CN115809281B - Expert database self-learning method and device based on data acquisition and equipment diagnosis - Google Patents
Expert database self-learning method and device based on data acquisition and equipment diagnosis Download PDFInfo
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- CN115809281B CN115809281B CN202211501301.3A CN202211501301A CN115809281B CN 115809281 B CN115809281 B CN 115809281B CN 202211501301 A CN202211501301 A CN 202211501301A CN 115809281 B CN115809281 B CN 115809281B
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Abstract
The application relates to an expert database self-learning method and device based on data acquisition and equipment diagnosis, comprising the steps of acquiring basic data information of each part in an industrial robot and environmental data information for deploying the industrial robot; monitoring the basic data information and the environment data information, and obtaining the equipment diagnosis result of the industrial robot according to the monitoring result; extracting basic data information of each part in the industrial robot and setting first dimension information in environment data information of the industrial robot, and performing fuzzy search on the first dimension information in an expert database to obtain second dimension information; and determining a correction coefficient according to the first dimension information and the second dimension information, correcting the second dimension information according to the correction coefficient and the equipment diagnosis result, and outputting the correction result to an expert database. According to the application, the data in the equipment fault prediction database is corrected through the acquired operation information and environment information of the industrial robot, so that the fault prediction capability of the industrial robot is improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an expert database self-learning method and device based on data acquisition and equipment diagnosis.
Background
The industrial robot is a multi-joint manipulator or a multi-degree-of-freedom machine device facing the industrial field, can automatically execute work, and is a machine which realizes various functions by self power and control capability. The robot can be commanded by human beings, can operate according to a preset program, and can also act according to the principle formulated by artificial intelligence technology.
Industrial robots can replace people in industrial production to do some monotonous, frequent and repeated long-time operations or operations under dangerous and severe environments, such as processes of stamping, pressure casting, heat treatment, welding, coating, plastic product forming, machining, simple assembly and the like, and in departments of atomic energy industry and the like, to carry or process operation of harmful materials to human bodies.
How to find or predict the faults of the industrial robot in time is a technical problem to be solved at present.
Disclosure of Invention
The application aims to solve the technical problem of providing an expert database self-learning method and device based on data acquisition and equipment diagnosis aiming at the defects of the prior art.
The technical scheme for solving the technical problems is as follows:
in a first aspect, the present application provides a self-learning method for an expert database based on data acquisition and equipment diagnosis, the method comprising:
acquiring basic data information of each part in the industrial robot and environmental data information for deploying the industrial robot according to a first preset task table;
monitoring the basic data information and the environment data information according to a second preset task table, and obtaining a device diagnosis result of the industrial robot according to a monitoring result;
extracting basic data information of each component in the industrial robot and setting first dimension information in environment data information of the industrial robot, and performing fuzzy search on the first dimension information in the expert database to obtain second dimension information;
and determining a correction coefficient according to the first dimension information and the second dimension information, correcting the second dimension information according to the correction coefficient and the equipment diagnosis result, and outputting the correction result to the expert database.
Further, the collecting basic data information of each component in the industrial robot and disposing environmental data information of the industrial robot according to the first preset task table specifically includes:
based on first task information in the first preset task table, basic data information of each part in the industrial robot is collected at fixed time, wherein the basic data information comprises special data and alarm data of each part in the industrial robot;
based on the second task information in the first preset task table, according to the application scene classification of the robot, environmental data information of the industrial robot is collected and deployed at fixed time, wherein the environmental data information comprises workshop temperature, humidity, dust and noise of the industrial robot.
Further, the monitoring the basic data information and the environmental data information according to the second preset task table, and obtaining the equipment diagnosis result of the industrial robot according to the monitoring result, specifically includes:
calculating special data in the acquired basic data information at regular time based on third task information in the second preset task table to obtain trend change data of the special data, and comparing the trend change data with a corresponding index threshold to obtain a first comparison result;
based on fourth task information in the second preset task table, comparing each item of data in the acquired environmental data information with a corresponding environmental index threshold value at regular time to obtain a second comparison result;
based on fifth task information in the second preset task table, calculating alarm data in the acquired basic data information at regular time to obtain alarm scores of the alarm data;
and if the first comparison result, the second comparison result and the alarm score are obtained, obtaining the equipment diagnosis result of the industrial robot.
