicle model, configuration, load, etc.). Among them, the test length (T) total This includes at least one of the following: test mileage or test duration. This method allows for the collection of as much relevant test information as possible, providing a sufficient data foundation for subsequent analysis and improving the reliability of that analysis. In some embodiments, test operating condition information, such as the proportion of enhanced road spectrum and special environmental conditions, can also be obtained to provide more comprehensive data and further enhance the reliability of subsequent analysis.

[0034] For example, the fault records obtained include the mileage or time when the fault occurred, the location of the fault, the description of the fault mode, the cause of the fault (preliminary analysis), and the severity level of the fault (e.g., according to preset standards, such as: 1-minor, 2-moderate, 3-severe, 4-fatal).

[0035] For example, obtaining detailed maintenance records, including the start time, end time, duration, personnel, replacement parts list, and description of maintenance measures for each fault.

[0036] In some embodiments, the method for obtaining the vehicle equivalent failure rate may be as follows: Figure 2 As shown in steps 231-232.

[0037] In step 231, the equivalent value E of the fault is determined based on the severity of the fault, the complexity of maintenance, and the functional impact. i Here, 'i' represents the fault identifier. The higher the fault severity, the greater the repair complexity, and the greater the functional impact, the higher the corresponding fault equivalent value. In related technologies, fault rate statistics often treat all faults equally, ignoring the significant differences between different fault modes, leading to distorted evaluation results. The method described in the above embodiments can improve the realism and accuracy of vehicle detection.

[0038] In some embodiments, a fault equivalent coefficient K can be set for each common fault. i The equivalent fault value is obtained by summing the fault equivalent coefficients of all faults that occur during the test. In some embodiments, a fault equivalent coefficient (K) can be established. iA database / model is provided for querying and use, where i is a fault identifier. In some embodiments, multiple dimensions, such as fault severity S, can be considered. i Maintenance complexity O i and functional impact degree D i Three dimensions to determine K i The fault equivalence coefficient can be dynamically adjusted according to the test scenario, thereby improving the environmental adaptability of fault analysis and further enhancing accuracy. In some embodiments, K i >=1.

[0039] The above S i The assessment is based on the degree of failure, including safety, regulatory compliance, and loss of key functions.

[0040] The above O i The assessment is based on factors such as the time required for standard repair procedures, tool complexity, and skill requirements. For example, replacing light bulb K. o =1.0, replace engine K o =5.0.

[0041] The above D i The assessment is based on the duration of vehicle downtime or the degree of functional degradation caused by the malfunction. For example, if it does not affect driving (K...). d =1.0, causing anchor K to break down. d =3.0.

[0042] Equivalent value E for each fault i =K i .

[0043] In some embodiments, a fault equivalent coefficient can be calculated based on a preset algorithm, taking into account fault severity, maintenance complexity, and functional impact. The preset algorithm can be set or adjusted as needed; for example, it can be set as a weighted sum of fault severity, maintenance complexity, and functional impact, or K can be obtained by looking up a predefined fault mode-equivalent coefficient mapping table. i Or through formula K i =f(S i O i D i The result is obtained through calculation, where f is a linear or nonlinear function, etc.

[0044] In some embodiments, the fault and the fault severity S i Maintenance complexity O i and functional impact degree D i Fault equivalent coefficient K i The correspondence can be shown in Table 1.

[0045] Table 1 Fault Equivalent Coefficient

[0046]

[0047] In some embodiments, storage fault and fault equivalent coefficient K can be generated and maintained. i A corresponding database, or a database storing faults and their severity levels (S). i Maintenance complexity O i and functional impact degree D i A database of corresponding relationships, or faults and fault severity S. i Maintenance complexity O i and functional impact degree D i Fault equivalent coefficient K i This database contains the corresponding relationships for easy querying, improving data reliability and ease of use.

[0048] When a vehicle experiences multiple faults, the equivalent value N of the overall vehicle faults is... eq =ΣE i This is the summation of the equivalent values ​​of all faults that occur in the vehicle.

[0049] In step 232, the vehicle equivalent failure rate is determined based on the equivalent value of the fault and the test length, wherein the test length includes at least one of test mileage or test duration.

