CN111325257A - Method and device for analyzing vehicle faults - Google Patents
Method and device for analyzing vehicle faults Download PDFInfo
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Abstract
The invention relates to a method and a device for analyzing a vehicle fault, wherein the method comprises the following steps: receiving fault information to be diagnosed; obtaining a predetermined characteristic related to the fault to be diagnosed indicated in the fault information to be diagnosed from a database; processing the fault information to be diagnosed and the predetermined characteristics by using a fault analysis model, wherein the fault analysis model is a preferred model determined by training a model for classification by using historical fault data and historical fault related characteristics; and generating a fault diagnosis result for the fault to be diagnosed.
Description
Technical Field
The invention relates to a method and a device for analyzing a vehicle fault.
Background
At present, automobiles become indispensable vehicles for human trip. The performance of the existing automobile is more and more perfect, the structure is more and more complex, and therefore the difficulty of diagnosing the faults of the automobile is continuously increased. The current automobile fault diagnosis method mainly comprises a manual diagnosis method and an instrument diagnosis method. Because the current automobile faults are various in types and reasons, the diagnosis of the automobile faults is time-consuming and inaccurate whether a manual diagnosis method or an instrument diagnosis method is adopted.
Therefore, in order to ensure driving safety, when a vehicle fails, it is desirable to quickly and accurately determine the cause of the failure and/or the probability of the failure caused by each cause, so as to facilitate maintenance personnel to quickly and accurately eliminate the failure.
Disclosure of Invention
This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The invention aims to provide a method and a device for analyzing automobile faults, which are used for at least partially overcoming the defects in the prior art, so that fault causes can be quickly located, and accurate diagnosis results can be obtained. In addition, by using the method for analyzing the automobile fault, accurate diagnosis results aiming at the fault are fed back to a manufacturer, and reference can be provided for evaluating the feasible range of the production parameters.
The embodiment of the invention provides a method and a device for analyzing automobile faults, wherein the method comprises the following steps: receiving fault information to be diagnosed; obtaining a predetermined characteristic related to the fault to be diagnosed indicated in the fault information to be diagnosed from a database; processing the fault information to be diagnosed and the predetermined characteristics by using a fault analysis model, wherein the fault analysis model is a preferred model determined by training a model for classification by using historical fault data and historical fault-related characteristics; and generating a fault diagnosis result for the fault to be diagnosed.
An embodiment of the present invention also provides an apparatus for analyzing a fault of an automobile, including: the receiving module is used for receiving fault information to be diagnosed; an obtaining module, configured to obtain, from a database, a predetermined feature related to a fault to be diagnosed indicated in the fault information to be diagnosed; the processing module is used for processing the fault information to be diagnosed and the predetermined characteristics by utilizing a fault analysis model, wherein the fault analysis model is a preferred model determined by training a model for classification by using historical fault data and historical fault related characteristics; and the generating module is used for generating a fault diagnosis result aiming at the fault to be diagnosed.
An apparatus for analyzing a malfunction of a vehicle according to an embodiment of the present invention includes: a processor; and a memory for storing executable instructions, wherein the executable instructions, when executed, cause the processor to perform the aforementioned method.
A machine-readable medium according to an embodiment of the invention has stored thereon executable instructions, which when executed, cause a machine to perform the aforementioned method.
It should be noted that one or more of the above aspects include the features described in detail below and particularly pointed out in the claims. The following description and the annexed drawings set forth in detail certain illustrative features of the one or more aspects. These features are indicative of but a few of the various ways in which the principles of various aspects may be employed and the present disclosure is intended to include all such aspects and their equivalents.
Drawings
The disclosed aspects will hereinafter be described in conjunction with the appended drawings, which are provided to illustrate, but not to limit, the disclosed aspects.
FIG. 1 shows an architectural diagram of a system for analyzing vehicle faults, in accordance with one embodiment of the present invention;
FIG. 2 shows a schematic diagram of an exemplary training process for a fault analysis model, in accordance with one embodiment of the present invention;
FIG. 3 shows a schematic flow diagram for training an XGboost model for classification and analyzing vehicle faults using the trained preferred model, according to one embodiment of the invention;
FIG. 4 shows a flow diagram of a method for analyzing vehicle faults in accordance with one embodiment of the present invention;
FIG. 5 shows a schematic diagram of an apparatus for analyzing vehicle faults according to one embodiment of the invention; and
fig. 6 shows a schematic view of an apparatus for analyzing a malfunction of a vehicle according to an embodiment of the present invention.
