Evaluation method and device for multi-factor power assembly fault of vehicle

Abstract

The invention relates to the technical field of vehicles, in particular to a vehicle multi-factor power assembly fault assessment method and device, and the method comprises the steps: collecting power assembly fault information of a vehicle under a target driving condition; obtaining target fault data of the vehicle according to the power assembly fault information, and matching a target fault type of the vehicle by using the target fault data; and calculating a target cumulative occurrence probability of the target fault type, and determining a power assembly fault occurrence probability model of the vehicle by using the target cumulative occurrence probability, so as to generate a multi-factor power assembly fault evaluation result of the vehicle by using the power assembly fault occurrence probability model. According to the embodiment of the invention, the power assembly fault occurrence probability model of the vehicle can be determined according to the cumulative occurrence probability of the same fault type and the cumulative occurrence probability of different fault types, so that the multi-factor power assembly fault assessment result of the vehicle is generated, and the comprehensiveness and accuracy of vehicle power assembly fault assessment are effectively improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method and device for evaluating vehicle multi-factor powertrain failure. Background Art

[0002] In the related technology, the current vehicle powertrain system simulation and testing can be carried out through real vehicle testing. The test environment is real. During the test, the driver's operation is closely related to the surrounding environment. The test is not easy to replicate, and is affected by the test method and test conditions. The test coverage is not comprehensive and some functions cannot be tested. In addition, when conducting fault testing, some serious faults cannot be tested on the actual vehicle, so the corresponding fault detection and handling methods cannot be tested. In addition, it is also possible to test by building a powertrain bench. Its test coverage is relatively comprehensive compared to the first method and can be tested repeatedly.

[0003] However, the actual vehicle testing in the related technology is affected by the test method and test conditions, resulting in incomplete test coverage and the inability to test some complex functions. In addition, the cycle for building a powertrain bench is long and the cost is high, and it is impossible to test serious fault detection and handling methods, which reduces the applicability and accuracy of vehicle powertrain fault assessment and needs to be urgently resolved. Summary of the invention

[0004] The present application provides a method and device for evaluating vehicle multi-factor powertrain faults to solve the problems in the related art that complex faults cannot be tested, the comprehensiveness of fault testing is reduced, and the applicability and accuracy of vehicle powertrain fault evaluation are reduced.

[0005] The first aspect of the present application provides a method for evaluating multi-factor powertrain failures of a vehicle, comprising the following steps: collecting powertrain failure information of the vehicle under a target driving condition; acquiring target failure data of the vehicle based on the powertrain failure information, and using the target failure data to match a target failure type of the vehicle; calculating a target cumulative occurrence probability of the target failure type, and using the target cumulative occurrence probability to determine a powertrain failure occurrence probability model of the vehicle, so as to use the powertrain failure occurrence probability model to generate a multi-factor powertrain failure evaluation result of the vehicle.

[0006] Through the above-mentioned technical means, the embodiment of the present application can determine the vehicle's powertrain failure probability model based on the cumulative occurrence probability of the same fault type and the cumulative occurrence probability of different fault types, thereby generating a multi-factor powertrain failure assessment result for the vehicle, effectively improving the comprehensiveness and accuracy of the vehicle's powertrain failure assessment.

[0007] Optionally, in one embodiment of the present application, obtaining target fault data of the vehicle based on the powertrain fault information includes: recording the powertrain fault information of the vehicle based on a preset fault record format; and obtaining target fault data of the vehicle based on the powertrain fault information.

[0008] Through the above-mentioned technical means, the embodiment of the present application can record the powertrain fault information according to the fault record format, and obtain the target fault data of the vehicle according to the powertrain fault information, thereby effectively improving the reliability of the fault information.

[0009] Optionally, in one embodiment of the present application, the determining the target fault type of the vehicle using the target fault data includes: identifying the target fault cause of the vehicle using the target fault data; and determining the target fault type of the vehicle according to the target fault cause.

[0010] Through the above-mentioned technical means, the embodiment of the present application can utilize the target fault data to match the target fault type of the vehicle, that is, analyze the cause of the fault according to the fault data, distinguish the same fault type and different fault types, and effectively improve the accuracy and comprehensiveness of the powertrain fault assessment.

[0011] Optionally, in one embodiment of the present application, determining the target fault type of the vehicle based on the target fault cause includes: judging whether the target fault cause satisfies a preset same fault condition; if the target fault cause satisfies the preset same fault condition, determining the target fault type to be a preset same fault type; if the target fault cause does not satisfy the preset same fault condition, determining the target fault type to be a preset non-same fault type.

[0012] Through the above technical means, the embodiment of the present application can determine whether the target fault type is a preset same fault type or a non-same fault type according to whether the target fault cause meets the same fault condition, so as to improve the accuracy of powertrain fault assessment.

[0013] Optionally, in one embodiment of the present application, after determining that the target fault type is a preset same fault type, it also includes: determining the failure rate of the preset same fault type based on the target fault data, and analyzing the failure rate to obtain an analysis result; determining the first cumulative occurrence probability of the preset same fault type based on the analysis result.

