CN115061451A - Automobile fault diagnosis method and device, intelligent terminal and storage medium - Google Patents
Automobile fault diagnosis method and device, intelligent terminal and storage medium Download PDFInfo
- Publication number
- CN115061451A CN115061451A CN202210666158.7A CN202210666158A CN115061451A CN 115061451 A CN115061451 A CN 115061451A CN 202210666158 A CN202210666158 A CN 202210666158A CN 115061451 A CN115061451 A CN 115061451A
- Authority
- CN
- China
- Prior art keywords
- fault
- data
- vehicle
- sample data
- data stream
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Abstract
The invention discloses a method, a device and a terminal for diagnosing automobile faults. Compared with the prior art, the fault model is established on line according to the real-time detection data instead of comparing the numerical values of the detection data according to the determined rule, so that the automobile fault which is not covered by the OBD diagnostic system can be predicted, and the automobile fault can also be positively predicted when the input parameters are changed due to the aging of the automobile, so that the detection data of the OBD diagnostic system can be secondarily diagnosed, the OBD diagnostic system is a supplement to the OBD diagnostic system, and the accuracy of detecting the automobile fault is improved.
Description
Technical Field
The invention relates to the technical field of automobile fault diagnosis, in particular to an automobile fault diagnosis method and device, an intelligent terminal and a storage medium.
Background
With the rapid development of the automobile industry, the structure of the automobile becomes more and more complex, the automation degree becomes higher and more, and the electronic control system of the whole automobile becomes more and more complex. The running state and the abnormity of the automobile are judged by intelligent fault diagnosis technology so as to enhance the safety performance of the automobile and ensure the driving safety.
The existing automobile fault diagnosis technology mainly depends On a vehicle-mounted OBD diagnosis system (On Board Diagnostics: vehicle-mounted automatic diagnosis system), however, the OBD diagnosis system depends heavily On logic rules determined according to expert knowledge, is difficult to realize comprehensive coverage of various faults, and cannot realize diagnosis of unknown faults; and the aging of parts, sensors and the like in the use of the vehicle can change the input parameters of the controller, so that the OBD diagnosis system has misjudgment.
Accordingly, there is a need in the art for improvements and enhancements.
Disclosure of Invention
The invention mainly aims to provide an automobile fault diagnosis method, an automobile fault diagnosis device, an intelligent terminal and a storage medium, which can intelligently predict the automobile fault uncovered by an OBD diagnosis system and improve the accuracy of detecting the automobile fault.
In order to achieve the above object, a first aspect of the present invention provides a vehicle fault diagnosis method, including:
acquiring a fault diagnosis result of a vehicle-mounted diagnosis system and a fault data stream corresponding to the fault diagnosis result, wherein the fault data stream comprises detection data of at least one vehicle-mounted sensor;
calculating the similarity between the fault data stream and each sample data in a pre-established sample data base, and screening all the sample data according to the similarity to obtain screened sample data;
performing online modeling based on the screened sample data to obtain a fault model for representing the relation between the detection data and the automobile fault;
inputting the fault data stream into the fault model to obtain a fault prediction result;
and searching the fault prediction result in the fault diagnosis result, and outputting the fault prediction result when the fault prediction result is not found.
Optionally, the calculating the similarity between the fault data stream and sample data in a pre-established sample database includes:
calculating the distance and angle between the sample data and the fault data stream;
and obtaining the similarity between the fault data stream and the sample data based on the distance and the angle.
Optionally, the expression for obtaining the similarity between the fault data stream and the sample data based on the distance and the angle is as follows:
wherein γ is a weight parameter with a value between 0 and 1, θ i Is a failed data stream Z q And sample data Z i D is the fault data stream Z q And sample data Z i The euclidean distance between them.
Optionally, the performing online modeling based on the screened sample data to obtain a fault model for characterizing a relationship between the detection data and the vehicle fault includes:
and generating the fault model according to the screened sample data by adopting a local weighted linear regression method based on an autoregressive ergodic model.