[0050] In some embodiments, the equivalent value of the fault can be correlated with the test length (T). total The ratio of λ to λ is used as the vehicle's equivalent failure rate, for example, the vehicle's equivalent failure rate λ. eq =N eq / T total (Units are: equivalent number of failures / thousand kilometers, equivalent number of failures or thousand hours).

[0051] Based on the methods in the above embodiments, the differences between different faults are taken into consideration. The faults are analyzed from the perspectives of fault severity, maintenance complexity, and functional impact. The impact differences of different faults are quantified and unified, solving the problem of counting faults of different severity equally. The evaluation results are more consistent with the actual reliability level of the vehicle, improving the objectivity and accuracy of vehicle fault analysis, and helping to provide a reliable data foundation for vehicle improvement.

[0052] In some embodiments, the fault parameters include the mean time between failures, such as... Figure 2 As shown in step 233, the mean time between failures (MTBF) is determined based on the test length and the number of failures. For example, the ratio of the test length to the number of failures is calculated as the mean time between failures, i.e., MTBF = T. total / N (where N is the total number of actual failures, not the equivalent number of failures). This method retains traditional indicators and reflects the frequency of failures.

[0053] In some embodiments, fault parameters include the Maintenance Effectiveness Assessment (MTE) value, such as... Figure 2 As shown in step 234, the repair efficiency evaluation value of the fault is determined based on the baseline repair time and actual repair time of the fault.

[0054] In some embodiments, the maintenance performance evaluation value includes the average maintenance performance η. avg In step 234, for each fault, the repair efficiency can be obtained based on the baseline repair time and the actual repair time. Then, based on the repair efficiency of each fault and the number of faults, the average repair efficiency is determined. The baseline repair time can be dynamically adjusted according to the test scenario, thereby improving the environmental adaptability of fault analysis and further enhancing accuracy.

[0055] For example, calculate the maintenance efficiency index η for each fault. i :

[0056] η i =T benchmark_i / T actual_i

[0057] Among them, T actual_i T is the actual repair time for fault i. benchmark_j This is the baseline repair time for fault i. η i >1 indicates that the maintenance efficiency is higher than the benchmark; η i <1 indicates that the efficiency is lower than the benchmark.

[0058] The average maintenance efficiency η of the whole vehicle avg =(Ση i ) / N. In some embodiments, η avg It can also be η i The weighted average value, with weights set and adjusted based on experience or needs.

[0059] In some embodiments, a baseline maintenance time can be preset based on experience. The baseline maintenance time can be set or adjusted as needed. Table 2 shows the baseline maintenance time corresponding to the maintenance operations for the faults. The data in this table are merely examples of feasible embodiments and do not constitute an undue limitation on this disclosure.

[0060] Table 2. Baseline maintenance time for maintenance operations

[0061]

[0062] In some embodiments, a database of the correspondence between maintenance operations and baseline maintenance times can be generated and maintained to improve the accuracy and ease of use of the data.

[0063] In some embodiments, the maintenance effectiveness assessment value includes the total effective maintenance time percentage (TE%). In step 234, the sum of the baseline maintenance times for the fault and the sum of the actual maintenance times for the fault can be determined. Then, based on the sum of the baseline maintenance times and the sum of the actual maintenance times for the fault, the total effective maintenance time percentage for the fault is determined. For example, the ratio of the sum of the baseline maintenance times to the sum of the actual maintenance times for the fault is used as the total effective maintenance time percentage (TE%), i.e., TE% = (ΣT benchmark_i ) / Total Repair_Time *100%, of which Total Repair_Time This represents the total actual repair time for the fault.

[0064] This method allows us to evaluate the efficiency and rationality of the maintenance process, avoiding the sacrifice of quality for the sake of short-term gains, or exposing design flaws that make maintenance inconvenient.

