CN113688156A - Mechanical fault detection system and method based on big data - Google Patents
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Abstract
The invention is suitable for the field of computers, and particularly relates to a mechanical fault detection system and method based on big data, wherein the method comprises the following steps: acquiring parameter data to be detected; inquiring a fault source positioning database according to the parameter data to be detected, and determining fault source information; acquiring fault solution data according to fault source information; and performing relevance evaluation on the fault solution data according to the fault source information, and sequencing the fault solution data according to a relevance evaluation result to obtain a detection result. According to the invention, the parameters to be detected are analyzed firstly, so that whether the fault exists and the position of the fault are preliminarily determined, the solutions are simultaneously obtained from the online and the local according to the specific type of the fault, the corresponding solutions are generated by utilizing the large database formed by the solutions, and finally the detection result containing the solutions is fed back to related personnel, so that the overhaul time is shortened, and the loss caused by the fault is reduced.
Description
Technical Field
The invention belongs to the field of computers, and particularly relates to a mechanical fault detection system and method based on big data.
Background
Big data, or mass data, refers to the data that is too large to be captured, managed, processed and organized into information that can help enterprise business decision more actively within a reasonable time through the current mainstream software tools.
In the middle of current industrial equipment, along with its degree of automation's rising, the convenience of use has also progressively improved to, carry out fault detection voluntarily and also be used gradually in the middle of industrial equipment, utilize automatic fault detection, can carry out self-checking in system operation process, thereby send out the police dispatch newspaper when detecting the trouble, avoid causing further loss.
In the existing system, although self-checking can be performed, the self-checking degree is low, and an alarm can be only performed when a problem occurs in specific data, but the possibility that the problem occurs in the same specific data is high, so that a maintainer still needs to perform problem troubleshooting, the maintenance experience is not obtained, and much time is wasted in the process.
Disclosure of Invention
The embodiment of the invention aims to provide a mechanical fault detection method based on big data, and aims to solve the problems that an existing self-checking system can only give an alarm when specific data are in problem and cannot give a corresponding solution, so that the big data are analyzed to give the corresponding solution, and the maintenance efficiency is greatly improved.
The embodiment of the invention is realized in such a way that a mechanical fault detection method based on big data comprises the following steps:
acquiring parameter data to be detected, wherein the parameter data to be detected at least comprises main parameters to be detected;
inquiring a fault source positioning database according to the parameter data to be detected, and determining fault source information;
acquiring fault solution data according to fault source information, wherein the fault solution data at least comprises cloud data and local data;
and performing relevance evaluation on the fault solution data according to the fault source information, and sequencing the fault solution data according to a relevance evaluation result to obtain a detection result.
Preferably, the step of querying the fault source location database according to the parameter data to be detected and determining the fault source information includes:
analyzing the parameter data to be detected to obtain at least one parameter to be detected;
generating a parameter vector to be detected according to the parameter to be detected;
and inquiring a fault source positioning database according to the parameter vector to be detected to obtain fault source information.
Preferably, the step of obtaining the fault solution data according to the fault source information specifically includes:
acquiring cloud data from a cloud database according to fault source information;
and screening the local data from the local database according to the fault source information.
Preferably, the step of performing relevance evaluation on the fault solution data according to the fault source information, and sorting the fault solution data according to a relevance evaluation result to obtain a detection result specifically includes:
continuously numbering the failure solution data;
comparing the fault source information with the fault solution data according to the serial number sequence to obtain a comparison result;
and performing correlation evaluation according to the comparison result to obtain a detection result.
Preferably, the parameter data to be detected further includes a secondary parameter to be detected, and after the processing of the primary parameter to be detected is completed, the secondary parameter to be detected is treated as the primary parameter to be detected.
Preferably, after the step of obtaining the detection result, a scheme adopted for solving the problem is uploaded as local data.