Optionally, the screening is performed on all the sample data according to the similarity, and obtaining the screened sample data includes:
and setting the sample data with the maximum similarity as the screened sample data.
Optionally, the storing the fault data stream and the fault diagnosis result in a real-time database in advance according to a time sequence, and the obtaining the fault diagnosis result of the vehicle-mounted diagnosis system and the fault data stream corresponding to the fault diagnosis result includes:
and acquiring the fault data stream and the fault diagnosis result according to a time sequence based on the real-time database.
Optionally, the outputting the failure prediction result includes:
acquiring a fault processing scheme based on the fault prediction result;
and transmitting the fault prediction result and the fault processing scheme to an automobile networking system of an automobile.
A second aspect of the present invention provides an automobile fault diagnosis apparatus, wherein the apparatus comprises:
the data acquisition module is used for acquiring a fault diagnosis result of the vehicle-mounted diagnosis system and a fault data stream corresponding to the fault diagnosis result, wherein the fault data stream comprises detection data of at least one vehicle-mounted sensor;
the sample screening module is used for calculating the similarity between the fault data stream and each sample data in a pre-established sample database, and screening all the sample data according to the similarity to obtain the screened sample data;
the online modeling module is used for performing online modeling on the basis of the screened sample data to obtain a fault model for representing the relation between the detection data and the automobile fault;
the prediction module is used for inputting the fault data stream into the fault model to obtain a fault prediction result;
and the output module is used for searching the fault prediction result in the fault diagnosis result and outputting the fault prediction result when the fault prediction result is not found.
A third aspect of the present invention provides an intelligent terminal, where the intelligent terminal includes a memory, a processor, and a vehicle fault diagnosis program stored in the memory and operable on the processor, and the vehicle fault diagnosis program implements any one of the steps of the vehicle fault diagnosis method when executed by the processor.
A fourth aspect of the present invention provides a computer-readable storage medium, wherein a vehicle fault diagnosis program is stored on the computer-readable storage medium, and when being executed by a processor, the computer-readable storage medium implements any one of the steps of the vehicle fault diagnosis method.
Therefore, the fault data stream of the OBD diagnosis system is matched in the sample database, so that the fault model is established on line according to the matched sample data, the fault prediction result of the fault data stream is obtained according to the fault model, and the fault prediction result is output when the fault prediction result is a fault which is not diagnosed by the OBD diagnosis system. Compared with the prior art, the fault model is established on line according to the real-time detection data instead of comparing the numerical values of the detection data according to the determined rule, so that the automobile fault which is not covered by the OBD diagnostic system can be predicted, and the automobile fault can also be positively predicted when the input parameters are changed due to the aging of the automobile, so that the detection data of the OBD diagnostic system can be secondarily diagnosed, the OBD diagnostic system is a supplement to the OBD diagnostic system, and the accuracy of detecting the automobile fault is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic diagram of an automotive fault diagnosis system architecture according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for diagnosing vehicle faults according to an embodiment of the present invention;
FIG. 3 is a block diagram of a fault diagnosis module of the embodiment of FIG. 2;
FIG. 4 is a detailed flowchart illustrating the calculation of similarity in step S200 in the embodiment of FIG. 1;
FIG. 5 is a schematic structural diagram of a vehicle fault diagnosis device provided by an embodiment of the invention;
fig. 6 is a schematic block diagram of an internal structure of an intelligent terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when …" or "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted depending on the context to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings of the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
The safety and reliability of the electric automobile are important indexes for measuring the quality of the electric automobile. The automobile fault diagnosis technology judges the running state and the abnormity of the electric automobile, so that the safety performance of the electric automobile is enhanced, and the driving safety is ensured.