[0065] Based on the method in the embodiments shown above, the maintenance efficiency evaluation value is incorporated into the scope of vehicle fault analysis. The vehicle fault is evaluated from the aspects of maintenance difficulty and maintenance efficiency. Combined with other fault parameters, the effect after maintenance can be further considered, such as the possibility of fault recurrence. It not only focuses on "whether it is broken" and "how long it will break", but also scientifically evaluates "how easy it is to repair", comprehensively reflecting the product reliability and maintainability, thereby further improving the comprehensiveness and reliability of fault analysis.

[0066] In some embodiments, the fault parameters include the initial fault record value, such as... Figure 2 As shown in step 235, the mileage FFM of the first failure or the time FFT of the first failure is obtained. The first failure record value can reflect whether the failure is due to wear or assembly and material defects, which is beneficial for subsequent improvements.

[0067] In some embodiments, the fault parameters include at least two of the following: vehicle equivalent failure rate, mean fault interval, maintenance effectiveness assessment value, and first fault record value, wherein the first fault record value is the mileage or time at which the first fault occurred. This method enables the analysis of vehicle faults from multiple perspectives, improving the comprehensiveness and reliability of fault assessment.

[0068] In step S15, the weight of each fault parameter is determined based on the test objective, user scenario, and fault distribution, where fault distribution refers to the driving mileage or time point at which the fault occurred.

[0069] In some embodiments, a basic indicator set I, I = {λ}, can be generated based on the data obtained in step S13. eq ,MTBF,η avg (or TE%), FFM}, and then determine the weight of each element in the set. The types of elements in the above set can be increased or decreased as needed, improving flexibility.

[0070] In some embodiments, the test objective, user scenario (e.g., intended use of the vehicle), and fault distribution can each be treated as a dimension, and the single-dimensional weight of the fault in each dimension can be obtained, including obtaining a first weight W based on the test objective. obj Obtain the second weight W based on the user scenario. scene And obtain the third weight W based on the fault distribution. dist .

[0071] Furthermore, the weight of each fault parameter is determined based on the single-dimensional weight and the corresponding dimension's influence factor. In some embodiments, the weight W of fault parameter i in set I is... dyn_i =W base_i *(α*W obj_i +β*W scene_i +γ*W dist_i ), where W base_i W is the preset base weight of fault parameter i (summing to 1), where i is the fault parameter identifier. obj_i W scene_i W dist_i These represent the first, second, and third weights of fault parameter i, respectively. α, β, and γ are adjustable influence factor coefficients (α + β + γ = 1), corresponding to the relative importance of each dimension. In some embodiments, the weights of different fault parameters can be further normalized, as shown by the formula:

[0072] W dyn_i_norm =W dyn_i / Σ(W dyn_j The weights of fault parameter i are normalized and updated to serve as the weights of fault parameter i, where i and j are fault parameter identifiers, with a minimum value of 1 and a maximum value of the number of fault parameter types.

[0073] In some embodiments, a first weight is determined for each failure parameter based on the test objective, wherein: if the test objective is durability verification, the first weight of the first failure record value and the average failure parameter is higher; if the test objective is maintainability verification, the first weight of the maintenance effectiveness assessment value is higher; if the test objective is comprehensive acceptance, the first weight of each failure parameter is less different, or may be the same.

[0074] In some embodiments, an initial value for the first weight can be set, and an offset for the first weight under different experimental objectives can be set based on the initial value. The correspondence between the experimental objectives and the offsets of the first weight is shown in Table 3. The data in Table 3 is an illustration of feasible embodiments and does not constitute an undue limitation on this application.

[0075] Table 3 Examples of weight offset quantization rules for the experimental target dimension

[0076]

[0077] In some embodiments, a database of correspondences between experimental targets and weight offsets (or first weights) can be generated and maintained to improve the accuracy and ease of use of the data.

[0078] In some embodiments, determining the weight of each fault parameter includes: determining a second weight for each fault parameter based on the user scenario, wherein: if the user scenario is long-haul logistics, the second weight of the first fault record value and the average fault parameter is higher; if the user scenario is engineering or mining work, the second weight of the vehicle equivalent failure rate is higher; if the user scenario is urban delivery, the second weight of the maintenance efficiency assessment value is higher.