Further, the extracting basic data information of each component in the industrial robot and setting first dimension information in environment data information of the industrial robot specifically includes:
and extracting data in special data which is not in the corresponding index threshold value, the alarm data with the alarm score being larger than a preset score value and environment data information with the alarm score being larger than the corresponding environment index threshold value as the first dimension information.
Further, the step of performing the fuzzy search on the first dimension information in the expert database to obtain second dimension information specifically includes:
establishing a search item according to the special data, the environment data information and the alarm data in the first dimension information, wherein the search item comprises establishing a characteristic value, a characteristic index and a characteristic type according to the special data, the environment data information and the alarm data;
wherein each item of special data corresponds to one search item, each item of environment data information corresponds to one search item, and each item of alarm data corresponds to one search item;
searching in the expert database according to all the searched items, and determining that the searched result is effective if the searched items in the searched result exceed a preset searching threshold value;
and when the searching result is valid, the searching result is the second dimension information, and the diagnosis information in the searching result is the diagnosis information in the second dimension information.
Further, the determining a correction coefficient according to the first dimension information and the second dimension information, correcting the second dimension information according to the correction coefficient and the equipment diagnosis result, and outputting the correction result to the expert database specifically includes:
calculating a variance value of each data in the first dimension information and each corresponding data in the second dimension information, and if the variance value is larger than a preset variance value, calculating a sub-correction coefficient of the data in the first dimension information according to the data in the first dimension information and the corresponding data in the second dimension information;
otherwise, the sub-correction coefficient of the data in the first dimension information is a first preset value;
when the sub-correction coefficient is not the first preset value, correcting the value range of the corresponding data in the second dimension information according to the sub-correction coefficient, and adding the equipment diagnosis result to the diagnosis information in the second dimension information.
In a second aspect, the present application provides an expert database self-learning apparatus based on data acquisition and equipment diagnosis, the apparatus comprising:
the first processing module is used for collecting basic data information of each part in the industrial robot and environmental data information for deploying the industrial robot according to a first preset task table;
the second processing module is used for monitoring the basic data information and the environment data information according to a second preset task table and obtaining the equipment diagnosis result of the industrial robot according to the monitoring result;
the third processing module is used for extracting basic data information of each component in the industrial robot and setting first dimension information in environment data information of the industrial robot, and performing fuzzy search on the first dimension information in the expert database to obtain second dimension information;
and the fourth processing module is used for determining a correction coefficient according to the first dimension information and the equipment diagnosis result, correcting the second dimension information according to the correction coefficient and the equipment diagnosis result, and outputting the correction result to the expert database.
The beneficial effects of the application are as follows: the expert database self-learning method based on data acquisition and equipment diagnosis comprises the steps of acquiring basic data information of each part in an industrial robot and environmental data information for deploying the industrial robot according to a first preset task table; monitoring the basic data information and the environment data information according to a second preset task table, and obtaining a device diagnosis result of the industrial robot according to a monitoring result; extracting basic data information of each component in the industrial robot and setting first dimension information in environment data information of the industrial robot, and performing fuzzy search on the first dimension information in the expert database to obtain second dimension information; and determining a correction coefficient according to the first dimension information and the second dimension information, correcting the second dimension information according to the correction coefficient and the equipment diagnosis result, and outputting the correction result to the expert database. According to the application, the data in the equipment fault prediction database is corrected through the acquired operation information and environment information of the industrial robot, so that the fault prediction capability of the industrial robot is improved.