The existing automobile fault diagnosis technology mainly depends on a vehicle-mounted OBD diagnosis system, and the system diagnoses based on a determined logic rule, namely only can judge relatively obvious fault information according to an established expert diagnosis system. However, in many cases, the automobile power system is not normal in performance, such as high idle speed, flameout in midway, and the like, which is caused by the coordination among a plurality of parts, the OBD diagnosis system cannot cover such situations, that is, cannot diagnose unknown faults of the automobile; due to the logic rules established based on expert cognition, once the knowledge level is low or other factors interfere, the fault diagnosis result can be seriously influenced, so that fault misjudgment is caused, and the safety of vehicle driving is seriously threatened; when the vehicle is in use, the input parameters of the controller can be changed due to aging of parts, sensors and the like, and the OBD diagnosis system can also be misjudged.
According to the vehicle-mounted OBD diagnosis system and the vehicle-mounted OBD diagnosis method, online modeling is carried out according to the acquired vehicle-mounted sensor data, a fault prediction result is obtained in real time, if the fault prediction result is different from a fault diagnosis result of the vehicle-mounted OBD diagnosis system, a vehicle owner is informed, the driving safety of the vehicle owner is guaranteed, and the vehicle-mounted OBD diagnosis system is a supplement of the vehicle-mounted OBD diagnosis system.
Exemplary method
The embodiment of the invention establishes a remote fault diagnosis system, the architecture of which is shown in figure 1, and the automobile fault diagnosis method is coded into a secondary diagnosis module to run on a background server. Each vehicle-mounted controller module in the decision application layer is connected with a vehicle-mounted sensor of the sensing layer through communication modes such as a hard wire, a LIN (serial communication network), a CAN (serial communication protocol), a CAN with Flexible Data rate, a vehicle-mounted Ethernet and the like, the vehicle-mounted sensor collects relevant Data of the automobile, relevant fault logic judgment is carried out through a vehicle-mounted OBD diagnosis system, and information and fault information of each vehicle-mounted sensor are uploaded to a background server through a vehicle networking host. It should be noted that, although the present embodiment deploys the vehicle fault diagnosis method on the remote server, it may also be deployed on other devices such as the internet of vehicles host.
Specifically, as shown in fig. 2, the method for diagnosing the vehicle fault includes the following steps:
step S100: acquiring a fault diagnosis result of a vehicle-mounted diagnosis system and a fault data stream corresponding to the fault diagnosis result, wherein the fault data stream comprises detection data of at least one vehicle-mounted sensor;
specifically, the OBD diagnostic system monitors systems and components of the engine, catalytic converter, particulate trap, oxygen sensor, emission control system, fuel system, EGR (exhaust gas recirculation), etc., in real time. The detection data of the vehicle-mounted sensor is transmitted to an ECU (electronic control unit), the ECU compares the detection data in numerical value, and whether the automobile breaks down or not is judged according to a determined logic rule. When a fault occurs, the ECU records fault information and a fault code, and gives a warning through a fault lamp to inform the driver. Through a standard data interface of the ECU, fault information including a fault data stream composed of various kinds of detection data corresponding to the fault diagnosis result and these detection data can be acquired by means of a general scanning tool.
In this example, as shown in FIG. 3, a 1 、a 2 ...a n ；b 1 、b 2 …b n ；...z 1 、z 2 …z n Respectively fault data streams corresponding to different faults; y is 1 、y 2 …y n In the diagnosis system of each controller module of the vehicle based on the on-board OBDThe obtained fault diagnosis result is judged by the logic rule(s). Then combining the fault data stream and the fault diagnosis result into y 1 、a 1 、a 2 ...a n ；y 2 、b 1 、b 2 …b n And uploading the data vectors to a background server.
Step S200: calculating the similarity between the fault data stream and each sample data in a pre-established sample data base, and screening all the sample data according to the similarity to obtain screened sample data;
specifically, various failure results and detection data corresponding to the failure results are collected in advance and stored in a sample database as sample data. Preferably, a sample database is established according to the collected data in the automobile data monitoring platform of each automobile manufacturer. And then, calculating the similarity between the fault data stream and the detection data in the sample data, and determining the sample data closest to the current fault data stream, namely the sample data after screening according to the similarity.