[0014] Through the above-mentioned technical means, the embodiments of the present application can analyze the failure rate of the same fault type, so as to determine the first cumulative occurrence probability of the same fault type, effectively improving the comprehensiveness and robustness of vehicle powertrain fault assessment.

[0015] Optionally, in one embodiment of the present application, the target cumulative occurrence probability of the target fault type is calculated, and the powertrain fault occurrence probability model of the vehicle is determined using the target cumulative occurrence probability, including: determining a powertrain cumulative fault occurrence probability curve based on the first cumulative occurrence probability and a preset second cumulative occurrence probability of a non-identical fault type; and determining the powertrain fault occurrence probability model of the vehicle using the powertrain cumulative failure occurrence probability curve.

[0016] Through the above-mentioned technical means, the embodiment of the present application can obtain the cumulative failure probability of the powertrain failure based on the first cumulative occurrence probability and the second cumulative occurrence probability to determine the powertrain cumulative failure probability curve, thereby determining the powertrain failure probability model of the vehicle, effectively improving the comprehensiveness and accuracy of the vehicle powertrain failure assessment.

[0017] The second aspect of the present application provides an evaluation device for multi-factor powertrain failure of a vehicle, including: a collection module, used to collect powertrain failure information of the vehicle under a target driving condition; an acquisition module, used to obtain target failure data of the vehicle based on the powertrain failure information, and use the target failure data to match the target failure type of the vehicle; an evaluation module, used to calculate the target cumulative occurrence probability of the target failure type, and use the target cumulative occurrence probability to determine a powertrain failure occurrence probability model of the vehicle, so as to use the powertrain failure occurrence probability model to generate a multi-factor powertrain failure evaluation result of the vehicle.

[0018] Through the above-mentioned technical means, the vehicle multi-factor powertrain failure evaluation device can determine the vehicle's powertrain failure probability model based on the cumulative occurrence probability of the same fault type and the cumulative occurrence probability of different fault types, thereby generating the vehicle's multi-factor powertrain failure evaluation results, effectively improving the comprehensiveness and accuracy of the vehicle's powertrain failure evaluation.

[0019] Optionally, in one embodiment of the present application, the acquisition module includes: a recording unit, used to record the powertrain fault information of the vehicle based on a preset fault recording format; and an acquisition unit, used to acquire target fault data of the vehicle according to the powertrain fault information.

[0020] Through the above technical means, the acquisition module can record the powertrain fault information according to the fault record format, and obtain the target fault data of the vehicle according to the powertrain fault information, which effectively improves the reliability of the fault information.

[0021] Optionally, in one embodiment of the present application, the acquisition module includes: an identification unit, used to identify a target fault cause of the vehicle using the target fault data; and a first determination unit, used to determine a target fault type of the vehicle according to the target fault cause.

[0022] Through the above-mentioned technical means, the acquisition module can use the target fault data to match the target fault type of the vehicle, that is, analyze the cause of the fault according to the fault data, distinguish the same fault type and different fault types, and effectively improve the accuracy and comprehensiveness of the powertrain fault assessment.

[0023] Optionally, in one embodiment of the present application, the first determination unit includes: a judgment subunit, used to judge whether the target fault cause satisfies a preset same fault condition; a first judgment subunit, used to judge that the target fault type is a preset same fault type if the target fault cause satisfies the preset same fault condition; and a second judgment subunit, used to judge that the target fault type is a preset non-same fault type if the target fault cause does not satisfy the preset same fault condition.

[0024] Through the above technical means, the first determination unit can determine whether the target fault type is a preset same fault type or a non-same fault type according to whether the target fault cause meets the same fault condition, so as to improve the accuracy of powertrain fault assessment.

[0025] Optionally, in one embodiment of the present application, the device of the embodiment of the present application also includes: a processing module, which is used to determine the failure rate of the preset same fault type according to the target fault data after determining that the target fault type is the preset same fault type, and analyze the failure rate to obtain an analysis result; a determination module, which is used to determine the first cumulative occurrence probability of the preset same fault type according to the analysis result after determining that the target fault type is the preset same fault type.

[0026] Through the above-mentioned technical means, the vehicle multi-factor powertrain fault evaluation device can analyze the failure rate of the same fault type, so as to determine the first cumulative occurrence probability of the same fault type, effectively improving the comprehensiveness and robustness of the vehicle powertrain fault evaluation.

[0027] Optionally, in one embodiment of the present application, the evaluation module includes: a second determination unit, used to determine a powertrain cumulative fault occurrence probability curve based on the first cumulative occurrence probability and a preset second cumulative occurrence probability of a non-identical fault type; and a third determination unit, used to determine a powertrain fault occurrence probability model of the vehicle using the powertrain cumulative fault occurrence probability curve.

[0028] Through the above-mentioned technical means, the evaluation module can obtain the cumulative failure probability of the powertrain failure based on the first cumulative occurrence probability and the second cumulative occurrence probability to determine the powertrain cumulative failure probability curve, thereby determining the powertrain failure probability model of the vehicle, effectively improving the comprehensiveness and accuracy of the vehicle powertrain failure evaluation.

