CN113946972A - Automobile reliable life prediction method based on Internet of vehicles - Google Patents

Automobile reliable life prediction method based on Internet of vehicles Download PDF

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Publication number
CN113946972A
CN113946972A CN202111238898.2A CN202111238898A CN113946972A CN 113946972 A CN113946972 A CN 113946972A CN 202111238898 A CN202111238898 A CN 202111238898A CN 113946972 A CN113946972 A CN 113946972A
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fault
automobile
life
reliable
service life
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杨小娟
刘富佳
许书权
陈潮洲
邬果昉
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Research Institute of Highway Ministry of Transport
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/04—Ageing analysis or optimisation against ageing

Abstract

The invention discloses a method for predicting the reliable service life of an automobile based on an internet of vehicles, which utilizes a system for predicting the reliable service life of the automobile based on the internet of vehicles to realize the real-time acquisition, processing and analysis of vehicle operation information and fault data, thereby giving accurate, real-time and comprehensive judgment on the reliable service life of the automobile. The invention innovatively changes the prior after-sale maintenance data acquisition and analysis mode into the real-time online evaluation of the Internet of vehicles, adopts a least square method and a two-parameter Weibull distribution function to carry out reliable service life calculation, so that the measuring and calculating basis is more flexible and real, and different faults are classified and classified, so that the test result is more scientific and reasonable.