In this embodiment, the vehicle database of the background server includes a sample database and a real-time database, where the sample database is used to store fault results and corresponding fault data streams that are pre-collected by the real vehicle, and the real-time database is used to store fault data streams and diagnostic results that are uploaded by the vehicle in real time.
Optionally, feature extraction may be performed on the fault data stream in advance to obtain data features of the fault data stream, the data features are stored in the sample database, and during comparison, feature extraction is performed on the fault data stream in the real-time database, and then similarity matching is performed on the fault data stream and the data features in the sample database, so as to improve efficiency and speed of similarity matching.
Because the fault model needs to be generated on line according to the sample data after screening, the embodiment only sets the sample data with the maximum similarity as the sample data after screening in consideration of the calculation efficiency. Optionally, a similarity threshold may also be set, and a plurality of sample data greater than the similarity threshold are used as the sample data after the filtering.
Step S300: performing online modeling based on the screened sample data to obtain a fault model;
specifically, because knowledge describes the characteristics of a working point neighborhood, the detection data of the vehicle-mounted sensor and the corresponding fault result can be regarded as linear correlation, and therefore real-time online modeling can be achieved by selecting a simple linear model. And then, optimizing or fitting a linear model by taking the detection data in the screened sample data as an input value and taking the fault result in the screened sample data as an output value to obtain a fault model. It should be emphasized that the fault models obtained according to different sample data are also different in the present invention, that is, the fault data stream composed of the detection data of the in-vehicle sensor also corresponds to different fault models.
In this embodiment, an autoregressive ergodic model (ARX model) is selected, and the structure of the ARX model is as follows:
where the vector z (k) is [ y (k-1) ], y (k-n) y ),u 1 (k-n u ),...u m (k),...,u m (k-n u )] T Ψ is a model parameter vector
Because different data may contribute differently to the modeling accuracy, the model is derived based on a locally weighted linear regression equation, and the specific steps include:
assuming that there are N modeling data, then
y＝[y 1 ,y 2 ,...,y n ] T Where y is the column vector formed by the corresponding time instant outputs. Defining a modeling loss function: j (theta) ═ Z theta-y T W' (Z θ -y), and then deriving J (θ) from θ:
let the derivative value be 0, then
X T W′Xθ＝X T W′y
θ＝(X T W′X) -1 X T W′y，
The linear model can be described as:
And then, optimizing or fitting a linear model by taking the detection data in the screened sample data as an input value and the fault result in the screened sample data as an output value to obtain a fault model.
It should be noted that, in the present invention, a fault model at a corresponding time is generated according to a fault data stream input at a current time, and after a fault prediction result is obtained by analyzing the fault data stream at the current time using the fault model, the fault model is discarded, and a new fault model is generated by waiting for the fault data stream at the next time.
Step S400: inputting the fault data stream into a fault model to obtain a fault prediction result;
step S500: and searching a fault prediction result in the fault diagnosis result, and outputting the fault prediction result when the fault prediction result is not found.
Specifically, after the fault model is generated, the fault data stream obtained in real time is input into the fault model, and then the fault prediction result can be obtained. And matching the predicted result with the fault diagnosis result, if the fault prediction result is not found in the diagnosis result of the on-board OBD diagnosis system, that is, the fault data stream is not covered by the OBD diagnosis system or the OBD diagnosis system makes a misjudgment due to the change of parameters, and then outputting the fault prediction result, and timely notifying the owner of the vehicle to attract attention. The failure prediction result may be a binary result of a predetermined failure type, or may be a probability of the failure type. Alternatively, a residual value between the predicted result and the fault diagnosis result may be calculated, and if the residual value exceeds a set threshold range, the fault prediction result is output.