[0029] A third aspect of the present application provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle multi-factor powertrain failure assessment method as described in the above embodiment.

[0030] A fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the above-mentioned method for evaluating multi-factor powertrain failures of a vehicle.

[0031] A fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for evaluating multi-factor powertrain failures of a vehicle.

[0032] The embodiment of the present application can obtain the target fault data of the vehicle according to the powertrain fault information, and use the target fault data to match the target fault type of the vehicle, calculate the target cumulative occurrence probability of the target fault type, and thus determine the powertrain fault occurrence probability model of the vehicle, so as to use the powertrain fault occurrence probability model to generate the multi-factor powertrain fault assessment result of the vehicle, effectively improving the comprehensiveness and accuracy of the vehicle powertrain fault assessment. Thus, the problems in the related art that complex faults cannot be tested, the comprehensiveness of fault testing is reduced, and the applicability and accuracy of vehicle powertrain fault assessment are reduced are solved.

[0033] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0035] Figure 1 A flowchart of a method for evaluating a vehicle multi-factor powertrain failure provided according to an embodiment of the present application;

[0036] Figure 2 This is a schematic diagram of a powertrain cumulative failure occurrence probability curve of a specific embodiment of the present application;

[0037] Figure 3 It is a structural schematic diagram of a vehicle multi-factor powertrain fault assessment device provided according to an embodiment of the present application;

[0038] Figure 4 It is a schematic diagram of the structure of a vehicle provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0040] The following describes the vehicle multi-factor powertrain fault assessment method and device of the embodiment of the present application with reference to the accompanying drawings. In view of the problems that the related technologies mentioned in the background technology center cannot test complex faults, reduce the comprehensiveness of fault testing, and reduce the applicability and accuracy of vehicle powertrain fault assessment, the present application provides a vehicle multi-factor powertrain fault assessment method, in which the target fault data of the vehicle can be obtained according to the powertrain fault information, and the target fault data is used to match the target fault type of the vehicle, and the target cumulative occurrence probability of the target fault type is calculated, so as to determine the powertrain fault occurrence probability model of the vehicle, so as to generate the vehicle multi-factor powertrain fault assessment result using the powertrain fault occurrence probability model, and effectively improve the comprehensiveness and accuracy of the vehicle powertrain fault assessment. Therefore, the problems that the related technologies cannot test complex faults, reduce the comprehensiveness of fault testing, and reduce the applicability and accuracy of vehicle powertrain fault assessment are solved.

[0041] Specifically, Figure 1 A schematic flow chart of a method for evaluating a vehicle multi-factor powertrain failure provided in an embodiment of the present application.

[0042] like Figure 1 As shown, the vehicle multi-factor powertrain failure assessment method includes the following steps:

[0043] In step S101, powertrain fault information of a vehicle under a target driving condition is collected.

[0044] It is understandable that the embodiments of the present application can collect powertrain fault information of the vehicle under the target driving condition. For example, the embodiments of the present application can formulate a vehicle test plan based on the actual use scenario of the vehicle, so that the powertrain can not only ensure the designed life, but also ensure that there are very few faults or avoid maintenance during the life cycle. Among them, the actual use scenario of the vehicle, that is, the target driving condition of the vehicle can be: (1) covering different weather conditions, such as rain, ice and snow, high temperature, etc.; (2) covering different time periods, covering the user's travel time including the morning and evening peak congestion period; (3) covering the main public places for people, such as railway stations, schools, etc.; (4) covering multiple driving factors, such as aggressive, conservative, and stable; (5) turning on all functions related to the powertrain, such as idle start-stop function and AUTOHOLD function, and the driving mode covers all functions of the vehicle model, such as comfort mode, economy mode, and sports mode; (6) the road surface covers urban roads, general highways, expressways, etc., so that the powertrain fault information under different scenarios can be collected, thereby effectively improving the comprehensiveness of the powertrain fault assessment.

[0045] In step S102, target fault data of the vehicle is acquired according to the powertrain fault information, and the target fault data is used to match the target fault type of the vehicle.

[0046] It can be understood that the example of the present application can obtain the target fault data of the vehicle based on the powertrain fault information. For example, the powertrain fault information can be recorded in a certain format, such as the vehicle where the fault occurred, the time of the fault, the mileage of the fault, the fault phenomenon, etc. All faults that occur need to be recorded, so that the target fault data of the vehicle can be obtained based on the powertrain fault information, such as the specific number of faults, etc. Then, the target fault data can be used to match the target fault type of the vehicle, that is, the cause of the fault is analyzed according to the fault data, and the same fault type and different fault types are distinguished, which effectively improves the accuracy of the powertrain fault assessment.

[0047] In one embodiment of the present application, obtaining target fault data of the vehicle according to powertrain fault information includes: recording the powertrain fault information of the vehicle based on a preset fault recording format; and obtaining the target fault data of the vehicle according to the powertrain fault information.