Preferably, after the step of sorting the failure solution data according to the result of the correlation evaluation, the failure solution data having a correlation smaller than a preset value is discarded.
It is another object of the present invention to provide a big data based mechanical failure detection system, the system comprising:
the data acquisition module is used for acquiring parameter data to be detected, and the parameter data to be detected at least comprises main parameters to be detected;
the fault source determining module is used for inquiring the fault source positioning database according to the parameter data to be detected and determining fault source information;
the system comprises a solution acquisition module, a fault analysis module and a fault analysis module, wherein the solution acquisition module is used for acquiring fault solution data according to fault source information, and the fault solution data at least comprises cloud data and local data;
and the detection result generation module is used for carrying out correlation evaluation on the fault solution data according to the fault source information and sequencing the fault solution data according to the correlation evaluation result to obtain a detection result.
Preferably, the fault source determination module includes:
the parameter analyzing unit is used for analyzing the parameter data to be detected to obtain at least one parameter to be detected;
the vector generating unit is used for generating a parameter vector to be detected according to the parameter to be detected;
and the positioning unit is used for inquiring the fault source positioning database according to the parameter vector to be detected to obtain the fault source information.
Preferably, the solution acquiring module includes:
the cloud data acquisition unit is used for acquiring cloud data from a cloud database according to the fault source information;
and the local data screening unit is used for screening the local data from the local database according to the fault source information.
According to the mechanical fault detection method based on the big data, provided by the embodiment of the invention, the parameters needing to be detected are analyzed, so that whether the fault exists or not and the position where the fault occurs are preliminarily determined, the solutions are simultaneously obtained from the online and the local according to the specific type of the fault, the corresponding solutions are generated by utilizing the big database formed by the solutions, the detection result containing the solutions is finally fed back to related personnel, the overhaul time is shortened, and the loss caused by the fault is reduced.
Drawings
FIG. 1 is a flow chart of a big data based mechanical failure detection method according to an embodiment of the present invention;
fig. 2 is a flowchart of steps for querying a fault source location database according to parameter data to be detected and determining fault source information according to the embodiment of the present invention;
FIG. 3 is a flowchart of steps provided by an embodiment of the present invention for obtaining fault solution data based on fault source information;
FIG. 4 is a flowchart illustrating steps of performing relevance evaluation on fault solution data according to fault source information and sorting the fault solution data to obtain a detection result according to the embodiment of the present invention;
FIG. 5 is an architecture diagram of a big data based mechanical failure detection system according to an embodiment of the present invention;
FIG. 6 is an architecture diagram of a fault source determination module provided by an embodiment of the present invention;
fig. 7 is an architecture diagram of a solution acquisition module provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
In the middle of current industrial equipment, along with its degree of automation's rising, the convenience of use has also progressively improved to, carry out fault detection voluntarily and also be used gradually in the middle of industrial equipment, utilize automatic fault detection, can carry out self-checking in system operation process, thereby send out the police dispatch newspaper when detecting the trouble, avoid causing further loss. In the existing system, although self-checking can be performed, the self-checking degree is low, and an alarm can be only performed when a problem occurs in specific data, but the possibility that the problem occurs in the same specific data is high, so that a maintainer still needs to perform problem troubleshooting, the maintenance experience is not obtained, and much time is wasted in the process.
In the invention, parameters needing to be detected are analyzed firstly, so that whether a fault exists and the position where the fault occurs are preliminarily determined, solutions are simultaneously obtained from the online and the local according to the specific type of the fault, a corresponding solution is generated by utilizing a large database formed by the solutions, and finally the detection result containing the solution is fed back to related personnel, so that the overhaul time is shortened, and the loss caused by the fault is reduced.
As shown in fig. 1, a flowchart of a big data based mechanical failure detection method provided in an embodiment of the present invention is shown, where the method includes:
s100, parameter data to be detected are obtained, and the parameter data to be detected at least comprise main parameters to be detected.