In the embodiment, the fault prediction result is transmitted to the background server, the fault processing scheme is searched in the background service according to the fault prediction result, the fault prediction result and the fault processing scheme are transmitted to the vehicle networking system of the automobile, namely, the fault prediction result and the fault processing scheme are transmitted to the vehicle networking host through the background server, and the vehicle networking host reminds the current fault information and relevant processing measures of an automobile owner, so that effective supplement of the vehicle-mounted OBD diagnosis system is formed, the vehicle fault detection accuracy is improved, and the vehicle driving safety is guaranteed.
In summary, in this embodiment, sample data most similar to the current vehicle state is searched from the sample database at each moment, and online modeling is performed, so that the established fault model can represent the input and output characteristics at that moment. And then, further analyzing the obtained fault data stream according to the fault model to obtain a fault prediction result, so as to realize secondary diagnosis and real-time online prediction of the fault data stream. Because the vehicle fault is diagnosed by comparing the detected data of the sensor with the expert system by adopting a determined logic rule, and the fault is predicted by adopting intelligent network simulation, even if the deviation of the detected values is caused by aging of the sensor and the vehicle controller, the vehicle fault can be correctly predicted, and the vehicle fault which is not covered by the OBD diagnostic system can be predicted, so that the vehicle fault detection result is more accurate.
In this embodiment, because the uploaded fault data stream and the obtained prediction result may be executed asynchronously, the fault data stream and the fault diagnosis result are stored in the real-time database in a data vector manner according to a time sequence in advance, then the fault data stream in the real-time database is sequentially read according to the time sequence to perform fault prediction, and compared with the fault diagnosis result, and whether to transmit the fault data stream to the host of the internet of vehicles is determined according to the comparison result. Therefore, the real-time database is continuously updated along with the continuous uploading of the real-time data of the vehicle, and the fault model of the vehicle is continuously rolled and matched on line so as to meet the requirements of vehicle output real-time prediction and fault detection.
In an embodiment, as shown in fig. 4, the calculating the similarity in step S200 specifically includes the following steps:
step S210: calculating the distance and angle between the sample data and the fault data stream;
step S220: and obtaining the similarity between the fault data stream and the sample data based on the distance and the angle.
Specifically, the similarity is used for evaluating the similarity between the sample database and the fault data stream, namely, the sample data which can represent the current vehicle state most is selected from the sample database. In this embodiment, the sample data includes a sample fault data stream and a sample fault result, and the similarity is calculated according to the sample fault data stream and the real-time fault data stream.
The conventional method for calculating the similarity between samples generally measures the distance between a query vector and a sample vector in a database directly, and usually adopts the euclidean distance:
wherein Z is i Representing sample vectors in a database, Z q Representing the query vector at the current time.
However, the present embodiment considers that there are two measurement methods for the relative position of the midpoint in space: distance and angle, and therefore angle, are also added to the metric, namely:
then based on the distance and the angle, calculating the similarity between the fault data stream and the sample data according to the following expression:
wherein γ is a weight parameter with a value between 0 and 1, θ i Is a failed data stream Z q And sample data Z i D is the fault data stream Z q And sample data Z i The euclidean distance between them. Only when cos (theta) i ) When the data flow is more than or equal to 0, the fault data flow is related to the sample data, the similarity index is meaningful, otherwise, the current and sample data are directly discarded.
By defining the similarity index, the range is [0,1], the more similar the fault data stream is to the sample data, the closer the index is to 1, and the sample data in the database is selected through the index.
From the above, by designing the similarity calculation formula and considering the distance and angle between the sample data and the fault data stream, the sample data which can represent the current vehicle state most can be selected from the sample database according to the index.