[0048] For example, the embodiment of the present application can record the powertrain fault information of the vehicle based on the fault record format, that is, the vehicle test fault is recorded in the format required below to ensure that the information required for data analysis is covered. The basic information test data analysis is based on the name of the item, the name of the component, the description of the problem, etc. Table 1 is a reliability data record table, and the specific Table 1 is as follows:

[0049] Table 1

[0050]

[0051] Next, the embodiment of the present application can obtain the target fault data of the vehicle according to the powertrain fault information, such as the specific number of faults, etc., which effectively improves the reliability of the fault information.

[0052] Optionally, in one embodiment of the present application, using target fault data to determine a target fault type of the vehicle includes: using the target fault data to identify a target fault cause of the vehicle; and determining the target fault type of the vehicle according to the target fault cause.

[0053] For another example, the embodiments of the present application can utilize target fault data to match the target fault type of the vehicle, that is, analyze the cause of the fault according to the fault data, distinguish between the same fault type and different fault types, and effectively improve the accuracy and comprehensiveness of powertrain fault assessment.

[0054] Optionally, in one embodiment of the present application, a target fault type of the vehicle is determined based on a target fault cause, including: determining whether the target fault cause satisfies a preset same fault condition; if the target fault cause satisfies the preset same fault condition, determining the target fault type to be a preset same fault type; if the target fault cause does not satisfy the preset same fault condition, determining the target fault type to be a preset non-same fault type.

[0055] In the embodiment of the present application, the preset same fault condition is a condition that the faults are caused by the same reason.

[0056] For another example, the embodiment of the present application can determine whether the target fault cause meets the same fault condition. When the target fault cause meets the same fault condition, the target fault type is determined to be the same fault type; when the target fault cause does not meet the same fault condition, the target fault type is determined to be a non-same fault type, and the fault data of the same cause can be specially sorted for problem law analysis to improve the accuracy of powertrain fault assessment, wherein Table 2 is a reliability record table of the same fault cause, and Table 2 is as follows:

[0057] Table 2

[0058]

[0059] Next, Table 3 is a durability failure reliability data record table, and the specific Table 3 is as follows:

[0060] Table 3

[0061] Serial number Is it a fault? Mileage (unit: km) Parts Description 1 yes 39502 First piece 2 yes 47008 First piece 3 yes 50021 First piece 4 no 33598 Replacement parts 5 no 29579 Replacement parts 6 no 61007 First piece 7 no 65030 First piece 8 no 29333 Replacement parts

[0062] Optionally, in one embodiment of the present application, after determining that the target fault type is the preset same fault type, it also includes: determining the failure rate of the preset same fault type based on the target fault data, and analyzing the failure rate to obtain an analysis result; determining the first cumulative occurrence probability of the preset same fault type based on the analysis result.

[0063] In some embodiments, the embodiments of the present application can quantitatively evaluate the failure rate of the same fault type, that is, the failures of the same cause. Generally, the failure rate is constant or increases when the fault is not repaired or improved. The embodiments of the present application can record the mileage of each fault for intermittent faults, regard multiple test vehicles as one vehicle, and use the single data complete data method to evaluate the failure rate trend. This type of fault can generally be solved by software updates, and the software update time is the timing truncation time point; for hardware failures that cause the vehicle to need to replace parts to restore and solve the problem, record the mileage of the vehicle where the fault occurred and the mileage of the vehicle without the fault, and record the mileage of the replaced parts at the same time. This type of fault is generally solved by replacing improved parts, and the time point of replacing the new state is the timing truncation time point.

[0064] Among them, for intermittent failures, the AMSAA (Army Materiel System Analysis Activity, AMSAA) model calculation steps can be applied, that is, the process of trend test, parameter estimation (unbiased estimation), goodness of fit test, MTBF (Mean Time Between Failures) estimation, and cumulative failure rate calculation. The AMSAA model is used to determine whether the failure rate of the problem is increasing, unchanged, or decreasing. The basic principle of the AMSAA model is:

[0065] (1) The number of failures N(t) of a repairable product during the development period (0, t] is a mean function And instantaneous intensity λ(t)=dEN(t) / dt=abt b-1 A non-homogeneous Poisson process. That is:

[0066]

[0067] Among them, the intensity is λ(t)=abt b-1 A non-homogeneous Poisson process {N(t); t≥0} is also called a power-law process.

[0068] (2) After the development of the maintainable product reaches time T, no design improvement or correction is performed. It can be roughly considered that after the product is finalized, its failure distribution follows an exponential distribution, that is:

[0069] λ(t)=abT b-1

[0070] The MTBF at the time of finalization is called the MTBF that the product can achieve, that is:

[0071]

[0072] Let the successive failure times of a repairable product during the development stage (0, T] be:

[0073] 0 < t 1 < t 2 <… < t n ≤ T

[0074] When 0 < b < 1, as i increases, the failure time interval t i - t i-1 (i = 1, 2, …, n) also increases, and λ(t) shows a strictly monotonic decrease, then the product reliability can be considered to be in a growth process.