Detailed Description
The present disclosure will now be discussed with reference to various exemplary embodiments. It is to be understood that the discussion of these embodiments is merely intended to enable those skilled in the art to better understand and thereby practice the embodiments of the present disclosure, and does not teach any limitation as to the scope of the present disclosure.
Various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
FIG. 1 shows an architectural diagram of a system 100 for analyzing vehicle faults, in accordance with one embodiment of the present invention. As shown in FIG. 1, an exemplary system 100 for analyzing automobile faults may include a fault analysis model 102, an input port 104, a database 106, and an output port 108.
In some embodiments, the fault analysis model 102 may receive fault information to be diagnosed from the input ports 104. In some examples, the information about the fault to be diagnosed indicates information related to the fault when the vehicle has a fault, which includes, but is not limited to, the time, the place, and the specific fault performance of the fault, such as the vehicle cannot start, the engine is turned off, the vehicle is noisy, and so on. The fault information to be diagnosed may be provided in a text form. In some examples, the fault information to be diagnosed may be obtained by one or more of: detected by the vehicle's system, detected by sensors external to the vehicle and communicated to the vehicle's system or cloud database, manually recorded or entered, or any other suitable means of information acquisition. The fault analysis model 102 may also obtain from the database 106 predetermined characteristics associated with the fault to be diagnosed indicated in the fault information to be diagnosed, e.g., indicated by a specific fault manifestation in the fault information to be diagnosed, including but not limited to production process parameters and post-load part parameters such as: year or date of manufacture, mileage, distance traveled after loading the transmission, pump temperature, length of time the clutch is currently hot, oil pressure, transmission chip data read from the transmission, etc. In some examples, the production process parameters represent parameters at the station, such as part torque, lubrication fluid osmotic pressure, part force, torque, temperature, and the like; the after-loading part parameters may include, but are not limited to, the following: rotational speed, mileage, clutch temperature, pressure, losses, etc.
The received fault information to be diagnosed and the obtained predetermined characteristics may be provided as input to the fault analysis model 102 for subsequent processing or analysis. The fault analysis model 102 is utilized to process the information of the fault to be diagnosed and the predetermined characteristics to generate a diagnosis result for the fault to be diagnosed. The fault diagnosis results may be provided to the output port 108 for output to a user or presented on a display. In some examples, the fault diagnosis results may include, but are not limited to, any one or more of the following: a result characteristic associated with the category of the fault to be diagnosed, at least one cause causing the fault to be diagnosed and a probability of each cause causing the fault. In some examples, each diagnostic fault corresponds to at least one cause, for example, causes of an upshift squeak fault include, but are not limited to, the following: the function of the sensor is limited due to electrical faults and pressure drop of the internal temperature sensor; reasons for a shift not skip fault include, but are not limited to, the following: exceeding gear oil temperature, failed gear-up synchronization, etc. For example, if the classification result of the fault to be diagnosed is "oil leakage", the fault diagnosis result may be: the result characteristics [ production year, mileage ], failure reasons and probability [ sealing element failure 50%, fastening nut falling 10%, valve blockage 15%, oil tank damage 5%, oil pipe damage 10%, structural design problem 5%, and improper installation 5% ]. It will be appreciated that the specific characteristics, causes of failure, and probability values provided herein are exemplary and that in actual practice, any suitable resulting characteristics, causes of failure, and probabilities of each may be present or generated.
The fault diagnosis results may be provided from output port 108 to database 106 for storage in database 106 or provided to the model during a training phase for retraining the model.
It should be understood that the components or modules shown in fig. 1 are merely exemplary, and in actual practice, one or more components may be added or subtracted as desired. In addition, the various components included in the system 100 may be connected in any manner, whether wirelessly or by wire.