In the step, parameter data to be detected is obtained, for a detection system, electrical signals or other electrical data are finally processed, so that data acquisition needs to be performed on the equipment through an external means, for example, image acquisition is performed on key parts of the equipment by using a camera device, an infrared camera is used for shooting wires, the temperatures of fault points and non-fault points are different, so that discrimination can be performed by using the information, in addition, additional devices such as a temperature sensor, a humidity sensor and a light sensor can be used for performing data acquisition on the equipment to be detected, finally, the acquired information is summarized to be the parameter data to be detected, the parameter data to be detected at least comprises main parameter to be detected, the main parameter to be detected refers to parameters directly influencing the equipment, such as the starting and stopping of a motor, the heating temperature and the like, once the parameters have problems, the product prepared by the equipment has defects and finally causes great loss, certainly, the parameter data to be detected also comprises a secondary parameter to be detected, after the primary parameter to be detected is processed, the secondary parameter to be detected is treated as the primary parameter to be detected, the secondary parameter to be detected is possibly influenced, but the existing non-processing equipment can still normally run, so that after the processing sequence is continued, the primary parameter to be detected is processed again after the processing is completed.
S200, inquiring a fault source positioning database according to the parameter data to be detected, and determining fault source information.
In this step, the fault source location database is queried according to the parameter data to be detected, most of the devices are produced in batches, and particularly for the devices used for a long time, a corresponding maintenance record can be generated each time the devices are overhauled, the types and positions of faults are recorded in the maintenance record, so that the parameters to be detected can be known to be abnormal according to the parameter data to be detected, for example, in a certain device, multipoint temperature control is needed, the temperatures of two points exceed a preset value, the temperature values of the two points are the parameters to be detected, and the fault source location database is queried according to the numerical values of the parameters to be detected and the approximate positions of the parameters to be detected, so that the types and reasons of the faults at this time are determined.
S300, acquiring fault solution data according to the fault source information, wherein the fault solution data at least comprises cloud data and local data.
In this step, the failure solution data is obtained according to the failure source information, and for the equipment, the failure solution data may be distributed to different places, so that the same problem may occur in respective use areas, particularly for the same batch of products, if the failure occurs, the failure types of the products are likely to be the same, and therefore after each manual overhaul, the actual effective solution adopted for the failure condition is uploaded to the system, so that the solutions can be simultaneously obtained from the online and the local when the solutions need to be inquired, and for the local data, the user font can be uploaded to the local maintenance case so as to be convenient for reference for the subsequent overhaul.
S400, performing relevance evaluation on the fault solution data according to the fault source information, and sequencing the fault solution data according to the relevance evaluation result to obtain a detection result.
In this step, the relevance evaluation is performed on the fault solution data according to the fault source information, when a problem occurs in the same parameter, the problem may be caused by a plurality of reasons, at this time, the evaluation is performed to judge which fault solution is closer and has a reference value, for example, for an abnormal rise in temperature at a certain position, there may be three reasons, so at least three fault solutions are to be selected, at this time, the analysis is performed according to the matching condition of the fault solutions and the fault source information, the fault solution data is sorted to obtain a detection result, and then the detection result includes the fault solution with the most reference value.
As shown in fig. 2, as a preferred embodiment of the present invention, the step of querying a fault source location database according to parameter data to be detected to determine fault source information specifically includes:
s201, analyzing the parameter data to be detected to obtain at least one parameter to be detected.
In this step, the data to be detected is first analyzed, because the data of the parameters to be detected collected at the same time may include a plurality of parameters to be detected, and therefore the data needs to be divided separately to obtain a plurality of groups of parameters to be detected.
And S202, generating a parameter vector to be detected according to the parameter to be detected.
In this step, a parameter vector to be detected is generated according to the parameter to be detected, and each parameter to be detected is used as an element to generate a parameter vector to be detected in order to facilitate retrieval, where the parameter vector to be detected includes all the elements.