[0075] When b > 1, as i increases, the failure time interval t i - t i-1 (i = 1, 2, …, n) decreases, and λ(t) strictly monotonically increases, then the product is in a process of decreasing reliability.

[0076] When b = 1, λ(t) = a, and the non - homogeneous Poisson process degenerates into a homogeneous Poisson process. The failure time interval t i - t i-1 (i = 1, 2, …, n) follows an exponential distribution, and the product reliability neither increases nor decreases, being approximately constant.

[0077] Specifically, the following details the calculation steps of applying the AMSAA model:

[0078] The first step: Inspection of the failure rate change trend. The failure rate change trend is a statistical hypothesis test, which makes a probability judgment on whether there is a significant change in the failure rate of the vehicle during the test. In the embodiment of this application, the U - test method is adopted. Let the test statistic be μ, and compare the calculated value with the critical value μ 1-α / 2 as follows:

[0079] Among them, when μ < - μ 1-α / 2 it indicates that at the significance level of α / 2, the failure rate shows a decreasing trend;

[0080] When μ > μ 1-α / 2 it indicates that at the significance level of α / 2, the failure rate shows an increasing trend;

[0081] - μ 1-a / 2 < μ < μ 1-a / 2 it indicates that at the significance level of α / 2, the failure rate hardly changes.

[0082] Among them, the test data in the embodiment of the present application is time-truncated, and the test statistic is shown as follows:

[0083]

[0084] Where, M = n. Substituting the data in Table 2, we can get μ = 0.6545.

[0085] Next, Table 4 shows the critical value μ of the trend test statistic μ 1-a / 2 Table, specific Table 4 is as follows:

[0086] Table 4

[0087]

[0088] Set the significance level α = 0.10, and refer to Table 4 to obtain the critical value μ of the trend test statistic 1-α / 2 =1.645. -μ 1-a / 2 <μ<μ 1-a / 2 When , the significance level of 0.05 shows that the failure rate has no significant change, indicating that the failure rate of this cause is stable.

[0089] Step 2: Unbiased parameter estimation. In this embodiment, the same problem of multiple vehicles can be regarded as a single product, using time-cut data, assuming t 1 ,t 2 ,…,t n is the time when the first n faults occurred in the power law process. Under the condition that the number of faults at the time of time truncation N(T) = n is known, we have:

[0090]

[0091] Under the condition of N(T)=n, n≥1, (t 1 ,t 2 ,…,t n ) is:

[0092]

[0093] make:

[0094]

[0095] then,

[0096]

[0097] Let U 1， U 2 , ..., U n is a sequence of independent and identically distributed random variables, with the common distribution density g(t) mentioned above, then the order statistic U (1) , U(2) , ..., U (n) (replace U 1， U 2 , …, U n The joint distribution density of the values ​​(arranged from small to large) is:

[0098]

[0099] Therefore, when N(T)=n, t 1 ,t 2 ,…,t n The conditional distribution of U (1)， U (2)， …, U (n) The joint distribution of is the same, since and have the same distribution. Note

[0100]

[0101] here

[0102] It can be seen that for any 0 <x<1,

[0103]

[0104] Visible i It obeys the uniform distribution on (0, 1), so -lnξ i ~Γ(1,1). And ξ 1 ,ξ 2 ,…,ξ n Independent of each other, so That is, under the condition that N(T)=n, Therefore

[0105] From this we can see that:

[0106]

[0107] visible is not an unbiased estimate of b. It is known that the (conditional) unbiased estimate of the shape parameter b is:

[0108]

[0109] Accordingly, the approximate unbiased estimates of the scale parameter a and MTBF can be obtained as follows:

[0110]

[0111] Among them, the unbiased estimates of b and a are

[0112] Step 3: Goodness of fit test. The embodiment of the present application can perform a goodness of fit test to check whether the fault data of the test vehicle conforms to the AMSAA model. The embodiment of the present application adopts the Cramér-Von Mises test method and is performed according to the following steps:

[0113] 1) Calculate the goodness of fit test statistic C 2 (M):

[0114] For time-censored data, the Cramér-Von Mises test statistic is:

[0115]

[0116] Wherein, M is the goodness-of-fit test parameter, M = n; is an unbiased estimate of the growth parameter b,

[0117] For failure censored data, the Cramer-Von Mises test statistic is:

[0118]

[0119] Where M is the goodness of fit test parameter, <= n-1; is an unbiased estimate of the growth parameter b,

[0120]

[0121] 2) Determine C 2 Critical value of (<):

[0122] According to the significance level a and M value of the test, we can get C 2 The critical value C of (M) 2 (M,α), where Table 5 shows the critical value C of the Cramér-Von Mises goodness-of-fit test 2 (M,α) table, the specific table 5 is as follows:

[0123] Table 5

[0124]

[0125] 3) Compare C 2 The calculated value of (M) and C 2 Critical value of (M): For the set significance level α, if C 2 The calculated value of (M) is less than or equal to C 2 The critical value C of (M) 2(M,α), indicating that the failure data of this cause conforms to the AMSAA model, and the AMSAA model can be used to statistically estimate the reliability parameters of the product.