Fig. 2 shows a schematic diagram of an exemplary training process 200 for the fault analysis model 102, in which a preferred model is determined by training a model 202 for classification, such as an XGBoost model, and used as a fault analysis model in the fault analysis process, according to one embodiment of the invention.
As shown in FIG. 2, the XGboost model 202 obtains historical fault information and historical fault-related characteristics from a database 204. The XGBoost model 202 processes the historical fault information and historical fault-related characteristics to generate a predicted diagnostic result for the historical faults indicated in the historical fault information. In addition, the XGBoost model 202 obtains the true result of the detection for the fault from the detection or diagnostic system 206 and compares it to the predicted diagnostic result to determine the deviation between the two. If the deviation of the predicted result from the true result is smaller than a threshold value by adjusting the parameters and/or parameter values of the fault-related characteristic, the parameter at that time is determined as a preferred parameter and the model using the preferred parameter is determined as a preferred model, wherein the preferred model can be used as the fault analysis model 102 in the fault analysis process. Optionally, the preferred parameters may be output to the XGBoost model 202 via output port 208 for retraining the XGBoost model 202 with updated parameters in a subsequent training process.
In some examples, historical diagnostic truth that is analyzed or detected for historical faults by the detection or diagnostic system may be merged along with historical fault information into historical fault data for storage in the database 204.
A specific training process for a model 202 for classification, such as the XGBoost model, will be illustrated below with reference to the dashed box of fig. 3.
FIG. 3 shows a flow diagram 300 for training a model for classification, such as an XGboost model, and analyzing vehicle faults using the trained preferred model, in accordance with an embodiment of the invention. In this embodiment, operations 302, 304 within the dashed box are training processes for a fault analysis model, and operations 306, 308, 310, 312 are processes for processing the fault to be diagnosed using the trained preferred model to generate a diagnosis result.
As shown in FIG. 3, at block 302, historical fault information, historical fault-related characteristics, and corresponding diagnostic results for historical faults indicated in the historical fault information are obtained. The information may be obtained from a database.
At block 304, the XGBoost model may be trained using the obtained historical fault information, the features related to the fault, and the diagnostic results for the fault. For example, the XGBoost model is trained by taking fault information, high dimensional data related to a fault (such as characteristics of the year of production or date of production, mileage after loading of a transmission, temperature of a pump, endurance time at the current temperature of a clutch, oil pressure, and the like) and a diagnosis result as inputs using a cross-validation method to determine preferred parameters of the XGBoost model, and the XGBoost model using the preferred parameters is used as a preferred model or a fault analysis model.
At block 306, fault information to be diagnosed, which may be in text form, is received. In some examples, the fault information to be diagnosed is provided as input to the preferred model.
At block 308, a preferred model is loaded as a fault analysis model and features associated with the fault to be diagnosed indicated in the fault information to be diagnosed are obtained from block 310 as inputs to the preferred model. In some examples, characteristics associated with the fault to be diagnosed may include, but are not limited to, production process parameters, post-loading part parameters, and the like.
At block 314, based on the processing of the preferred model, a diagnostic result corresponding to the category of faults is generated. The fault diagnosis results may include, but are not limited to, any one or more of the following: a result characteristic associated with the category of the fault to be diagnosed, at least one cause causing the fault to be diagnosed and a probability of each cause causing the fault.
FIG. 4 shows an example process 400 for analyzing automobile faults, according to one embodiment of the invention.
At block 402, fault information to be diagnosed may be received. In some examples, the fault information to be diagnosed may be in text form.
At block 404, predetermined characteristics associated with the fault to be diagnosed indicated in the fault information to be diagnosed are obtained from a database. In some examples, the predetermined characteristics associated with the fault to be diagnosed may be represented by high dimensional data or parameters, such as parametric representations including characteristics of date of production, mileage, and the like.
At block 406, the diagnostic fault information and the predetermined features are processed using a fault analysis model, wherein the fault analysis model is a preferred model determined by training a model for classification using historical fault data and historical fault-related features, as described above with reference to the training process in fig. 2 or fig. 3. In some examples, the model used for classification is the XGBoost model. In some examples, the historical fault data includes historical fault information and corresponding diagnostic results.
At block 408, a fault diagnosis result for the fault to be diagnosed is generated, wherein the fault diagnosis result includes at least one of: a result characteristic associated with the category of the fault to be diagnosed, at least one cause causing the fault to be diagnosed and a probability of each cause.
In some embodiments, the fault analysis model is determined based further on: obtaining historical fault data and historical fault related characteristics, wherein the historical fault data comprises historical fault information and corresponding historical diagnosis results; training the model for classification using the historical fault information, the historical fault-related features, and the historical diagnostic results to determine preferred parameters for the model for classification; and using a preferred model selection using the preferred parameters as the fault analysis model.
Optionally, the method 400 may further include: and saving the fault diagnosis result in the database and/or providing the fault diagnosis result to the XGboost model to train the XGboost model again. In some embodiments, the operation of providing the fault diagnosis result to the XGBoost model to retrain the XGBoost model further comprises: providing the result features to the XGboost model to retrain the XGboost model as updated historical fault-related features.
Fig. 5 shows a schematic diagram of an apparatus 500 for analyzing a fault in a vehicle according to an embodiment of the invention. The apparatus 500 shown in fig. 5 can be implemented by software, hardware or a combination of software and hardware.
As shown in fig. 5, the apparatus 500 may include a receiving module 502, an obtaining module 504, a processing module 506, and a generating module 508.
The receiving module 502 may be used to receive fault information to be diagnosed. In some examples, the fault information to be diagnosed may be in text form.
The obtaining module 504 may be configured to obtain the predetermined characteristics related to the fault to be diagnosed indicated in the fault information to be diagnosed from the database.
The processing module 506 may be configured to process the information about the fault to be diagnosed and the predetermined features by using a fault analysis model, wherein the fault analysis model is a preferred model determined by training a model for classification using historical fault data and historical fault-related features. In some examples, the model used for classification is an XGBoost model, and the historical fault data includes historical fault information and corresponding diagnostic results.
The generating module 508 may be configured to generate a fault diagnosis result for the fault to be diagnosed. In some examples, the fault diagnosis result includes at least one of: a result characteristic associated with the category of the fault to be diagnosed, at least one cause causing the fault to be diagnosed and a probability of each cause.
Furthermore, optionally, the apparatus 500 may further include a saving module, configured to save the fault diagnosis result in the database. Optionally or alternatively, the apparatus 500 may further comprise a providing module for providing the fault diagnosis result to the XGBoost model to train the XGBoost model again.
Further, the providing module is further configured for: providing the result features to the XGboost model to retrain the XGboost model as updated historical fault-related features.
Fig. 6 shows a schematic view of an apparatus 600 for analyzing a malfunction of a vehicle according to an embodiment of the present invention.
As shown in fig. 6, the device 600 may include a processor 602 and a memory 604, wherein the memory 604 is configured to store executable instructions that, when executed, cause the processor 602 to perform the methods shown in fig. 3-4.
Embodiments of the present invention also provide a machine-readable medium having stored thereon executable instructions that, when executed, cause a machine to perform the method illustrated in fig. 3-4.
It should be understood that all operations in the methods described above are exemplary only, and the present disclosure is not limited to any operations in the methods or the order of the operations, but rather should encompass all other equivalent variations under the same or similar concepts.
It should also be understood that all of the modules in the above described apparatus may be implemented in various ways. These modules may be implemented as hardware, software, or a combination thereof. In addition, any of these modules may be further divided functionally into sub-modules or combined together.
The processor has been described in connection with various apparatus and methods. These processors may be implemented using electronic hardware, computer software, or any combination thereof. Whether such processors are implemented as hardware or software depends upon the particular application and the overall design constraints imposed on the system. By way of example, the processor, any portion of the processor, or any combination of processors presented in this disclosure may be implemented as a microprocessor, microcontroller, Digital Signal Processor (DSP), Field Programmable Gate Array (FPGA), Programmable Logic Device (PLD), state machine, gated logic, discrete hardware circuits, and other suitable processing components configured to perform the various functions described in this disclosure. The functionality of a processor, any portion of a processor, or any combination of processors presented in this disclosure may be implemented as software executed by a microprocessor, microcontroller, DSP, or other suitable platform.
The above description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein. All structural and functional equivalents to the elements of the various aspects described herein that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims.
It will be understood by those skilled in the art that various modifications and changes may be made in the embodiments disclosed above without departing from the spirit of the invention, and these modifications and changes are intended to fall within the scope of the invention as defined in the appended claims.
Claims (12)
1. A method for analyzing a vehicle fault, comprising:
receiving fault information to be diagnosed;
obtaining a predetermined characteristic related to the fault to be diagnosed indicated in the fault information to be diagnosed from a database;
processing the fault information to be diagnosed and the predetermined characteristics by using a fault analysis model, wherein the fault analysis model is a preferred model determined by training a model for classification by using historical fault data and historical fault-related characteristics; and
and generating a fault diagnosis result aiming at the fault to be diagnosed.
2. The method of claim 1, wherein the fault analysis model is determined based further on:
obtaining historical fault data and historical fault related characteristics, wherein the historical fault data comprises historical fault information and corresponding historical diagnosis results;
training the model for classification using the historical fault information, the historical fault-related features, and the historical diagnostic results to determine preferred parameters for the model for classification; and
selecting a preferred model using the preferred parameters for use as the fault analysis model.
3. The method of claim 1, wherein the model for classifying is an XGBoost model, and wherein the fault diagnosis result comprises at least one of: a result characteristic associated with the category of the fault to be diagnosed, at least one cause causing the fault to be diagnosed and a probability of each cause.
4. The method of claim 3, further comprising:
saving the fault diagnosis result in the database and/or providing the fault diagnosis result to the XGboost model to train the XGboost model again.
5. The method of claim 4, wherein providing the fault diagnosis results to the XGboost model to retrain the XGboost model further comprises: providing the result features to the XGboost model to retrain the XGboost model as updated historical fault-related features.
6. An apparatus for analyzing a fault in a vehicle, comprising:
the receiving module is used for receiving fault information to be diagnosed;
an obtaining module, configured to obtain, from a database, a predetermined feature related to a fault to be diagnosed indicated in the fault information to be diagnosed;
the processing module is used for processing the fault information to be diagnosed and the predetermined characteristics by utilizing a fault analysis model, wherein the fault analysis model is a preferred model determined by training a model for classification by using historical fault data and historical fault related characteristics; and
and the generating module is used for generating a fault diagnosis result aiming at the fault to be diagnosed.
7. The apparatus of claim 6, wherein the fault analysis model is determined based further on:
obtaining historical fault data and historical fault related characteristics, wherein the historical fault data comprises historical fault information and corresponding historical diagnosis results;
training the model for classification using the historical fault information, the historical fault-related features, and the historical diagnostic results to determine preferred parameters for the model for classification; and
selecting a preferred model using the preferred parameters for use as the fault analysis model.
8. The apparatus of claim 6, wherein the model for classifying is an XGboost model, and wherein the fault diagnosis result comprises at least one of: a result characteristic associated with the category of the fault to be diagnosed, at least one cause causing the fault to be diagnosed and a probability of each cause.
9. The apparatus of claim 8, further comprising:
the storage module is used for storing the fault diagnosis result in the database; and/or
And the providing module is used for providing the fault diagnosis result to the XGboost model so as to train the XGboost model again.
10. The apparatus of claim 9, wherein the means for providing is further for: providing the result features to the XGboost model to retrain the XGboost model as updated historical fault-related features.
11. An apparatus for analyzing a fault in a vehicle, comprising:
a processor; and
a memory to store executable instructions, wherein the executable instructions, when executed, cause the processor to perform the method of claims 1-5.
12. A machine-readable medium having stored thereon executable instructions, wherein the executable instructions, when executed, cause a machine to perform the method of claims 1-5.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010092191.4A CN111325257A (en) | 2020-02-14 | 2020-02-14 | Method and device for analyzing vehicle faults |
| EP21156584.1A EP3865963A1 (en) | 2020-02-14 | 2021-02-11 | Method and device for analyzing vehicle failure |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202010092191.4A CN111325257A (en) | 2020-02-14 | 2020-02-14 | Method and device for analyzing vehicle faults |
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