Additional aspects of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly explain the embodiments of the present application or the drawings used in the description of the prior art, and it is obvious that the drawings described below are only some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an expert database self-learning method based on data acquisition and equipment diagnosis according to an embodiment of the application;
fig. 2 is a schematic block diagram of an expert database self-learning device based on data acquisition and equipment diagnosis according to another embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
As shown in fig. 1, the method for self-learning an expert database based on data acquisition and equipment diagnosis according to the embodiment of the application includes the following steps:
110. and acquiring basic data information of each part in the industrial robot and environmental data information for deploying the industrial robot according to a first preset task table.
120. And monitoring the basic data information and the environment data information according to a second preset task table, and obtaining the equipment diagnosis result of the industrial robot according to the monitoring result.
130. Extracting basic data information of each part in the industrial robot and setting first dimension information in environment data information of the industrial robot, and performing fuzzy search on the first dimension information in the expert database to obtain second dimension information.
140. And determining a correction coefficient according to the first dimension information and the second dimension information, correcting the second dimension information according to the correction coefficient and the equipment diagnosis result, and outputting the correction result to the expert database.
Based on the above embodiment, further, the collecting, according to the first preset task table, basic data information of each component in the industrial robot and environmental data information for deploying the industrial robot specifically includes:
based on first task information in the first preset task table, basic data information of each part in the industrial robot is collected at fixed time, wherein the basic data information comprises special data and alarm data of each part in the industrial robot;
based on the second task information in the first preset task table, according to the application scene classification of the robot, environmental data information of the industrial robot is collected and deployed at fixed time, wherein the environmental data information comprises workshop temperature, humidity, dust and noise of the industrial robot.
Based on the foregoing embodiment, further, the monitoring the basic data information and the environmental data information according to the second preset task table, and obtaining the device diagnosis result of the industrial robot according to the monitoring result, specifically includes:
calculating special data in the acquired basic data information at regular time based on third task information in the second preset task table to obtain trend change data of the special data, and comparing the trend change data with a corresponding index threshold to obtain a first comparison result;
based on fourth task information in the second preset task table, comparing each item of data in the acquired environmental data information with a corresponding environmental index threshold value at regular time to obtain a second comparison result;
based on fifth task information in the second preset task table, calculating alarm data in the acquired basic data information at regular time to obtain alarm scores of the alarm data;
and if the first comparison result, the second comparison result and the alarm score are obtained, obtaining the equipment diagnosis result of the industrial robot.
Based on the above embodiment, further, the extracting basic data information of each component in the industrial robot and setting first dimension information in environment data information of the industrial robot specifically includes:
and extracting data in special data which is not in the corresponding index threshold value, the alarm data with the alarm score being larger than a preset score value and environment data information with the alarm score being larger than the corresponding environment index threshold value as the first dimension information.
Based on the above embodiment, further, the performing the fuzzy search on the first dimension information in the expert database to obtain second dimension information specifically includes:
establishing a search item according to the special data, the environment data information and the alarm data in the first dimension information, wherein the search item comprises establishing a characteristic value, a characteristic index and a characteristic type according to the special data, the environment data information and the alarm data;
wherein each item of special data corresponds to one search item, each item of environment data information corresponds to one search item, and each item of alarm data corresponds to one search item;
searching in the expert database according to all the searched items, and determining that the searched result is effective if the searched items in the searched result exceed a preset searching threshold value;
and when the searching result is valid, the searching result is the second dimension information, and the diagnosis information in the searching result is the diagnosis information in the second dimension information.
As a preferred embodiment of the present application, further, the determining a correction coefficient according to the first dimension information and the second dimension information, correcting the second dimension information according to the correction coefficient and the device diagnosis result, and outputting the correction result to the expert database specifically includes:
calculating a variance value of each data in the first dimension information and each corresponding data in the second dimension information, and if the variance value is larger than a preset variance value, calculating a sub-correction coefficient of the data in the first dimension information according to the data in the first dimension information and the corresponding data in the second dimension information;
otherwise, the sub-correction coefficient of the data in the first dimension information is a first preset value;
when the sub-correction coefficient is not the first preset value, correcting the value range of the corresponding data in the second dimension information according to the sub-correction coefficient, and adding the equipment diagnosis result to the diagnosis information in the second dimension information.
The expert database self-learning method based on data acquisition and equipment diagnosis provided by the embodiment comprises the steps of acquiring basic data information of each part in the industrial robot and environmental data information for deploying the industrial robot according to a first preset task table; monitoring the basic data information and the environment data information according to a second preset task table, and obtaining a device diagnosis result of the industrial robot according to a monitoring result; extracting basic data information of each component in the industrial robot and setting first dimension information in environment data information of the industrial robot, and performing fuzzy search on the first dimension information in the expert database to obtain second dimension information; and determining a correction coefficient according to the first dimension information and the second dimension information, correcting the second dimension information according to the correction coefficient and the equipment diagnosis result, and outputting the correction result to the expert database. According to the application, the data in the equipment fault prediction database is corrected through the acquired operation information and environment information of the industrial robot, so that the fault prediction capability of the industrial robot is improved.
As shown in fig. 2, an expert database self-learning device based on data acquisition and equipment diagnosis, wherein the device comprises:
the first processing module is used for collecting basic data information of each part in the industrial robot and environmental data information for deploying the industrial robot according to a first preset task table;
the second processing module is used for monitoring the basic data information and the environment data information according to a second preset task table and obtaining the equipment diagnosis result of the industrial robot according to the monitoring result;
the third processing module is used for extracting basic data information of each component in the industrial robot and setting first dimension information in environment data information of the industrial robot, and performing fuzzy search on the first dimension information in the expert database to obtain second dimension information;
and the fourth processing module is used for determining a correction coefficient according to the first dimension information and the equipment diagnosis result, correcting the second dimension information according to the correction coefficient and the equipment diagnosis result, and outputting the correction result to the expert database.
Based on the above embodiment, further, the first processing module is specifically configured to collect, at regular time, basic data information of each component in the industrial robot based on first task information in the first preset task table, where the basic data information includes special data and alarm data of each component in the industrial robot;
based on the second task information in the first preset task table, according to the application scene classification of the robot, environmental data information of the industrial robot is collected and deployed at fixed time, wherein the environmental data information comprises workshop temperature, humidity, dust and noise of the industrial robot.
Based on the above embodiment, further, the root second processing module is specifically configured to calculate, based on third task information in the second preset task table, private data in the collected basic data information at regular time, obtain trend change data of the private data, and compare the trend change data with a corresponding index threshold to obtain a first comparison result;
based on fourth task information in the second preset task table, comparing each item of data in the acquired environmental data information with a corresponding environmental index threshold value at regular time to obtain a second comparison result;
based on fifth task information in the second preset task table, calculating alarm data in the acquired basic data information at regular time to obtain alarm scores of the alarm data;
and if the first comparison result, the second comparison result and the alarm score are obtained, obtaining the equipment diagnosis result of the industrial robot.
Based on the foregoing embodiment, further, the third processing module is specifically configured to extract, as the first dimension information, data in special data not in the corresponding index threshold, the alarm data with the alarm score greater than a preset score value, and environmental data information greater than the corresponding environmental index threshold.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps 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 solution. 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 application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium.
Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), an electrical carrier signal, a telecommunication signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
The present application is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the present application, and these modifications and substitutions are intended to be included in the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (1)
1. An expert database self-learning method based on data acquisition and equipment diagnosis, which is characterized by comprising the following steps:
acquiring basic data information of each part in the industrial robot and environmental data information for deploying the industrial robot according to a first preset task table;
monitoring the basic data information and the environment data information according to a second preset task table, and obtaining a device diagnosis result of the industrial robot according to a monitoring result;
extracting basic data information of each component in the industrial robot and setting first dimension information in environment data information of the industrial robot, and performing fuzzy search on the first dimension information in the expert database to obtain second dimension information;
determining a correction coefficient according to the first dimension information and the second dimension information, correcting the second dimension information according to the correction coefficient and the equipment diagnosis result, and outputting the correction result to the expert database;
the step of collecting basic data information of each part in the industrial robot and environmental data information for deploying the industrial robot according to a first preset task table specifically comprises the following steps:
based on first task information in the first preset task table, basic data information of each part in the industrial robot is collected at fixed time, wherein the basic data information comprises special data and alarm data of each part in the industrial robot;
based on second task information in the first preset task table, environmental data information of the industrial robot is collected and deployed at fixed time, wherein the environmental data information comprises workshop temperature, humidity, dust, noise and robot application scenes of the industrial robot;
the monitoring of the basic data information and the environmental data information according to the second preset task table, and obtaining the equipment diagnosis result of the industrial robot according to the monitoring result, specifically includes:
calculating special data in the acquired basic data information at regular time based on third task information in the second preset task table to obtain trend change data of the special data, and comparing the trend change data with a corresponding index threshold to obtain a first comparison result;
based on fourth task information in the second preset task table, comparing each item of data in the acquired environmental data information with a corresponding environmental index threshold value at regular time to obtain a second comparison result;
based on fifth task information in the second preset task table, calculating alarm data in the acquired basic data information at regular time to obtain alarm scores of the alarm data;
obtaining a device diagnosis result of the industrial robot according to the first comparison result, the second comparison result and the alarm score;
the extracting basic data information of each component in the industrial robot and setting first dimension information in environment data information of the industrial robot specifically includes:
extracting data in special data which is not in the corresponding index threshold value, the alarm data with the alarm score being larger than a preset score value and environment data information with the alarm score being larger than the corresponding environment index threshold value as the first dimension information;
the step of performing the simulation search on the first dimension information in the expert database to obtain second dimension information, specifically comprising the following steps:
establishing a search item according to special data, environment data information, a robot application scene and alarm data in the first dimension information, wherein the search item comprises establishing a characteristic value, a characteristic index and a characteristic type according to the special data, the environment data information and the alarm data;
wherein each item of special data corresponds to one search item, each item of environment data information and a robot application scene corresponds to one search item, and each item of alarm data corresponds to one search item;
searching in the expert database according to all the searched items, and determining that the searched result is effective if the searched items in the searched result exceed a preset searching threshold value;
when the searching result is valid, the searching result is the second dimension information, and the diagnosis information in the searching result is the diagnosis information in the second dimension information;
the method for determining the correction coefficient according to the first dimension information and the second dimension information, correcting the second dimension information according to the correction coefficient and the equipment diagnosis result, and outputting the correction result to the expert database specifically comprises the following steps:
calculating a variance value of each data in the first dimension information and each corresponding data in the second dimension information, and if the variance value is larger than a preset variance value, calculating a sub-correction coefficient of the data in the first dimension information according to the data in the first dimension information and the corresponding data in the second dimension information;
otherwise, the sub-correction coefficient of the data in the first dimension information is a first preset value;
when the sub-correction coefficient is not the first preset value, correcting the value range of the corresponding data in the second dimension information according to the sub-correction coefficient, and adding the equipment diagnosis result to the diagnosis information in the second dimension information.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109204389A (en) * | 2018-09-12 | 2019-01-15 | 济南轨道交通集团有限公司 | A kind of subway equipment fault diagnosis and self-healing method, system |
| CN111007452A (en) * | 2019-12-07 | 2020-04-14 | 新奥数能科技有限公司 | Fault diagnosis method and device of data acquisition system |
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| US20040199573A1 (en) * | 2002-10-31 | 2004-10-07 | Predictive Systems Engineering, Ltd. | System and method for remote diagnosis of distributed objects |
| GB0415951D0 (en) * | 2004-07-16 | 2004-08-18 | Meadwestvaco Packaging Systems | A method for identifying abnormal operation of a machine and an apparatus therefor |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109204389A (en) * | 2018-09-12 | 2019-01-15 | 济南轨道交通集团有限公司 | A kind of subway equipment fault diagnosis and self-healing method, system |
| CN111007452A (en) * | 2019-12-07 | 2020-04-14 | 新奥数能科技有限公司 | Fault diagnosis method and device of data acquisition system |
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