[0126] For the set significance level α, if C 2 The calculated value of (M) is greater than C 2 The critical value C of (M) 2 (M,α), indicating that the failure data for this reason does not conform to the AMSAA model, and the AMSAA model cannot be used to statistically estimate the reliability parameters of the product.

[0127] The embodiment of the present application is a time-truncated data. Using the above formula, C can be calculated. 2 (M) = 0.0163 < 0.204, indicating that the goodness of fit test does not reject the AMSAA model at a significance level of 0.05, and the AMSAA model can be used to fit the test data of the cause of the fault.

[0128] Step 4: MTBF estimation. When updating the software, θ(115100) = 15771. During the whole process, the software was not updated, and the original failure rate was homogeneous Poisson distribution, with a constant failure rate. Therefore, the MTBF of the failure caused by this reason is 15771, that is, the fleet travels an average of 15771 kilometers before a failure caused by this reason occurs.

[0129] Step 5: Cumulative failure rate. The failure rate of the homogeneous Poisson distribution above is the failure rate in the exponential model Then the cumulative failure rate is calculated as working hours F 1 (t) is shown in the following formula:

[0130]

[0131] Similarly, the MTBF of the second reason = 66791 kilometers, then The MTBF of the third reason = 80874 km, then

[0132] For wear-out faults, the faults can only be solved by replacing the hardware. For wear-out related faults, the manifestation of this kind of fault always exists, and as the mileage of each test vehicle increases, the problem of this cause will occur more and more frequently in each vehicle, that is, the reliability is declining and the failure rate is getting higher and higher. This kind of fault needs to be solved by replacing parts before the design change. Among them, three vehicles have replaced certain parts, and the parts have not failed after the replacement. Among them, Table 4 is the replacement record of a certain part before the design change.

[0133] In the embodiment of the present application, a two-parameter Weibull model is used for the wear-out type fault, and the likelihood function method is applied to solve the shape parameters and characteristic life of the Weibull model.

[0134] Among them, the likelihood function of the sample is:

[0135]

[0136] Among them, x 1 ,x 2 ,…,x n is the sampling value of the sample; f(x; θ) is the probability density function of the population; θ is the parameter to be determined in the probability density function.

[0137] It should be noted that the basic idea of ​​maximum likelihood is that since the sample comes from the population, the sample can reflect the characteristics of the population to a certain extent. If the observed value of the sample x=(x 1 , x 2 , …, x n ), then we can think that since the probability of occurrence in one trial should be very large. Therefore, among all the possibilities of θ, we select a value that maximizes the probability of occurrence of the sample observation result as the estimated value of θ, recorded as This is the maximum likelihood estimate.

[0138] In a random truncation test, assuming that the total sample is n, there are r failure data in the test data, and the failure times are x i , i=1,2,…,r; T i is the truncation time of the non-failed samples, i = 1, 2, ..., k, that is, the observation time or truncation time of the sample, and n = r + k. The likelihood function of the sample based on the randomly truncated data is:

[0139]

[0140] Among them, f(t,θ) is the overall probability density function of the sample. The likelihood function that obeys the two-parameter Weibull distribution is:

[0141]

[0142] The maximum likelihood estimates of the shape parameter β and scale parameter η are obtained by taking the extreme value of the likelihood function and solving the following equation:

[0143] and

[0144] That is:

[0145]

[0146] Among them, the first item x is all samples, the second item x is the sample with failure, and the shape parameter β is solved by iteration.

[0147] Next, based on the shape parameter estimates obtained above The characteristic life can be calculated Estimated value:

[0148]

[0149] Apply the above formula to calculate the data in Table 4, and we get Then the cumulative failure probability function of this cause is obtained as follows:

[0150]

[0151] Therefore, the embodiments of the present application can classify faults according to their occurrence patterns, use the AMSAA model to determine the fault rate change trend for intermittent software faults, and apply the exponential model to the constant fault rate to obtain the cumulative failure probability. For wear-type hardware faults, the Weibull distribution model is applied to calculate the cumulative failure probability, so as to determine the first cumulative occurrence probability of the same fault type, effectively improving the comprehensiveness and robustness of vehicle powertrain fault assessment.

[0152] In step S103, a target cumulative occurrence probability of a target fault type is calculated, and a powertrain fault occurrence probability model of the vehicle is determined using the target cumulative occurrence probability, so as to generate a multi-factor powertrain fault assessment result of the vehicle using the powertrain fault occurrence probability model.

[0153] It can be understood that the embodiments of the present application can calculate the target cumulative occurrence probability of the target fault type, for example, the cumulative occurrence probability of the same fault type, and sum the cumulative probability distributions of faults of different causes to obtain the cumulative failure probability of the powertrain failure, and then determine the powertrain failure probability model of the vehicle, so as to use the powertrain failure probability model to generate a multi-factor powertrain failure assessment result of the vehicle, thereby effectively improving the comprehensiveness and accuracy of the vehicle powertrain failure assessment.