S203, inquiring a fault source positioning database according to the parameter vector to be detected to obtain fault source information.
In this step, the fault source location database is queried according to the parameter vector to be detected, each element in the parameter vector to be detected is used as a search item, and thus the search item is used to search a corresponding matching item in the fault source location database, so as to obtain fault source information.
As shown in fig. 3, as a preferred embodiment of the present invention, the step of acquiring failure solution data according to failure source information specifically includes:
s301, cloud data are obtained from a cloud database according to the fault source information.
In this step, cloud data is obtained from the cloud database according to the fault source information, for each user, the user can overhaul the device in the process of using the device, after the overhaul is completed each time, the fault condition encountered this time is recorded in detail, the final solution is recorded, and then the information is uploaded to the cloud.
S302, local data are screened from a local database according to the fault source information.
In this step, local data is screened from a local database according to fault source information, the local database is a database which is arranged locally, is not uploaded to a network and can be read only on a storage device, and is mainly used for equipment with a security requirement, so that technology cannot be leaked, and therefore, after maintenance personnel maintain the local database, the data is stored in the local database, and when the local database is actually used, local data related to the detection is called from the local database.
As shown in fig. 4, as a preferred embodiment of the present invention, the step of performing relevance evaluation on the fault solution data according to the fault source information, and sorting the fault solution data according to a result of the relevance evaluation to obtain a detection result specifically includes:
s401, failure solution data are numbered continuously.
In this step, the failure solution data is numbered consecutively, and the failure solution data includes a plurality of failure solutions, so for analyzing the failure solutions, the failure solutions are numbered consecutively to determine the order of analysis.
S402, comparing the fault source information with the fault solution data according to the numbering sequence to obtain a comparison result.
In this step, the failure source information is read one by one in the order of the numbers, the failure source information is compared with the failure solution data, and in the process, the contents contained in the two are matched to determine the same contents between the two, for example, three items of data are contained in the failure source information, the failure solution in the failure solution data contains ten items of data, two items of the ten items of data are overlapped with the data in the failure source information, and then the overlapped two sets of data are recorded in the comparison result.
And S403, performing correlation evaluation according to the comparison result to obtain a detection result.
In this step, correlation evaluation is performed according to the number of coincident data in the comparison result, for example, when the fault source information includes ten items of data, and the final coincident data is 5 groups, the correlation is 0.5, and the fault solution with the highest correlation is recorded in the detection result. And after the step of sorting the fault solution data according to the correlation evaluation result, discarding the fault solution data with the correlation smaller than a preset value. And after the detection is finished and when the scheme is determined to be effective, uploading the scheme adopted by the problem as local data.
As shown in fig. 5, the present invention provides a big data based mechanical failure detection system, which is characterized in that the system includes:
the data acquiring module 100 is configured to acquire parameter data to be detected, where the parameter data to be detected at least includes main parameters to be detected.
In the system, the data acquisition module 100 acquires the parameter data to be detected, and finally processes the electric signals or other electric data, therefore, the data acquisition is carried out on the equipment by external means, the data acquisition is carried out on the equipment to be detected, and finally the acquired information is summarized, namely parameter data to be detected, the parameter data to be detected at least comprises a main parameter to be detected, the main parameter to be detected refers to a parameter directly influencing equipment, the parameter data to be detected also comprises a secondary parameter to be detected, after the processing of the primary parameters to be detected is completed, the secondary parameters to be detected are treated as the primary parameters to be detected, the secondary parameters to be detected have possible influence, however, the processing equipment can still normally operate at present, so that the main parameters to be detected are processed after the processing is finished after the processing sequence.
And the fault source determining module 200 is configured to query a fault source location database according to the parameter data to be detected, and determine fault source information.
In the system, the fault source determining module 200 queries the fault source location database according to the parameter data to be detected, for each device, most of the devices are produced in batch, and particularly for the long-term used devices, a corresponding maintenance record can be generated each time the device is overhauled, the type and the position of a fault are recorded in the maintenance record, and then, according to the parameter data to be detected, which parameters are abnormal can be known.
A solution obtaining module 300, configured to obtain fault solution data according to the fault source information, where the fault solution data includes at least cloud data and local data.
In the present system, the solution obtaining module 300 obtains the failure solution data according to the failure source information, and for the equipment, the failure solution data may be distributed to different places, so that the same problem may occur in each use area, especially for the same batch of products, if the failure occurs, the failure type is likely to be the same, and therefore after each manual overhaul, the actual effective solution adopted for the failure condition is uploaded to the system, so that the solution can be obtained from the online and the local at the same time when the solution needs to be queried, and for the local data, the user font can be uploaded to the local maintenance case so as to make reference for the subsequent overhaul.
The detection result generating module 400 is configured to perform relevance evaluation on the fault solution data according to the fault source information, and sort the fault solution data according to a relevance evaluation result to obtain a detection result.
In the system, the detection result generation module 400 performs correlation evaluation on the fault solution data according to the fault source information, when a problem occurs in the same parameter, the problem may be caused by a plurality of reasons, at this time, evaluation is required to determine which fault solution is closer and has a higher reference value, analysis is performed according to the matching condition of the fault solution and the fault source information, the fault solution data is sorted to obtain a detection result, and then the detection result includes the fault solution with the highest reference value.
As shown in fig. 6, as a preferred embodiment of the present invention, the fault source determining module includes:
the parameter analyzing unit 201 is configured to analyze the parameter data to be detected to obtain at least one parameter to be detected.
In this module, the parameter analyzing unit 201 analyzes data to be detected, because the same collected data of parameters to be detected may include a plurality of parameters to be detected, and therefore the data needs to be divided separately to obtain a plurality of sets of parameters to be detected.
The vector generating unit 202 is configured to generate a parameter vector to be detected according to the parameter to be detected.
In this module, the vector generation unit 202 generates a parameter vector to be detected according to the parameter to be detected, and for convenience of retrieval, each parameter to be detected is used as an element, so as to generate a parameter vector to be detected, where the parameter vector to be detected includes all the elements.
And the positioning unit 203 is configured to query a fault source positioning database according to the parameter vector to be detected, so as to obtain fault source information.
In this module, the positioning unit 203 queries the fault source positioning database according to the parameter vector to be detected, and uses each element in the parameter vector to be detected as a search item, so as to search a corresponding matching item in the fault source positioning database by using the search item, so as to obtain fault source information.
As shown in fig. 7, as a preferred embodiment of the present invention, the solution obtaining module includes:
a cloud data obtaining unit 301, configured to obtain cloud data from a cloud database according to the fault source information.
In this module, the cloud data obtaining unit 301 obtains cloud data from the cloud database according to the fault source information, and for each user, the user overhauls the device in the process of using the device, and after completing the overhaul each time, records the fault condition encountered this time in detail, records the final solution, and uploads the above information to the cloud.
A local data screening unit 302, configured to screen local data from a local database according to the failure source information.
In this module, the local data filtering unit 302 filters local data from a local database according to failure source information, the local database is a local database, which is not uploaded to a network and can be read directly only on a storage device, and is mainly used for devices with security requirements, which can ensure that the technology does not leak, so that after maintenance by maintenance personnel, the data is stored in the local database, and when in actual use, the local data related to the detection is called from the local database.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A big data based mechanical fault detection method, characterized in that the method comprises:
acquiring parameter data to be detected, wherein the parameter data to be detected at least comprises main parameters to be detected;
inquiring a fault source positioning database according to the parameter data to be detected, and determining fault source information;
acquiring fault solution data according to fault source information, wherein the fault solution data at least comprises cloud data and local data;
and performing relevance evaluation on the fault solution data according to the fault source information, and sequencing the fault solution data according to a relevance evaluation result to obtain a detection result.
2. The big data-based mechanical fault detection method according to claim 1, wherein the step of querying a fault source location database according to parameter data to be detected to determine fault source information specifically comprises:
analyzing the parameter data to be detected to obtain at least one parameter to be detected;
generating a parameter vector to be detected according to the parameter to be detected;
and inquiring a fault source positioning database according to the parameter vector to be detected to obtain fault source information.
3. The big-data-based mechanical failure detection method according to claim 1, wherein the step of obtaining failure solution data according to failure source information specifically comprises:
acquiring cloud data from a cloud database according to fault source information;
and screening the local data from the local database according to the fault source information.
4. The big-data-based mechanical fault detection method according to claim 1, wherein the step of performing correlation evaluation on the fault solution data according to the fault source information, and sorting the fault solution data according to the correlation evaluation result to obtain the detection result specifically comprises:
continuously numbering the failure solution data;
comparing the fault source information with the fault solution data according to the serial number sequence to obtain a comparison result;
and performing correlation evaluation according to the comparison result to obtain a detection result.
5. The big-data-based mechanical failure detection method according to claim 1, wherein the parameter data to be detected further comprises a secondary parameter to be detected, and after the processing of the primary parameter to be detected is completed, the secondary parameter to be detected is treated as the primary parameter to be detected.
6. The big-data based mechanical failure detection method according to claim 1, wherein after the step of obtaining the detection result, a solution adopted for solving the problem is uploaded as local data.
7. The big-data based mechanical failure detection method according to claim 1, wherein the step of sorting the failure solution data according to the correlation evaluation result is followed by discarding the failure solution data having a correlation smaller than a preset value.
8. A big data based mechanical fault detection system, the system comprising:
the data acquisition module is used for acquiring parameter data to be detected, and the parameter data to be detected at least comprises main parameters to be detected;
the fault source determining module is used for inquiring the fault source positioning database according to the parameter data to be detected and determining fault source information;
the system comprises a solution acquisition module, a fault analysis module and a fault analysis module, wherein the solution acquisition module is used for acquiring fault solution data according to fault source information, and the fault solution data at least comprises cloud data and local data;
and the detection result generation module is used for carrying out correlation evaluation on the fault solution data according to the fault source information and sequencing the fault solution data according to the correlation evaluation result to obtain a detection result.
9. The big-data based mechanical fault detection system of claim 8, wherein the fault source determination module comprises:
the parameter analyzing unit is used for analyzing the parameter data to be detected to obtain at least one parameter to be detected;
the vector generating unit is used for generating a parameter vector to be detected according to the parameter to be detected;
and the positioning unit is used for inquiring the fault source positioning database according to the parameter vector to be detected to obtain the fault source information.
10. The big-data based mechanical fault detection system of claim 8, wherein the solution acquisition module comprises:
the cloud data acquisition unit is used for acquiring cloud data from a cloud database according to the fault source information;
and the local data screening unit is used for screening the local data from the local database according to the fault source information.
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| CN114264336A (en) * | 2021-12-22 | 2022-04-01 | 深圳市玄羽科技有限公司 | Mechanical equipment fault monitoring method based on big data |
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| CN110377009A (en) * | 2019-07-30 | 2019-10-25 | 南京爱福路汽车科技有限公司 | A kind of method and system for automobile failure diagnosis |
| CN110675079A (en) * | 2019-09-30 | 2020-01-10 | 腾讯科技（深圳）有限公司 | Fault data processing method and device and computer equipment |
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| CN110377009A (en) * | 2019-07-30 | 2019-10-25 | 南京爱福路汽车科技有限公司 | A kind of method and system for automobile failure diagnosis |
| CN110704224A (en) * | 2019-09-18 | 2020-01-17 | 上海麦克风文化传媒有限公司 | Online fault processing method and system |
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