Description

Automobile reliable life prediction method based on Internet of vehicles
Technical Field
The invention relates to a vehicle performance evaluation technology, in particular to a method for predicting the reliable service life of an automobile based on an internet of vehicles.
Background
With the development of automobile technology towards intellectualization, networking and electromotion, the number and complexity of vehicle-mounted sensors and electronic devices are gradually increased, so that the traditional automobile maintenance business is not limited to mechanical fault maintenance any more, and further the diagnosis and maintenance of automobile electronic systems and parts are turned to. At present, the fault diagnosis of the electronic components of the automobile mainly depends on the fault code information reported by an automobile self-diagnosis system, and maintenance personnel carry out technical judgment and maintenance operation on the incoming vehicles by utilizing a diagnostic instrument on the basis of fault decoding.
The reliable service life of the automobile reflects the overall technical level and performance state of the automobile, is influenced by comprehensive factors such as the driving habit, the maintenance degree and the delivery quality level of the automobile, carries out scientific assessment and real-time calculation on the reliable service life of the automobile, and provides important technical basis for the technical research and development of automobile products, the improvement of the driving habit of the automobile owner, the optimization of the automobile maintenance period, the transaction assessment of the second-hand automobile and the like. In the past, reliability analysis and service life prediction of vehicles are mainly based on automobile maintenance records and fault data, not only a large amount of maintenance records need to be accumulated, but also fault information needs to be accurately and truly identified, so that the time delay performance is realized, and the service life and the reliability level of the vehicles cannot be accurately judged in real time and efficiently. Meanwhile, the requirements on the quantity of vehicle maintenance records and the data quality are high, the operation difficulty is high in practical application, the universal applicability is not achieved, and the technical evaluation of the vehicles in use cannot be met.
Disclosure of Invention
In view of the current situation, the invention follows the development trend of modern automobile technology, provides an automobile reliable service life prediction method based on the Internet of vehicles, focuses on utilizing an automobile reliable service life prediction system based on the Internet of vehicles to realize real-time acquisition, processing and analysis of data such as vehicle operation information, fault codes and the like, and realizes real-time online evaluation and prediction of the reliable service life of the vehicle by establishing an automobile reliable service life calculation method, thereby giving accurate, real-time and comprehensive judgment on the reliable service life of the automobile. The method updates the algorithm operation result in time according to the actual use condition, the maintenance condition and the fault occurrence condition of the vehicle in use, so that the system calculation has the characteristics of real time, intelligence, convenience and the like, is favorable for scientifically judging the technical state of the vehicle in use, reasonably developing the vehicle maintenance, improving the vehicle using habit of a vehicle owner and further having an important supporting function for prolonging the reliable service life of the vehicle.
The invention innovatively changes the prior after-sale maintenance data acquisition and analysis mode into the real-time online evaluation of the Internet of vehicles, so that the measuring and calculating basis is more flexible and real, and different faults are classified and classified, so that the measured result is more scientific and reasonable. The system disclosed by the invention covers various vehicle types, is high in intelligent degree and convenient and efficient to use, and the calculation model of the reliable service life of the vehicle can flexibly and truly reflect the actual use habits, maintenance conditions and other factors of the vehicle to influence the reliable service life of the vehicle, so that important technical references are provided for the evaluation of a second-hand vehicle, the quality evaluation of the vehicle types, the improvement of driving habits, the maintenance assistance and the like, the improvement of the use quality and the safety level of the vehicle is promoted, and the reliable service life of the vehicle is prolonged.
The technical scheme adopted by the invention is as follows:
a vehicle reliable life prediction method based on the Internet of vehicles realizes prediction by arranging a system comprising an OBD data acquisition interface, a data storage module, a data analysis processing module and a central controller ECU module on a vehicle;
the method comprises the following steps:
1) data acquisition and processing: acquiring basic operation parameters and fault parameter information of a vehicle in real time by using an OBD data acquisition interface;
2) and (3) fault analysis: according to fault information generated by the vehicle, finding a fault grade matched with the fault information and a corresponding equivalent fault coefficient from a fault classification library;
accumulating the accumulated mileage and the accumulated equivalent fault number corresponding to all the fault information, and establishing a relationship between the accumulated mileage and the accumulated equivalent fault number;
3) calculating the reliable service life of the automobile: and calculating the reliable service life of the automobile by using a two-parameter Weibull distribution function according to the relationship between the accumulated mileage and the accumulated equivalent fault number.
Further, the OBD data acquisition interface acquires basic operation parameter information and fault parameter information of the vehicle and stores the information in the data storage module;
the data storage module is internally provided with an automobile fault classification library, automobile historical operation information, automobile historical fault information and automobile reliable service life information;
and the data analysis processing module is internally provided with an automobile fault classification model and an automobile reliable service life calculation model.
Further, the fault grades are divided according to the hazard degree, and the higher the hazard degree is, the higher the corresponding equivalent fault coefficient is;
the cumulative equivalent number of faults was calculated as:
Figure BDA0003318493110000021
niis the equivalent fault coefficient per fault, and M is the number of occurrences of faults.
Further, establishing a relation between the accumulated mileage and the accumulated equivalent fault number by using a least square method:
Ni＝aLi+b (1)
ni is the cumulative equivalent number of faults, M is the number of occurrences of faults,
Figure BDA0003318493110000022
Figure BDA0003318493110000023
further, the two-parameter weibull distribution function is expressed as:
Figure BDA0003318493110000031
wherein m is a shape parameter, p is a scale parameter, t is time, and t belongs to [0, ∞);
let p equal to ηmThen there is
Figure BDA0003318493110000032
Finishing to obtain:
Figure BDA0003318493110000033
then it is obtained according to equation (1):
Figure BDA0003318493110000034
will be provided with
Figure BDA0003318493110000035
If m is substituted for formula (3), the distribution function f (t) is determined:
Figure BDA0003318493110000036
according to the known a and b, F (t) can be calculated, and the reliable service life of the automobile can be calculated by using the F (t).
The calculation of the reliable life characteristics of the automobile comprises the average life, the reliable life, the median life and the characteristic life, and the reliability R is given and calculated as follows:
average life:
Figure BDA0003318493110000037
reliable life:
Figure BDA0003318493110000038
median life: taking the R as 50 percent,
Figure BDA0003318493110000039
characteristic life: taking R as e-1，
te-1＝η。
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following remarkable beneficial effects:
1. compared with the traditional calculation mode of vehicle reliability, the method has the advantages that the real-time calculation and the prejudgment of the reliable service life of the vehicle are realized by innovatively utilizing the real-time monitoring data and the fault code information of the vehicle and relying on the vehicle networking technology, so that the factors of calculation delay, data acquisition difficulty, maintenance data non-standardization and the like of the traditional measurement and calculation mode are abandoned, the influence of the actual use habits, maintenance conditions and other factors of the vehicle on the reliable service life of the vehicle can be flexibly and truly reflected, and the accurate, real-time and comprehensive judgment on the reliable service life of the vehicle is further provided.
2. The traditional automobile use reliability analysis and service life prediction are failure information extraction analysis based on vehicle maintenance data, further classification and calculation are not carried out on various failures, and due to different vehicle use conditions of failure reactions of different degrees, classification and classification are carried out on automobile failures, failure distribution function calculation and service life prediction are carried out by calculating equivalent failure number, so that the measuring and calculating method and the result are more scientific and reasonable.
3. The invention calculates the reliable service life of the vehicle, provides rich evaluation indexes such as average service life, reliable service life, median service life, characteristic service life and the like, and can reflect the service condition and service life level of the vehicle from different layers.
4. The method for predicting the reliable service life of the automobile based on the internet of vehicles realizes real-time acquisition and comprehensive analysis and processing of the operation data and the fault information of the automobile, can cover various automobile types, has high intelligent degree and convenient and efficient use, meets the requirements of various users such as automobile owners, maintenance personnel, used automobile evaluators and the like to install and use on mobile phones, tablet personal computers and other equipment, has important reference function on the aspects of used automobile evaluation, automobile type quality evaluation, driving habit improvement, maintenance assistance and the like, is favorable for scientifically judging the influence degree of daily use and maintenance level of the automobile on the reliable service life of the automobile, and further promotes the extension of the reliable service life of the automobile.
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The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a diagram of an external contact layout implemented by the method of the present invention;
FIG. 2 is a diagram of an information processing architecture of an automotive reliable life prediction system;
FIG. 3 is a diagram of a data storage module architecture;
FIG. 4 is a block diagram of a data analysis processing module;
FIG. 5 is a diagram of a mobile application software architecture;
FIG. 6 is a flow chart of the method of the present invention.
Detailed Description
The present invention is described in detail below with reference to the accompanying drawings and examples, wherein the drawings and examples are only used for illustrating the present invention together, but it should be understood by those skilled in the art that the following examples are not intended to limit the technical solutions of the present invention, and any equivalent changes or modifications made within the spirit of the technical solutions of the present invention should be considered as falling within the protection scope of the present invention.
In order to realize the test of the reliable service life of the automobile, the invention provides a set of prediction system, as shown in fig. 1, the system is connected with the automobile through an OBD data acquisition interface, fault data calculation and reliable service life model calculation are carried out by acquiring the running information and the fault data of the automobile, and the running information, the fault data and the reliable service life measurement result of the automobile are uploaded to mobile end application software installed on mobile phones, tablet computers and the like to be displayed in a wireless signal remote data transmission mode.
As shown in fig. 2, the system comprises: the system comprises an OBD data acquisition interface, a data storage module, a data analysis processing module, a central controller ECU module, a wireless communication module, mobile terminal application software and the like.
The input end of the OBD data acquisition interface is connected to a CAN bus of an automobile control system in the form of a connector and the like, and the working conditions of an automobile electric control system and other functional modules are monitored in real time; the output end of the OBD data acquisition interface is connected with the input end of the data storage module, and the output end of the data storage module is connected with the input ends of the data analysis processing module and the wireless communication module; the output end of the wireless communication module is connected with the input end of the mobile terminal application software; the OBD data acquisition interface acquires basic operation data of the vehicle, reads a vehicle diagnosis fault code and stores the acquired data in the data storage module.
As shown in fig. 3, the data storage module includes a vehicle fault classification library, vehicle historical operation information, vehicle historical fault information, vehicle reliable life information, and the like.
As shown in fig. 4, the data analysis processing module includes an automobile fault classification model, an automobile reliable life calculation model, and the like, and calls an automobile fault classification library from the data storage module when the automobile fault classification model is calculated, calls historical vehicle operation information and historical vehicle fault information and the like in the data storage module when the automobile reliable life model is calculated, and transmits the obtained fault type and the automobile reliable life prediction result to the data storage module.