[0154] Among them, in one embodiment of the present application, a target cumulative occurrence probability of a target fault type is calculated, and a powertrain fault occurrence probability model of a vehicle is determined using the target cumulative occurrence probability, including: determining a powertrain cumulative fault occurrence probability curve based on a first cumulative occurrence probability and a preset second cumulative occurrence probability of a non-identical fault type; and determining a powertrain fault occurrence probability model of the vehicle using the powertrain cumulative fault occurrence probability curve.

[0155] In the actual implementation process, the embodiment of the present application can sum up the cumulative probability distribution of failures of different causes to obtain a second cumulative probability of occurrence of a non-same fault type, thereby obtaining the cumulative probability of powertrain failure based on the first cumulative probability of occurrence and the second cumulative probability of occurrence, that is, a quantitative evaluation of the probability of failure occurrence. For example, the embodiment of the present application can give four causes of powertrain failure and the corresponding cumulative probability model, as shown in the following formula, and the corresponding cumulative failure curve is as follows: Figure 2 As shown,

[0156]

[0157] Among them, the fault causes are not limited to the above four, if there are more causes, they can be accumulated.

[0158] Therefore, the embodiment of the present application can sum the failure probabilities of different causes and derive a cumulative failure probability function and a corresponding curve, thereby determining a powertrain failure probability model for the vehicle to quantitatively evaluate the powertrain failure probability.

[0159] The embodiments of the present application can collect powertrain failures under actual user usage conditions through whole vehicle road tests that simulate user usage conditions, record failure information in the form of reliability data statistical analysis, analyze failure causes, determine the relationship between different failure causes, analyze the failure trend caused by the same cause, determine whether it is random or wear and tear, perform failure analysis on different types of failures, superimpose the failure probabilities of different causes, evaluate the powertrain reliability performance, and evaluate the reliability of the improved powertrain.

[0160] According to the vehicle multi-factor powertrain fault assessment method proposed in the embodiment of the present application, the target fault data of the vehicle can be obtained according to the powertrain fault information, and the target fault data can be used to match the target fault type of the vehicle, and the target cumulative occurrence probability of the target fault type can be calculated, thereby determining the vehicle's powertrain fault occurrence probability model, and using the powertrain fault occurrence probability model to generate the vehicle's multi-factor powertrain fault assessment results, effectively improving the comprehensiveness and accuracy of the vehicle powertrain fault assessment. As a result, the problems of the inability to test complex faults, reducing the comprehensiveness of fault testing, and reducing the applicability and accuracy of vehicle powertrain fault assessment in the related art are solved.

[0161] Next, a vehicle multi-factor powertrain fault assessment device proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0162] Figure 3 It is a block diagram of a device for evaluating vehicle multi-factor powertrain faults according to an embodiment of the present application.

[0163] like Figure 3 As shown, the vehicle multi-factor powertrain fault assessment device 10 includes: a collection module 100 , an acquisition module 200 and an assessment module 300 .

[0164] Specifically, the acquisition module 100 is used to acquire powertrain fault information of the vehicle under a target driving condition.

[0165] The acquisition module 200 is used to acquire target fault data of the vehicle according to the powertrain fault information, and use the target fault data to match the target fault type of the vehicle.

[0166] The evaluation module 300 is used to calculate the target cumulative occurrence probability of the target fault type, and determine the powertrain fault occurrence probability model of the vehicle using the target cumulative occurrence probability, so as to generate a multi-factor powertrain fault evaluation result of the vehicle using the powertrain fault occurrence probability model.

[0167] Optionally, in one embodiment of the present application, the acquisition module includes: a recording unit and an acquisition unit.

[0168] The recording unit is used to record the powertrain fault information of the vehicle based on a preset fault recording format.

[0169] The acquisition unit is used to acquire target fault data of the vehicle according to the powertrain fault information.

[0170] Optionally, in one embodiment of the present application, the acquisition module includes: an identification unit and a first determination unit.

[0171] Wherein, the identification unit is used to identify the target fault cause of the vehicle using the target fault data.

[0172] The first determination unit is used to determine a target fault type of the vehicle according to a target fault cause.

[0173] Optionally, in one embodiment of the present application, the first determining unit includes: a judging subunit, a first determining subunit and a second determining subunit.

[0174] The judgment subunit is used to judge whether the target fault cause meets the preset same fault condition.

[0175] The first determination subunit is configured to determine that the target fault type is a preset same fault type if the target fault cause satisfies a preset same fault condition.

[0176] The second determination subunit is configured to determine that the target fault type is a preset non-same fault type if the target fault cause does not satisfy a preset same fault condition.

[0177] Optionally, in one embodiment of the present application, the device 10 of the embodiment of the present application further includes: a processing module and a determination module.

[0178] The processing module is used to determine the failure rate of the preset same failure type according to the target failure data after determining that the target failure type is the preset same failure type, and analyze the failure rate to obtain the analysis result.

[0179] The determination module is used to determine a first cumulative occurrence probability of the preset same fault type according to the analysis result after determining that the target fault type is the preset same fault type.