Exemplary device
As shown in fig. 5, corresponding to the above-mentioned automobile fault diagnosis method, an embodiment of the present invention further provides an automobile fault diagnosis apparatus, where the automobile fault diagnosis apparatus includes:
a data obtaining module 600, configured to obtain a fault diagnosis result of a vehicle-mounted diagnosis system and a fault data stream corresponding to the fault diagnosis result, where the fault data stream includes detection data of at least one vehicle-mounted sensor;
the sample screening module 610 is configured to calculate a similarity between the fault data stream and each sample data in a pre-established sample database, and perform screening on all the sample data according to the similarity to obtain screened sample data;
the online modeling module 620 is used for performing online modeling based on the screened sample data to obtain a fault model for representing the relationship between the detection data and the automobile fault;
a prediction module 630, configured to input the fault data stream into the fault model, so as to obtain a fault prediction result;
and the output module 640 is configured to search the fault prediction result in the fault diagnosis result, and output the fault prediction result if the fault prediction result is not found.
Specifically, in this embodiment, the specific functions of each module of the vehicle fault diagnosis device may refer to the corresponding descriptions in the vehicle fault diagnosis method, and are not described herein again.
Based on the above embodiment, the present invention further provides an intelligent terminal, and a schematic block diagram thereof may be as shown in fig. 6. The intelligent terminal comprises a processor, a memory, a network interface and a display screen which are connected through a system bus. Wherein, the processor of the intelligent terminal is used for providing calculation and control capability. The memory of the intelligent terminal comprises a nonvolatile storage medium and an internal memory. The nonvolatile storage medium stores an operating system and a vehicle failure diagnosis program. The internal memory provides an environment for the operation of an operating system and a vehicle failure diagnosis program in the nonvolatile storage medium. The network interface of the intelligent terminal is used for being connected and communicated with an external terminal through a network. When being executed by a processor, the automobile fault diagnosis program realizes the steps of any one of the automobile fault diagnosis methods. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen.
It will be understood by those skilled in the art that the block diagram of fig. 6 is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation to the intelligent terminal to which the solution of the present invention is applied, and a specific intelligent terminal may include more or less components than those shown in the figure, or combine some components, or have different arrangements of components.
In one embodiment, an intelligent terminal is provided, where the intelligent terminal includes a memory, a processor, and a vehicle fault diagnosis program stored in the memory and executable on the processor, and the vehicle fault diagnosis program performs the following operation instructions when executed by the processor:
acquiring a fault diagnosis result of a vehicle-mounted diagnosis system and a fault data stream corresponding to the fault diagnosis result, wherein the fault data stream comprises detection data of at least one vehicle-mounted sensor;
calculating the similarity between the fault data stream and each sample data in a pre-established sample data base, and screening all the sample data according to the similarity to obtain screened sample data;
performing online modeling based on the screened sample data to obtain a fault model for representing the relation between the detection data and the automobile fault;
inputting the fault data stream into the fault model to obtain a fault prediction result;
and searching the fault prediction result in the fault diagnosis result, and outputting the fault prediction result when the fault prediction result is not found.
Optionally, the calculating the similarity between the fault data stream and sample data in a pre-established sample database includes:
calculating the distance and angle between the sample data and the fault data stream;
and obtaining the similarity between the fault data stream and the sample data based on the distance and the angle.
Optionally, the expression for obtaining the similarity between the fault data stream and the sample data based on the distance and the angle is as follows:
wherein y is a weight parameter with the value between 0 and 1, and theta i Is a faulty data stream Z q And sample data Z i D is the fault data stream Z q And sample data Z i The euclidean distance between them.
Optionally, the performing online modeling based on the screened sample data to obtain a fault model for characterizing a relationship between the detection data and the vehicle fault includes:
and generating the fault model according to the screened sample data by adopting a local weighted linear regression method based on the autoregressive ergodic model.
Optionally, the screening is performed on all the sample data according to the similarity, and obtaining the screened sample data includes:
and setting the sample data with the maximum similarity as the screened sample data.
Optionally, the storing the fault data stream and the fault diagnosis result in a real-time database in advance according to a time sequence, and the obtaining the fault diagnosis result of the vehicle-mounted diagnosis system and the fault data stream corresponding to the fault diagnosis result includes:
and acquiring the fault data stream and the fault diagnosis result according to a time sequence based on the real-time database.