The central controller ECU module is connected with other components, monitors the running state of each component and realizes the regulation and control function on the running of the whole system.
The wireless communication module is connected with the data storage module, a wireless signal transmitter is embedded in the module, and automobile running information and automobile reliable service life calculation results in the data storage module are sent to the mobile terminal application software in a Bluetooth mode, a GPRS mode or a WIFI mode.
As shown in fig. 5, the mobile application software includes an automobile operation information query display module and an automobile reliable life calculation result display module, the application software is installed on a mobile phone, a tablet computer and other devices, a wireless signal receiver is embedded in the devices, and the devices receive and display the automobile historical operation information, the historical failure information, the automobile reliable life calculation result and the like sent by the wireless signal transmitter of the wireless communication module, and realize the autonomous query of the automobile historical operation information and the historical failure information in the automobile operation information query display module.
The invention provides a method for predicting the reliable service life of an automobile based on an internet of vehicles, which comprises the following basic steps as shown in figure 6:
1) data acquisition processing
Acquiring basic operation parameters and fault parameter information of a vehicle in real time by using an OBD data acquisition interface; the basic operation parameter information comprises but is not limited to vehicle driving mileage, driving speed, operation time, positioning information and the like, and the fault parameter information comprises but is not limited to fault code names, fault occurrence time, fault occurrence mileage and the like; storing the acquired basic vehicle operation parameter information into a vehicle historical operation information module of the data storage module, and storing the acquired vehicle fault parameter information into a vehicle historical fault information module of the data storage module to prepare for next data analysis;
furthermore, when the vehicle generates new operation parameters and fault parameter information, the data collected and transmitted to the data storage module by the OBD data collection interface can be updated in real time.
2) Fault analysis
Classifying faults: when a vehicle generates new fault code information, the vehicle fault classification model in the data analysis processing module calls a vehicle fault code classification library in the data storage module, the fault codes are matched with data in the fault classification library, faults are classified according to grades, and corresponding fault types and equivalent fault coefficients are obtained through equivalent fault coefficients corresponding to fault grades shown in the following table;
further, the fault types are classified into a very serious fault, a more serious fault, a general fault, and a minor fault according to the fault criticality.
Further, the equivalent fault coefficients of different types of faults are shown in the following table, and the set equivalent fault coefficient values are obtained by referring to related cases by technicians in a self-defined mode:
failure class Very serious fault More serious failure General failure Minor fault
Equivalent fault coefficient 5 2.5 1 0.2
Cumulative equivalent fault number calculation:
further, all fault information of the vehicle is summarized, and the accumulated mileage L of each fault occurrence is determinediI-0, 1, …, M, and the cumulative equivalent number of faults associated with the mileage
Figure BDA0003318493110000061
niIs the equivalent fault coefficient of each fault, and M is the number of times of occurrence of the fault, forming an array, as shown in the following table:
Figure BDA0003318493110000062
further, parameter information of the vehicle, such as fault code name, fault occurrence mileage, fault type, equivalent fault coefficient, accumulated equivalent fault number and the like, is stored as a group of data in a vehicle historical fault information module in the data storage module;
further, the vehicle historical fault information calculated and stored to the data storage module is updated in real time each time a new fault occurs in the vehicle.
3) Automobile reliable life calculation
The method comprises the following steps of carrying out automobile reliable service life calculation in an automobile reliable service life information calculation model of the data analysis processing module, and specifically comprises the following steps:
calculating parameters by least square method
Most of fault distribution rules belong to two-parameter Weibull distribution, and the distribution conditions are as follows: and obtaining related parameters and a distribution function by a least square method. Logarithmic sequence (L)i，Ni) (i-0, 1, …, M), consider LiAnd NiApproximately has a linear function relationship with each other,
namely Ni＝aLi+b (1)
Wherein a and b are undetermined constants, calculated by:
Figure BDA0003318493110000071
Figure BDA0003318493110000072
from the data given in the above examples, a-2.0221 and b-14.9727 were calculated.
Two parameter Weibull distribution function calculation
The known two-parameter weibull distribution function is:
Figure BDA0003318493110000073
wherein m is a shape parameter, p is a scale parameter, t is time, and t belongs to [0, ∞);
let p equal to ηmThen there is
Figure BDA0003318493110000074
Finishing to obtain:
Figure BDA0003318493110000075
then it is obtained according to equation (1):
Figure BDA0003318493110000081
substituting the obtained a and b into the formula to obtain the values of the parameters p and m, and further determining a distribution function F (t);
Figure BDA0003318493110000082
according to the above-mentioned examples, eta is 1643.4122, m is 2.0221,
then
Figure BDA0003318493110000083
Calculating reliable life characteristic of automobile
The calculation of the reliable service life characteristics of the automobile comprises average service life, reliable service life, median service life, characteristic service life and the like.
The following are calculated respectively:
average life: average value of normal working time of all products, use
Figure BDA0003318493110000089
Represents:
Figure BDA0003318493110000084
to obtain
Figure BDA0003318493110000085
Reliable life: maximum achievable working life at a given degree of reliability R, in tRRepresents:
Figure BDA0003318493110000086
when R is 0.9, t is obtained0.9＝540km；
Median life: operating life with a reliability of R50%, using t0.5Represents:
by
Figure BDA0003318493110000087
Get t0.5＝1371km；
Characteristic life: reliability of R ═ e-1Working life of, with te-1 represents:
by
Figure BDA0003318493110000088
Get te-1＝1643km；
Further, when a new fault occurs in the vehicle, the vehicle reliable service life calculation model can reestablish a distribution function according to the mileage data and the accumulated fault information to calculate the vehicle reliable service life, and the calculation result is updated in the mobile terminal application software in real time, so that the actual service condition of the vehicle is reflected. Therefore, the reliable service life of the automobile can be calculated in real time according to the fault updating of the automobile, and the reliability of the automobile can be predicted.

Claims (6)

1. A method for predicting the reliable service life of an automobile based on the Internet of vehicles is characterized by comprising the following steps:
the vehicle is provided with a system comprising an OBD data acquisition interface, a data storage module, a data analysis processing module and a central controller ECU module to realize prediction;
the method comprises the following steps:
1) data acquisition and processing: acquiring basic operation parameters and fault parameter information of a vehicle in real time by using an OBD data acquisition interface;
2) and (3) fault analysis: according to fault information generated by the vehicle, finding a fault grade matched with the fault information and a corresponding equivalent fault coefficient from a fault classification library;
accumulating the accumulated mileage and the accumulated equivalent fault number corresponding to all the fault information, and establishing a relationship between the accumulated mileage and the accumulated equivalent fault number;
3) calculating the reliable service life of the automobile: and calculating the reliable service life of the automobile by using a two-parameter Weibull distribution function according to the relationship between the accumulated mileage and the accumulated equivalent fault number.
2. The method for predicting the reliable life of the automobile based on the Internet of vehicles as claimed in claim 1, wherein:
the OBD data acquisition interface acquires basic operation parameter information and fault parameter information of the vehicle and stores the information in the data storage module;
the data storage module is internally provided with an automobile fault classification library, automobile historical operation information, automobile historical fault information and automobile reliable service life information;
and the data analysis processing module is internally provided with an automobile fault classification model and an automobile reliable service life calculation model.
3. The method for predicting the reliable life of the automobile based on the Internet of vehicles as claimed in claim 1, wherein: the fault grades are divided according to the hazard degree, and the higher the hazard degree is, the higher the corresponding equivalent fault coefficient is;
the cumulative equivalent number of faults was calculated as:
Figure FDA0003318493100000011
niis the equivalent fault coefficient per fault, and M is the number of occurrences of faults.
4. The method for predicting the reliable life of the automobile based on the Internet of vehicles as claimed in claim 1, wherein: and establishing a relation between the accumulated mileage and the accumulated equivalent fault number by using a least square method:
Ni＝aLi+b (1)
Niis the cumulative equivalent number of faults, M is the number of times of occurrence of faults,
Figure FDA0003318493100000021
Figure FDA0003318493100000022
5. the method for predicting the reliable life of the automobile based on the Internet of vehicles as claimed in claim 4, wherein:
the two-parameter weibull distribution function is expressed as:
Figure FDA0003318493100000023
wherein m is a shape parameter, p is a scale parameter, t is time, and t belongs to [0, ∞);
let p equal to ηmThen there is
Figure FDA0003318493100000024
Finishing to obtain:
Figure FDA0003318493100000025
then it is obtained according to equation (1):
Figure FDA0003318493100000026
L＝ln t，
Figure FDA0003318493100000027
m＝a；
will be provided with
Figure FDA0003318493100000028
If m is substituted for formula (3), the distribution function f (t) is determined:
Figure FDA0003318493100000029
from the known a, b, f (t) can be calculated.
6. The method for predicting the reliable life of the automobile based on the Internet of vehicles as claimed in claim 5, wherein:
the calculation of the reliable life characteristics of the automobile comprises the average life, the reliable life, the median life and the characteristic life, and the reliability R is given and calculated as follows:
average life:
Figure FDA0003318493100000031
reliable life:
Figure FDA0003318493100000032
median life: taking the R as 50 percent,
Figure FDA0003318493100000033
characteristic life: taking R as e-1，
Figure FDA0003318493100000034
CN202111238898.2A 2021-10-25 2021-10-25 Automobile reliable life prediction method based on Internet of vehicles Pending CN113946972A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116720852A (en) * 2023-08-08 2023-09-08 山东理工职业学院 New energy automobile maintenance data analysis management system based on artificial intelligence

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116720852A (en) * 2023-08-08 2023-09-08 山东理工职业学院 New energy automobile maintenance data analysis management system based on artificial intelligence
CN116720852B (en) * 2023-08-08 2023-10-31 山东理工职业学院 New energy automobile maintenance data analysis management system based on artificial intelligence

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