[0180] Optionally, in one embodiment of the present application, the evaluation module includes: a second determination unit and a third determination unit.

[0181] The second determination unit is used to determine a powertrain cumulative fault occurrence probability curve based on the first cumulative occurrence probability and a preset second cumulative occurrence probability of a non-identical fault type.

[0182] The third determination unit is used to determine a powertrain failure probability model of the vehicle by using the powertrain cumulative failure probability curve.

[0183] It should be noted that the above explanation of the embodiment of the method for evaluating vehicle multi-factor powertrain failure is also applicable to the device for evaluating vehicle multi-factor powertrain failure of this embodiment, and will not be repeated here.

[0184] According to the vehicle multi-factor powertrain fault assessment device proposed in the embodiment of the present application, the target fault data of the vehicle can be obtained according to the powertrain fault information, and the target fault data can be used to match the target fault type of the vehicle, and the target cumulative occurrence probability of the target fault type can be calculated, thereby determining the vehicle's powertrain fault occurrence probability model, and using the powertrain fault occurrence probability model to generate the vehicle's multi-factor powertrain fault assessment result, effectively improving the comprehensiveness and accuracy of the vehicle powertrain fault assessment. Therefore, the problems in the related art that complex faults cannot be tested, the comprehensiveness of fault testing is reduced, and the applicability and accuracy of vehicle powertrain fault assessment are reduced are solved.

[0185] Figure 4 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0186] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0187] When the processor 402 executes the program, the vehicle multi-factor powertrain fault assessment method provided in the above embodiment is implemented.

[0188] Furthermore, the vehicle also includes:

[0189] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0190] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0191] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0192] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0193] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0194] The processor 402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0195] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for evaluating multi-factor powertrain failures of a vehicle.

[0196] This embodiment also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned vehicle multi-factor powertrain fault assessment method.

[0197] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0198] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0199] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0200] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0201] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0202] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0203] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0204] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for evaluating vehicle multi-factor powertrain failure, characterized in that: The following steps are involved: Collect powertrain fault information of the vehicle under target driving conditions; Acquiring target fault data of the vehicle according to the powertrain fault information, and matching a target fault type of the vehicle using the target fault data; A target cumulative occurrence probability of the target fault type is calculated, and a powertrain fault occurrence probability model of the vehicle is determined using the target cumulative occurrence probability, so as to generate a multi-factor powertrain fault assessment result of the vehicle using the powertrain fault occurrence probability model.

2. The method according to claim 1, characterized in that The acquiring target fault data of the vehicle according to the powertrain fault information includes: Recording powertrain fault information of the vehicle based on a preset fault recording format; Target fault data of the vehicle is acquired according to the powertrain fault information.

3. The method according to claim 1, characterized in that The determining the target fault type of the vehicle by using the target fault data comprises: identifying a target fault cause of the vehicle using the target fault data; A target fault type of the vehicle is determined according to the target fault cause.

4. The method according to claim 3, characterized in that The determining the target fault type of the vehicle according to the target fault cause includes: Determine whether the target fault cause meets the preset same fault condition; If the target fault cause satisfies the preset same fault condition, determining the target fault type to be the preset same fault type; If the target fault cause does not satisfy the preset same fault condition, the target fault type is determined to be a preset non-same fault type.

5. The method according to claim 4, characterized in that After determining that the target fault type is the same as the preset fault type, the method further includes: Determine the failure rate of the preset same failure type according to the target failure data, and analyze the failure rate to obtain an analysis result; A first cumulative occurrence probability of the preset same fault type is determined according to the analysis result.

6. The method according to claim 5, characterized in that The calculating the target cumulative occurrence probability of the target fault type and determining the powertrain fault occurrence probability model of the vehicle using the target cumulative occurrence probability includes: Determining a powertrain cumulative fault occurrence probability curve based on the first cumulative occurrence probability and a preset second cumulative occurrence probability of a non-identical fault type; The powertrain cumulative failure occurrence probability curve is used to determine a powertrain failure occurrence probability model for the vehicle.

7. A vehicle multi-factor powertrain fault assessment device, characterized in that: include: A collection module, used to collect powertrain fault information of the vehicle under target driving conditions; an acquisition module, configured to acquire target fault data of the vehicle according to the powertrain fault information, and match a target fault type of the vehicle using the target fault data; An evaluation module is used to calculate a target cumulative occurrence probability of the target fault type, and determine a powertrain fault occurrence probability model of the vehicle using the target cumulative occurrence probability, so as to generate a multi-factor powertrain fault evaluation result of the vehicle using the powertrain fault occurrence probability model.

8. The device according to claim 7, characterized in that The acquisition module comprises: A recording unit, configured to record powertrain fault information of the vehicle based on a preset fault recording format; An acquisition unit is used to acquire target fault data of the vehicle according to the powertrain fault information.

9. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle multi-factor powertrain fault assessment method as described in any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the vehicle multi-factor powertrain fault assessment method as described in any one of claims 1-6.

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