Optionally, the outputting the failure prediction result includes:
acquiring a fault processing scheme based on the fault prediction result;
and transmitting the fault prediction result and the fault processing scheme to an automobile networking system of an automobile.
The embodiment of the invention also provides a computer-readable storage medium, wherein the computer-readable storage medium is stored with an automobile fault diagnosis program, and the automobile fault diagnosis program realizes the steps of any automobile fault diagnosis method provided by the embodiment of the invention when being executed by a processor.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned functions may be distributed as different functional units and modules according to needs, that is, the internal structure of the apparatus may be divided into different functional units or modules to implement all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the present invention. For the specific working processes of the units and modules in the system, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the above modules or units is only one logical division, and the actual implementation may be implemented by another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The integrated modules/units described above, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments described above may be implemented. The computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file or some intermediate form. The computer readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, software distribution medium, etc. It should be noted that the contents contained in the computer-readable storage medium can be increased or decreased as required by legislation and patent practice in the jurisdiction.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.
Claims (10)
1. The automobile fault diagnosis method is characterized by comprising the following steps:
acquiring a fault diagnosis result of a vehicle-mounted diagnosis system and a fault data stream corresponding to the fault diagnosis result, wherein the fault data stream comprises detection data of at least one vehicle-mounted sensor;
calculating the similarity between the fault data stream and each sample data in a pre-established sample data base, and screening all the sample data according to the similarity to obtain screened sample data;
performing online modeling based on the screened sample data to obtain a fault model for representing the relation between the detection data and the automobile fault;
inputting the fault data stream into the fault model to obtain a fault prediction result;
and searching the fault prediction result in the fault diagnosis result, and outputting the fault prediction result when the fault prediction result is not found.
2. The method for diagnosing vehicle failure according to claim 1, wherein the calculating the similarity between the failure data stream and the sample data in the pre-established sample database includes:
calculating the distance and angle between the sample data and the fault data stream;
and obtaining the similarity between the fault data stream and the sample data based on the distance and the angle.
3. The vehicle fault diagnosis method according to claim 2, wherein the expression for obtaining the similarity between the fault data stream and the sample data based on the distance and the angle is:
wherein γ is a weight parameter with a value between 0 and 1, θ i Is a faulty data stream Z q And sample data Z i D is the fault data stream Z q And sample dataZ i The euclidean distance between them.
4. The method for diagnosing the automobile fault according to claim 1, wherein the online modeling based on the screened sample data to obtain a fault model for characterizing a relationship between the detection data and the automobile fault comprises:
and generating the fault model according to the screened sample data by adopting a local weighted linear regression method based on an autoregressive ergodic model.
5. The method according to claim 1, wherein the step of performing screening according to the similarity among all the sample data to obtain screened sample data comprises:
and setting the sample data with the maximum similarity as the screened sample data.
6. The vehicle fault diagnosis method according to claim 1, wherein the step of storing the fault data stream and the fault diagnosis result in a real-time database in advance according to a time sequence, and the step of obtaining the fault diagnosis result of the vehicle-mounted diagnosis system and the fault data stream corresponding to the fault diagnosis result comprises:
and acquiring the fault data stream and the fault diagnosis result according to a time sequence based on the real-time database.
7. The vehicle failure diagnosis method according to claim 1, wherein the outputting the failure prediction result includes:
acquiring a fault processing scheme based on the fault prediction result;
and transmitting the fault prediction result and the fault processing scheme to an automobile networking system of an automobile.
8. Vehicle failure diagnosis apparatus, characterized in that the apparatus comprises:
the data acquisition module is used for acquiring a fault diagnosis result of the vehicle-mounted diagnosis system and a fault data stream corresponding to the fault diagnosis result, wherein the fault data stream comprises detection data of at least one vehicle-mounted sensor;
the sample screening module is used for calculating the similarity between the fault data stream and each sample data in a pre-established sample data base, and screening all the sample data according to the similarity to obtain the screened sample data;
the online modeling module is used for performing online modeling on the basis of the screened sample data to obtain a fault model for representing the relation between the detection data and the automobile fault;
the prediction module is used for inputting the fault data stream into the fault model to obtain a fault prediction result;
and the output module is used for searching the fault prediction result in the fault diagnosis result and outputting the fault prediction result when the fault prediction result is not found.