[0079] In some embodiments, the correspondence between user scenarios and the second weight is shown in Table 4. The data in Table 3 is an illustration of feasible embodiments and does not constitute an improper limitation on this application.

[0080] Table 4. Examples of weight offset quantization rules for user scenario dimension

[0081]

[0082] In some embodiments, a database of correspondences between user scenarios and weight offsets (or second weights) can be generated and maintained to improve the accuracy and ease of use of the data.

[0083] In some embodiments, determining the weight of each fault parameter includes: determining a third weight for each fault parameter based on the fault distribution, wherein: if the number of faults is high from the start of the test to the first predetermined distance or the first predetermined time length, the third weight of the first fault record value in the fault parameter is higher; if the number of faults is high from the end of the test to the second predetermined distance or the second predetermined time length, the third weight of the vehicle equivalent failure rate in the fault parameter is higher.

[0084] In some embodiments, the correspondence between fault distribution and third weight is shown in Table 5. The data in Table 3 is an illustration of feasible embodiments and does not constitute an improper limitation on this application.

[0085] Table 5. Examples of weight offset quantization rules for fault distribution dimension

[0086]

[0087] In some embodiments, a database of the correspondence between fault distribution and weight offset (or third weight) can be generated and maintained to improve the accuracy and ease of use of the data.

[0088] In step S17, the quality inspection evaluation value of the vehicle is determined based on multiple fault parameters and weights. For example, the inter-vehicle evaluation value is the weighted sum of the fault parameters of each fault that occurred during the test.

[0089] In some embodiments, the fault parameters can be standardized first, for example, by standardizing the calculated λ. eq MTBF, η avg The original values ​​(with different dimensions and magnitudes) such as λ, FFM, etc., are standardized (e.g., Min-Max normalization, Z-score normalization) to make them fall within [0,1] or a predetermined interval. In some embodiments, λ is... eq By taking the reciprocal or negative number, the larger the value of the various indicators after processing, the better the reliability, thereby improving the intuitiveness of reliability observation.

[0090] Furthermore, based on standardized fault parameters, a comprehensive quality inspection evaluation value (Score) is calculated:

[0091] Score=Σ(X i *W dyn_i_norm )

[0092] X i It is the standardized value of the fault parameter i.

[0093] In some embodiments, a range of quality inspection evaluation values ​​can be set to correspond to different reliability levels. The number of ranges and thresholds can be set and adjusted based on historical data. For example, the reliability of the whole vehicle can be divided into 4 levels: a value of [90-100] indicates excellent reliability, a value of [80-89] indicates good reliability, a value of [70-79] indicates medium reliability, and a value <70 indicates poor reliability.

[0094] This approach, based on a "dynamic comprehensive weight allocation model" that considers test objectives, user scenarios, and fault distribution, makes the evaluation results more targeted and instructive. The comprehensive score and grade provide intuitive conclusions on vehicle reliability evaluation, facilitating comparisons between different vehicle models or with historical data.

[0095] Based on the method in the above embodiments, the fault variability during vehicle testing is quantified, and the quantified faults are intelligently integrated with multiple indicators such as maintenance efficiency assessment, thereby improving the comprehensive assessment capability of vehicle quality and the accuracy of vehicle quality test results.

[0096] In some embodiments, such as Figure 1 As shown, the vehicle durability and reliability test data processing method of this disclosure also includes step S19.

[0097] In step S19, the weak points of the vehicle are determined based on the fault parameters.

[0098] In some embodiments, the vulnerable mode, system, or component can be determined based on the equivalent value of the fault, for example, according to E. i Sort out the systems or components that are causing high equivalent failure rates.

[0099] In some embodiments, fault points that are difficult and time-consuming to repair can be identified based on the fault repair efficiency assessment value.

[0100] In some embodiments, the high-incidence phase of a fault can be determined based on the initial fault record value and the fault distribution.

[0101] Based on the method in the embodiments shown above, the weak points of the vehicle can be identified, thereby enabling targeted improvements and improving the efficiency and effectiveness of vehicle improvement.

[0102] Based on the method in the embodiments shown in this disclosure, each fault parameter is standardized, and a weighted sum is obtained using dynamic weights. The overall vehicle reliability score is then obtained, and the reliability level is divided according to a preset threshold. At the same time, high-equivalent faults and low-maintenance-efficiency events are identified as weak links. This achieves a comprehensive evaluation of vehicle reliability from multiple dimensions, improves the objectivity and accuracy of the evaluation, makes the evaluation results more intuitive, and improves the effective utilization rate of test data, which is conducive to targeted improvements.

[0103] The following example, using a 6×4 new energy tractor unit that has completed a 100,000-kilometer reliability test, illustrates the vehicle quality inspection method disclosed herein. The data below are merely illustrative and do not constitute an undue limitation on this disclosure.

[0104] 1. Data Acquisition Phase

[0105] Collect 15 faults recorded during the test and their detailed maintenance information (including fault mode, level, maintenance time, etc.), total mileage T total =100000km.

[0106] 2. Indicator Calculation

[0107] (1) Calculate the vehicle equivalent failure rate λ eq

[0108] Check the equivalent coefficient library: for example, "motor bearing wear (S=1, O=1.5, D=1) -> K=1.15"; "motor controller failure (S=2, O=2.0, D=2) -> K=2.0".

[0109] Calculate each fault E i Then, the fault equivalent value N of the whole vehicle is calculated. eq =ΣE_i=28.5 (assuming E = 15 faults) i The total is 28.5.

[0110] Vehicle equivalent failure rate λ eq =28.5 / 100000 / 1000=0.285 equivalent number of failures / thousand kilometers.

[0111] (2) Calculate the mean time between failures (MTBF)

[0112] If N=15 (actual number of failures), then MTBF=100000 / 15≈6666.7km.

[0113] (3) Calculate maintenance efficiency η avg

[0114] Check the reference time database: such as "replace motor bearing T" benchmark =2.0 hours”; “Replace motor controller T” benchmark =1.5 hours.

[0115] Calculate each η i (If the actual bearing replacement takes 2.5 hours -> η) i =2.0 / 2.5=0.8).

[0116] η avg =(Ση i ) / 15=0.85.

[0117] (4) Obtain the mileage at which the first fault occurred: The first fault occurred at 15,000 km (slight clutch slippage).

[0118] 3. Calculate dynamic weights

[0119] Base weight (preset): W base =[λ eq ,MTBF,η avg [FFM], In this embodiment, λ eq =0.3, MTBF=0.3, η avg =0.2, FFM=0.2.

[0120] (1) Dimension of Experimental Objectives (W) obj ): Comprehensive Acceptance → Offset [0%,0%,0%,0%] → First Weight W obj =[1.0,1.0,1.0,1.0].

[0121] (2) User scenario dimension (W) scene Long-haul logistics → Offset [10%, 30%, -15%, 0%] → Second weight W scene =[1.1,1.3,0.85,1.0].

[0122] (3) Fault distribution dimension (W) dist ): Third weight W dist =[1.0,1.0,1.0,1.0].

[0123] Dynamic weight calculation:

[0124] Default coefficients: α=0.4, β=0.4, γ=0.2;

[0125] Weight W of vehicle equivalent failure rate dyn_λ_eq =0.312;

[0126] Weight W of mean fault interval dyn_MTBF =0.336;

[0127] Weight W of average maintenance efficiency dyn_η_avg =0.188;

[0128] Weight W of the mileage at which the first failure occurs dyn_FFM =0.2.

[0129] Normalized dynamic weights:

[0130] Weight W of vehicle equivalent failure rate dyn_norm_λ_eq ≈0.301;

[0131] Weight W of mean fault interval dyn_norm_MTBF ≈0.324;

[0132] Weight W of average maintenance efficiency dyn_norm_η_avg ≈0.181;

[0133] Weight W of the mileage at which the first failure occurs dyn_norm_FFM =≈0.193.

[0134] 4. Overall Evaluation

[0135] (1) Standardization of indicators:

[0136] Assume λ eqBest score: 0, Worst score: 0.5 (equivalent failures / thousand kilometers) → Standardized score = (0.5 - λ) eq ) / 0.5×100;

[0137] Assuming MTBF: best 20000km, worst 5000km → standardized score = (MTBF-5000) / (20000-5000)×100;

[0138] Assume η avg Best 1.5, worst 0.5 → Standardized score = (η) avg -0.5) / (1.5-0.5)×100;

[0139] Assuming FFM: best 100,000km (no faults), worst 0km → standardized score = (FFM-0) / 100,000×100.

[0140] Substitute the data into this example:

[0141] λ eq =0.285 → Standardized score: 43.0;

[0142] MTBF=6666.7 → Standardized score: 11.11;

[0143] η avg =0.85 → Standardized score: 35.0;

[0144] FFM=15000 → Standardized score: 15.0.

[0145] (2) Calculation of comprehensive score:

[0146] Score = Σ(Standardized score * Normalized dynamic weight)

[0147] =43.0×0.301+11.11×0.324+35.0×0.181+15.0×0.193

[0148] ≈25.77.

[0149] (3) Reliability level classification (preset level threshold):

[0150] Assume the following criteria are met: Excellent: Score ≥ 80; Good: 60 ≤ Score < 80; Average: 40 ≤ Score < 60; Poor: Score < 40

[0151] This example scores 25.77, which is considered a "poor" grade.

[0152] (4) Identification of weak links:

[0153] Highest equivalent fault: Assuming the faulty brake pad wear is Ei =2.55, which is one of the highest equivalent fault values ​​in this test.

[0154] The maintenance event with the lowest maintenance efficiency: Assume a certain maintenance η i =0.6 (below the average of 0.85), then identify the repair.

[0155] Flowcharts of some embodiments of the vehicle durability and reliability test data processing apparatus disclosed herein are as follows: Figure 3 As shown.

[0156] The fault parameter determination unit 33 is capable of determining multiple fault parameters based on vehicle test records. In some embodiments, the fault parameter determination unit 33 can perform the methods described in step S13 above and any of the embodiments shown in steps 231-235.

[0157] The weight determination unit 35 is capable of determining the weight of each fault parameter based on the test objective, user scenario, and fault distribution, wherein the fault distribution refers to the driving mileage or time point at which the fault occurred. In some embodiments, the weight determination unit 35 can execute the methods in step S15 above and any of the embodiments shown in steps 251-254.

[0158] The result determination unit 37 is capable of determining the quality inspection evaluation value of the vehicle based on multiple fault parameters and weights. In some embodiments, the result determination unit 37 can perform the methods in any of the embodiments shown in step S17 and step 270 above.

[0159] Based on the device in the above embodiment, the fault difference quantification in the vehicle testing process is realized. The faults after difference quantification are intelligently integrated with multiple indicators such as maintenance efficiency evaluation, which improves the comprehensive evaluation capability of vehicle quality and improves the accuracy of vehicle quality test results.

[0160] In some embodiments, such as Figure 3 As shown, the vehicle durability and reliability test data processing apparatus of this disclosure further includes an information acquisition unit 31, capable of acquiring vehicle test records. In some embodiments, the information acquisition unit 31 can execute the method in any of the embodiments shown in steps S11 and 210 above, acquiring vehicle test records as basic data and providing them to the fault parameter determination unit 33. The vehicle test records include recording information on the types, times, and mileage of various events occurring during the test. In some embodiments, they also include vehicle parameter information, test setting information, etc. Such an apparatus can provide sufficient data for subsequent analysis, which is beneficial to improving the accuracy of the analysis results.

[0161] In some embodiments, such as Figure 3As shown, the vehicle durability and reliability test data processing device disclosed herein also includes a fault analysis unit 39, which can determine the weak points of the vehicle based on fault parameters.

[0162] In some embodiments, the fault analysis unit 39 can determine the weak mode, system or component based on the equivalent value of the fault, wherein the equivalent value of the fault is determined based on one or more of the fault severity, maintenance complexity and functional impact.

[0163] In some embodiments, the fault analysis unit 39 can determine faults that are difficult to repair based on the fault's repair efficiency evaluation value, wherein the fault's repair efficiency evaluation value is based on the fault's baseline repair time and actual repair time.

[0164] In some embodiments, the fault analysis unit 39 can determine the high-incidence stage of a fault based on the initial fault record value and the fault distribution.

[0165] Based on the device in the embodiments shown above, the weak points of the vehicle can be identified, thereby enabling targeted improvements and improving the efficiency and effectiveness of vehicle improvement.

[0166] A schematic diagram of an embodiment of the vehicle durability and reliability test data processing device disclosed herein is shown below. Figure 4 As shown, the vehicle durability and reliability test data processing device includes a first memory 401 and a first processor 402. The first memory 401 can be a disk, flash memory, or any other non-volatile storage medium. The first memory stores instructions from the corresponding embodiments of the vehicle durability and reliability test data processing method described above. The first processor 402 is coupled to the first memory 401 and can be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The first processor 402 executes the instructions stored in the memory, thereby improving the comprehensive evaluation capability of vehicle quality and increasing the accuracy of vehicle quality test results.

[0167] In one embodiment, it can also be as follows: Figure 5 As shown, the vehicle durability and reliability test data processing device 500 includes a second memory 501 and a second processor 502. The second processor 502 is coupled to the second memory 501 via a BUS bus 503. This vehicle durability and reliability test data processing device 500 can also be connected to an external storage device 505 via a storage interface 504 to access external data, and can also be connected to a network or another computer system (not shown) via a network interface 506. Further details are omitted here.

[0168] In this embodiment, by storing data instructions in a memory and then processing the instructions by a processor, the comprehensive assessment capability of vehicle quality can be improved, and the accuracy of vehicle quality test results can be increased.

[0169] In another embodiment, a computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the steps of the method in the corresponding embodiment of the vehicle durability and reliability test data processing method. Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, apparatus, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0170] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0171] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0172] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0173] This concludes the detailed description of the present disclosure. To avoid obscuring the concept of the disclosure, some details known in the art have not been described. Those skilled in the art will fully understand how to implement the technical solutions disclosed herein based on the above description.

[0174] The methods and apparatus of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0175] It should be noted that the terms "first," "second," etc., used in the specification, claims, and drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit them; although this disclosure has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of this disclosure or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of this disclosure, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in this disclosure.

Claims

1. A method for processing vehicle durability and reliability test data, comprising: Based on vehicle test records, several fault parameters were determined, including the vehicle equivalent failure rate, which was determined based on the severity of the fault, the complexity of maintenance, and the degree of functional impact. Based on the test objectives, user scenarios, and fault distribution, the weight of each fault parameter is determined, including: determining a first weight for each fault parameter based on the test objectives; determining a second weight for each fault parameter based on the user scenarios; determining a third weight for each fault parameter based on the fault distribution, wherein the fault distribution is the driving mileage or time point where the fault occurred; and determining the weight of each fault parameter based on the first weight, the second weight, the third weight, and the influencing factor. The vehicle's quality inspection assessment value is determined based on the aforementioned fault parameters and their weights.

2. The vehicle durability and reliability test data processing method according to claim 1, wherein, If the test objective is durability verification, then the first weight of the first failure record value and the average failure parameter among the failure parameters is higher. If the test objective is maintainability verification, then the first weight of the maintenance effectiveness evaluation value among the fault parameters is higher; If the test objective is comprehensive acceptance, then the first weight of each of the fault parameters is equivalent.

3. The vehicle durability and reliability test data processing method according to claim 1, wherein, If the user scenario is long-haul logistics, then the second weight of the first fault record value and the average fault parameter in the fault parameters is higher. If the user scenario is engineering or mining work, then the second weight of the vehicle equivalent failure rate in the fault parameters is higher. If the user scenario is urban delivery, then the second weight of the repair efficiency evaluation value in the fault parameters is higher.

4. The vehicle durability and reliability test data processing method according to claim 1, wherein, If there are many failures from the start of the test to the first predetermined distance or the first predetermined time length, then the third weight of the first failure record value in the failure parameters is higher. If the number of failures is high during the second predetermined distance or second predetermined time before the end of the test, then the third weight of the vehicle equivalent failure rate in the failure parameters is higher.

5. The vehicle durability and reliability test data processing method according to claim 1, wherein, The determination of multiple fault parameters based on the vehicle test records includes: The equivalent value of the fault is determined based on the severity of the fault, the complexity of maintenance, and the impact on functionality. The vehicle equivalent failure rate is determined based on the equivalent value of the fault and the test length, wherein the test length includes at least one of test mileage or test duration.

6. The vehicle durability and reliability test data processing method according to claim 1, wherein, The fault parameters also include a maintenance efficiency assessment value, which is determined based on the baseline maintenance time and actual maintenance time of the fault.

7. The vehicle durability and reliability test data processing method according to claim 6, wherein, The determination of multiple fault parameters based on the vehicle test records includes: For each fault, the repair efficiency is obtained based on the baseline repair time and the actual repair time. The average maintenance efficiency is determined based on the maintenance efficiency of each fault and the number of faults. The maintenance efficiency evaluation value includes the average maintenance efficiency.

8. The vehicle durability and reliability test data processing method according to claim 6, wherein, The determination of multiple fault parameters based on the vehicle test records includes: The sum of the baseline repair time for the fault and the sum of the actual repair time for the fault; The percentage of total effective repair time for a fault is determined by summing the baseline repair time and summing the actual repair time for that fault. The maintenance efficiency assessment value includes the percentage of total effective maintenance time.

9. The vehicle durability and reliability test data processing method according to claim 1, wherein, The fault parameters also include at least one of the following: average fault interval and first fault record value, wherein the first fault record value is the mileage or time at which the first fault occurred.

10. The vehicle durability and reliability test data processing method according to claim 1, further comprising: Based on the fault parameters, identify the vehicle's weak points, including at least one of the following: Based on the equivalent value of the fault, identify the vulnerable mode, system, or component; Based on the maintenance efficiency assessment value of the fault, faults that are difficult to repair are identified, wherein the maintenance efficiency assessment value is determined based on the baseline maintenance time and the actual maintenance time of the fault. or Based on the initial fault record value and fault distribution, the high-incidence phase of the fault is determined.

11. A vehicle durability and reliability test data processing device, comprising: The fault parameter determination unit is configured to determine multiple fault parameters based on vehicle test records, including the vehicle equivalent failure rate, which is determined based on the severity of the fault, maintenance complexity, and functional impact. The weight determination unit is configured to determine the weight of each fault parameter based on the test objective, user scenario, and fault distribution, including: determining a first weight of each fault parameter based on the test objective; determining a second weight of each fault parameter based on the user scenario; determining a third weight of each fault parameter based on the fault distribution, wherein the fault distribution is the driving mileage or time point where the fault occurred; and determining the weight of each fault parameter based on the first weight, the second weight, the third weight, and an influencing factor. The result determination unit is configured to determine the quality inspection evaluation value of the vehicle based on a plurality of the fault parameters and the weights.

12. The vehicle durability and reliability test data processing device according to claim 11, further comprising: The fault analysis unit is configured to determine the vehicle's weak points based on the fault parameters, including at least one of the following: Based on the equivalent value of the fault, identify the vulnerable mode, system, or component; Based on the maintenance efficiency assessment value of the fault, faults that are difficult to repair are identified, wherein the maintenance efficiency assessment value is determined based on the baseline maintenance time and the actual maintenance time of the fault. or Based on the initial fault record value and fault distribution, the high-incidence phase of the fault is determined.

13. A vehicle durability and reliability test data processing device, comprising: Memory; as well as A processor coupled to the memory, the processor being configured to execute the vehicle durability and reliability test data processing method as described in any one of claims 1 to 10 based on instructions stored in the memory.

14. A computer-readable storage medium having stored thereon computer instructions that, when executed by a processor, implement the vehicle durability and reliability test data processing method according to any one of claims 1 to 10.

15. A computer program product comprising a computer program or instructions, wherein the computer program or instructions, when executed by a processor, implement the vehicle durability and reliability test data processing method according to any one of claims 1 to 10.

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