9. An intelligent terminal, characterized in that the intelligent terminal comprises a memory, a processor and a vehicle fault diagnosis program stored on the memory and operable on the processor, wherein the vehicle fault diagnosis program, when executed by the processor, implements the steps of the vehicle fault diagnosis method according to any one of claims 1 to 7.
10. Computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a vehicle failure diagnosis program, which when executed by a processor implements the steps of the vehicle failure diagnosis method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210666158.7A CN115061451A (en) | 2022-06-14 | 2022-06-14 | Automobile fault diagnosis method and device, intelligent terminal and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210666158.7A CN115061451A (en) | 2022-06-14 | 2022-06-14 | Automobile fault diagnosis method and device, intelligent terminal and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115061451A true CN115061451A (en) | 2022-09-16 |
Family
ID=83201196
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210666158.7A Pending CN115061451A (en) | 2022-06-14 | 2022-06-14 | Automobile fault diagnosis method and device, intelligent terminal and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115061451A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116880442A (en) * | 2023-07-03 | 2023-10-13 | 广州汽车集团股份有限公司 | Fault diagnosis method, device, electronic equipment and storage medium |
-
2022
- 2022-06-14 CN CN202210666158.7A patent/CN115061451A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116880442A (en) * | 2023-07-03 | 2023-10-13 | 广州汽车集团股份有限公司 | Fault diagnosis method, device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111414477A (en) | Vehicle fault automatic diagnosis method, device and equipment | |
| JP6830540B2 (en) | Cloud-based vehicle failure diagnostic methods, equipment and systems | |
| US7751955B2 (en) | Diagnostics data collection and analysis method and apparatus to diagnose vehicle component failures | |
| WO2010067547A1 (en) | Vehicle failure diagnostic device | |
| US20230013544A1 (en) | Method, Apparatus and System for Detecting Abnormal Operating States of a Device | |
| CN109163913A (en) | A kind of Diagnosis method of automobile faults and relevant device | |
| US20190228322A1 (en) | Vehicle repair guidance system | |
| Murphey et al. | Automotive fault diagnosis-part II: a distributed agent diagnostic system | |
| JP2003516275A (en) | How to identify car errors | |
| CN110826772A (en) | Vehicle exhaust emission prediction method, device, equipment and storage medium | |
| CN108829088B (en) | Automobile diagnosis method and device and storage medium | |
| CN110515781B (en) | Complex system state monitoring and fault diagnosis method | |
| CN111506048B (en) | Vehicle fault early warning method and related equipment | |
| US5821412A (en) | Apparatus and method for processing engine measurements | |
| CN115268417B (en) | Self-adaptive ECU fault diagnosis control method | |
| CN114239734B (en) | Distributed vehicle-mounted health management system | |
| CN112267949B (en) | Fault-tolerant control method and control system for atmospheric pressure sensor of diesel engine | |
| CN110597235A (en) | Universal intelligent fault diagnosis method | |
| CN115061451A (en) | Automobile fault diagnosis method and device, intelligent terminal and storage medium | |
| CN113703429A (en) | Method and device for performing diagnosis of vehicle | |
| CN110738332A (en) | Accident vehicle identification method and system, and storage medium | |
| Jain et al. | Systematic literature review on predictive maintenance of vehicles and diagnosis of vehicle's health using machine learning techniques | |
| CN111207932B (en) | Motor vehicle exhaust monitoring method and related equipment | |
| CN115586009A (en) | Fault diagnosis and health management system and method | |
| CN114355234A (en) | Intelligent quality detection method and system for power module |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |