CN116337135A - Instrument fault diagnosis method, system, electronic equipment and readable storage medium - Google Patents
Instrument fault diagnosis method, system, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The invention relates to a method, a system, electronic equipment and a readable storage medium for diagnosing instrument faults, wherein the method comprises the steps of acquiring self-checking data and detection data of an industrial instrument, wherein the self-checking data are working operation data of the industrial instrument, and the detection data are data of the industrial instrument for detecting equipment or products in a production process; screening the self-checking data according to a data selection rule to determine fault analysis data; determining fluctuation data of the fault analysis data according to the fault analysis data and the data fluctuation calculation rule; determining abnormal parameters according to the fluctuation data and the fault analysis rules; and outputting abnormal prompt information according to the abnormal parameters and the abnormal prompt rules. The invention improves the problem of false alarm missing of the industrial instrument on own abnormal conditions by analyzing the fluctuation condition of the data.
Description
Technical Field
The present disclosure relates to the field of fault diagnosis technologies, and in particular, to a method, a system, an electronic device, and a readable storage medium for diagnosing a fault of an instrument.
Background
Along with the development of technology, the application number of industrial instruments is gradually increased in the industrial production process, and a factory detects the production process, the product quality, the operation condition, the environmental protection index and the like through the industrial instruments, so that a data base is provided for a control system.
At present, industrial instruments tend to be intelligent, intelligent industrial instruments have a self-diagnosis function except a detection function, detect faults of the intelligent industrial instruments when detecting data of other equipment, and the intelligent industrial instruments can detect faults of the intelligent industrial instruments only by judging the fault condition when a certain parameter of the intelligent industrial instruments is not in a preset threshold range, and the fault detection is single, so that the abnormal condition of the intelligent industrial instruments is misreported and missed.
The prior art solutions described above have the following drawbacks: the self-diagnosis function of the industrial instrument has the problem of false alarm and missing report for the self-abnormality.
Disclosure of Invention
In order to solve the problem of false alarm and missing report of an industrial instrument on own abnormal conditions, the application provides an instrument fault diagnosis method, an instrument fault diagnosis system, electronic equipment and a readable storage medium.
In a first aspect of the present application, a method of diagnosing a meter fault is provided. The method comprises the following steps:
acquiring self-checking data and detection data of an industrial instrument, wherein the self-checking data are working operation data of the industrial instrument, and the detection data are data of the industrial instrument for detecting equipment or products in a production process;
screening the self-checking data according to a data selection rule to determine fault analysis data;
determining fluctuation data of the fault analysis data according to the fault analysis data and the data fluctuation calculation rule;
determining abnormal parameters according to the fluctuation data and the fault analysis rule;
and outputting abnormal prompt information according to the abnormal parameters and the abnormal prompt rules.
According to the technical scheme, after the self-checking data and the detection data of the industrial instrument are obtained, the data are subjected to preliminary screening, the accuracy of subsequent calculation can be improved while the calculated amount is reduced through screening the data, fluctuation calculation is performed on the screened data, fluctuation data are determined, then the fault conditions of the industrial instrument are analyzed according to the fluctuation data, corresponding abnormal parameters are determined through analysis of the fluctuation data, and corresponding fault abnormal processing modes are called through the abnormal parameters and corresponding prompt information is output. By analyzing the fluctuation condition of the fault analysis data, the corresponding fault condition can be obtained, and the problem that the industrial instrument fails to report the own abnormal condition by mistake is reduced to a certain extent.
In one possible implementation manner, the screening the self-checking data according to the data selection rule, and determining the fault analysis data includes:
classifying the self-checking data according to a preset classification rule;
and screening the self-checking data of each class according to a preset data time range, and determining fault analysis data.
According to the technical scheme, the acquired self-checking data are classified according to the data types, the classified data are screened according to the preset screening rule, the fault analysis data are determined, the subsequent data calculation amount can be reduced through data classification and screening, meanwhile, the data after screening can enable the final calculated result to be more approximate to the data condition in a real-time state, and a data basis is provided for improving the accuracy rate of fault analysis.
In one possible implementation manner, the determining the fluctuation data of the fault analysis data according to the fault analysis data and the data fluctuation calculation rule includes:
the fluctuation data comprises parameter fluctuation data and overall fluctuation data;
calculating standard deviation of fault analysis data of each type, wherein the standard deviation is the parameter fluctuation data;
calculating the parameter discrete coefficient of each type of fault analysis data;
and calculating overall fluctuation data according to a preset coefficient selection rule and the parameter discrete coefficient.
In one possible implementation manner, the determining an abnormal parameter according to the fluctuation data and the fault analysis rule includes:
the abnormal parameters comprise a first abnormal parameter and a second abnormal parameter;
when the parameter fluctuation data is not in the fluctuation preset range, judging whether the overall fluctuation data is in the overall fluctuation range or not;
if not, the detection module corresponding to the parameter fluctuation data has a first abnormality;
if yes, a second abnormality exists in the production flow corresponding to the fluctuation data;
and determining a first abnormal parameter and a second abnormal parameter according to a preset abnormal parameter corresponding table, the first abnormality and the second abnormality.
According to the technical scheme, the fluctuation condition of the data is judged through the parameter fluctuation data, whether the data of a certain parameter has sudden change or not can be obtained, when the sudden change exists, the module for detecting the data possibly has a problem, then the overall fluctuation data is judged, if the overall fluctuation data indicates that the data does not have obvious fluctuation, the data change is indicated to be possibly generated in all the data, and at the moment, the change of all the self-checking data possibly caused by the change of the process flow detected by the industrial instrument is indicated to be possible. The possible faults of the industrial instrument are analyzed by calculating the fluctuation conditions of certain data and the fluctuation conditions of all data, so that the predicted fault results are more accurate, and the problem of false alarm and missing report can be reduced to a certain extent.
In one possible implementation, the method further includes:
the anomaly parameters include a third anomaly parameter;
when the parameter fluctuation data is in a fluctuation preset range, judging whether the self-checking data acquired in real time is in a self-checking threshold range or not;
if not, a third abnormality exists in the detection module corresponding to the self-checking data;
and determining a third abnormal parameter according to a preset abnormal parameter corresponding table and the third abnormality.
In one possible implementation manner, the outputting the abnormality prompting information according to the abnormality parameter and the abnormality prompting rule includes:
according to the abnormal parameters, a corresponding prompt strategy is called;
and marking the self-checking data and the detection data according to the prompt strategy and outputting corresponding abnormal prompt information.
In one possible implementation, the tag is a confidence tag for the self-test data and the detection data.
In a second aspect of the present application, a meter fault diagnosis system is provided. The system comprises:
the data acquisition module is used for acquiring self-checking data and detection data of the industrial instrument;
the data screening module is used for determining fault analysis data according to the data selection rules and the self-checking data;
the fluctuation calculation module is used for determining fluctuation data of the fault analysis data according to the fault analysis data and the data fluctuation calculation rule;
the abnormality determining module is used for determining an abnormality parameter according to the fluctuation data and the fault analysis rule;
and the abnormality prompting module is used for marking the self-checking data and the detection data according to the abnormality parameters and the abnormality prompting rules and outputting abnormality prompting information.
In a third aspect of the present application, an electronic device is provided. The electronic device includes: a memory and a processor, the memory having stored thereon a computer program, the processor implementing the method as described above when executing the program.
In a fourth aspect of the present application, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as according to the first aspect of the present application.
In summary, the present application includes at least one of the following beneficial technical effects:
1. firstly, acquiring self-checking data and detection data of an industrial instrument, after acquiring the self-checking data and the detection data, carrying out preliminary screening on the data, carrying out fluctuation calculation on the screened data to determine fluctuation data, then analyzing the fault condition of the industrial instrument according to the fluctuation data, wherein each fault condition has corresponding abnormal parameters, determining the corresponding abnormal parameters through analysis of the fluctuation data, calling a processing mode corresponding to fault abnormality through the abnormal parameters, and outputting corresponding prompt information. By analyzing the fluctuation condition of the fault analysis data, the corresponding fault condition can be obtained, and the problem that the industrial instrument fails to report the own abnormal condition by mistake is reduced to a certain extent;
2. the fluctuation condition of the data is judged through the parameter fluctuation data, when obvious fluctuation exists in the fluctuation condition, the module for detecting the data is described as possibly having problems, then the overall fluctuation data is judged, if the overall fluctuation data indicates that the data does not have obvious fluctuation, the data change is indicated to be possibly generated in all the data, and the data change is indicated to be possibly generated in all the self-checking data due to the change of the process flow detected by the industrial instrument. The possible faults of the industrial instrument are analyzed by calculating the fluctuation conditions of certain data and the fluctuation conditions of all data, so that the predicted fault results are more accurate, and the problem of false alarm and missing report can be reduced to a certain extent.
Drawings
Fig. 1 is a schematic flow chart of an instrument fault diagnosis method provided by the application.
Fig. 2 is a schematic structural diagram of the instrument fault diagnosis system provided in the present application.
Fig. 3 is a schematic structural diagram of an electronic device provided in the present application.
In the figure, 200, an instrument fault diagnosis system; 201. a data acquisition module; 202. a data screening module; 203. a fluctuation calculation module; 204. an anomaly determination module; 205. an abnormality prompting module; 301. a CPU; 302. a ROM; 303. a RAM; 304. an I/O interface; 305. an input section; 306. an output section; 307. a storage section; 308. a communication section; 309. a driver; 310. removable media.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
The embodiment of the application provides an instrument fault diagnosis method, and the main flow of the method is described as follows.
As shown in fig. 1:
step S101: and acquiring self-checking data and detection data of the industrial instrument.
Specifically, the method is applied to a meter control system, and one or more industrial meters are contained in the meter control system. In the industrial instrument, parameters of the industrial instrument are not only data detection of part of process flow states in the system, but also fault detection data of the industrial instrument. The self-checking data represent instrument fault data such as voltage, current, resistance value, temperature, humidity, amplitude, effective power and the like of the faults of the industrial instrument, and the detecting data represent instrument detecting data such as measured value high-low limit alarm, measured value speed alarm, measured value deviation alarm, output upper-low limit alarm and the like of the technological process state of the instrument detected by the industrial instrument. In one example, the self-test data and the detection data are acquired by receiving data output from the industrial meter by a wired or wireless transmission method, and the data are transmitted by a transmission method of a bus such as Modbus , CANopen , etherCAT, PROFIBUS, etc., and in this example, the transmission method of the data is not limited only as long as the collection of the data output from the industrial meter can be achieved. The self-checking data and the detection data are updated in real time, namely, the detected data are output in real time when the industrial instrument works, and the data are acquired according to the latest data when the data are acquired.
Step S102: and determining fault analysis data according to the data selection rule and the self-checking data.
Specifically, the self-checking data are classified according to different monitoring contents of the self-checking data, and the self-checking data of each type are screened according to the data of each type and the data time range corresponding to the type, so as to determine fault analysis data. For example, the self-checking data includes the operating voltage of the industrial instrument and the operating temperature of the industrial instrument, for example, for the operating voltage, the data of the last two hours need to be analyzed, and for the operating temperature, the data of the last three hours need to be analyzed, and then all the self-checking data are screened according to the requirement to determine the operating voltage fault analysis data and the operating temperature fault analysis data.
Through screening the data, the calculated data is limited in a certain range, so that on one hand, the calculated amount in the fault determination process is reduced, and on the other hand, different selection ranges are corresponding to different types of data, the final calculated result is more approximate to the data condition in a real-time state, and a data base is provided for improving the accuracy of fault analysis.
Step S103: and determining fluctuation data of the fault analysis data according to the fault analysis data and the data fluctuation calculation rule.
Specifically, the fluctuation data includes parameter fluctuation data and overall fluctuation data, the standard deviation corresponding to each type of fault analysis data is calculated, the standard deviation is the parameter fluctuation data, the discrete coefficient of each type of fault analysis data is calculated, the discrete coefficient is a parameter discrete coefficient, the discrete coefficients of all parameter discrete coefficients are calculated according to all parameter discrete coefficients, and the discrete coefficient is the overall fluctuation data. Judging fluctuation conditions of the data according to standard deviation for each type of fault analysis data, wherein the dimensions of the data of the same type are the same; for the fluctuation situation of the plurality of class data, it is necessary to judge the overall fluctuation situation of the plurality of class data by calculating the discrete coefficient, and since the discrete coefficient is a dimensionless quantity, the discrete coefficient should be used to judge the fluctuation situation of the data when comparing the plurality of sets of data with different dimensionalities. For example, the standard deviation of the operating voltage and the standard deviation of the operating temperature are determined, the standard deviation can reflect the fluctuation situation of the operating voltage and the operating temperature, the fluctuation situation of the two types of data are general, and the fluctuation situation of the operating voltage and the operating temperature can be reflected by calculating the discrete coefficients of the two data of the discrete coefficient of the operating voltage and the discrete coefficient of the operating temperature. The calculation method of the standard deviation and the discrete coefficient is a well known technology of a person skilled in the art, and is not described herein.
Step S104: and determining abnormal parameters according to the fluctuation data and the fault analysis rules.
Specifically, according to the parameter fluctuation data and the overall fluctuation data, determining an abnormal parameter, wherein the abnormal parameter comprises a first abnormal parameter, a second abnormal parameter and a third abnormal parameter, when the parameter fluctuation data is within a fluctuation preset range, the data change of a certain parameter is stable, then the latest data corresponding to the parameter is obtained, whether the latest data is within a self-checking threshold range is judged, if yes, no abnormality exists in a detection module corresponding to the parameter, if no, the abnormality exists in the detection module corresponding to the parameter, the time of the abnormality is long, the abnormality is a third abnormality, and the abnormal parameter corresponding to the third abnormality is the third abnormal parameter. When the parameter fluctuation data is not in the fluctuation preset range, the sudden change of the data change of a certain parameter is shown, then whether the overall fluctuation data is in the overall fluctuation range is judged, if so, the data change with similar change amplitude of other parameters is shown, the abnormal change is possibly related to the process flow change detected by the industrial instrument, the abnormal is a second abnormal, the abnormal parameter corresponding to the second abnormal is a second abnormal parameter, if not, the data of the corresponding parameter is greatly changed, and the data of the other data is not changed to the same degree, the abnormality of the detection module for detecting the corresponding parameter is shown, the abnormality is a first abnormality, and the abnormal parameter corresponding to the first abnormality is a first abnormal parameter. Each anomaly has corresponding anomaly parameters, and the corresponding anomaly conditions are returned according to the difference of the anomaly parameters according to a preset anomaly parameter corresponding table, wherein the anomaly parameter corresponding table comprises the anomaly conditions and the anomaly parameters corresponding to the anomaly conditions.
In an embodiment, the determining of the self-checking threshold range further includes obtaining historical operation data, and determining the target prediction model according to the historical operation data and a preset training model. The historical operation data comprise monitoring working conditions, monitoring data, whether the data are abnormal or not and an abnormal threshold value, the monitoring working conditions indicate that the industrial instrument needs to monitor a technological process and working conditions of the industrial instrument, the monitoring data indicate data monitored by the industrial instrument under the monitoring working conditions, whether the data are abnormal or not indicates whether the monitoring data are abnormal or not, the abnormal threshold value indicates an abnormal threshold value corresponding to the monitoring data under the monitoring working conditions, the historical operation data are input into a preset training model, and a target prediction model can be obtained through model training. The target prediction model comprises the corresponding relation among the monitoring working condition, the monitoring data, whether the data is abnormal or not and the abnormal threshold value, and the corresponding abnormal threshold value is determined by inputting the monitoring working condition, the monitoring data and whether the data is abnormal or not into the target prediction model. In this embodiment, the preset training model is a neural network model, and in other embodiments, other training models may be used, which is not limited herein.
By training the target prediction model, the abnormal threshold corresponding to the monitoring working condition, the monitoring data and the abnormal condition of the data of the industrial instrument can be determined, the abnormal threshold can be adaptively changed according to different detection conditions, the accuracy of industrial instrument fault detection is improved, and false alarm and missing report of faults are reduced to a certain extent.
In another embodiment, the determining of the self-checking threshold range further includes obtaining historical anomaly data, and determining the self-checking threshold range according to the historical anomaly data and a threshold calculation rule. The historical abnormal data comprise monitoring working conditions, abnormal data, monitoring time, abnormal data types and instrument service life, wherein the monitoring working conditions represent the process flow and working conditions of the industrial instrument. The abnormal data refers to abnormal data detected by the industrial instrument under the monitoring working condition. The abnormal data type refers to the type of abnormal data detected, for example, the monitored data type is current, voltage, power, etc. The monitoring time indicates the acquisition time corresponding to the abnormal data. The meter life represents the life of an industrial meter used, for example, for a certain industrial meter, it has been used for three years, so the meter life is three years. And classifying the abnormal data according to the monitoring working conditions, the abnormal data types and the service life of the instrument, namely processing the abnormal data in the same monitoring working conditions, the same abnormal data types and the service life of the instrument, slicing the abnormal data according to the monitoring time, acquiring the maximum value and the minimum value of the abnormal data in each data slice, acquiring the minimum value in all the maximum values to obtain the maximum threshold value, and acquiring the maximum value in all the minimum values to obtain the minimum threshold value. The maximum threshold and the minimum threshold form a self-checking threshold range.
The historical abnormal data are obtained, the historical abnormal data are classified according to the monitoring working conditions, the abnormal data types and the service life of the instrument, threshold value calculation is carried out on the classified data, the influence of the monitoring working conditions and the service life of the instrument on the detected abnormal data is reduced to a certain extent, and the accuracy of fault detection is improved to a certain extent.
Judging the fluctuation condition of the data by calculating the standard deviation of each type of data, namely parameter fluctuation data, obtaining whether the data of a certain parameter has abrupt change, when the abrupt change exists, indicating that a module for detecting the data possibly has problems, then calculating the discrete coefficient of each type of data, namely parameter discrete coefficient, and calculating the discrete coefficient of all parameter discrete coefficients, namely overall fluctuation data, wherein if the overall fluctuation data indicates that the data does not obviously fluctuate, the data is possibly changed, and the process flow detected by the industrial instrument is possibly changed, so that all self-checking data are possibly changed. The possible faults of the industrial instrument are analyzed by calculating the fluctuation conditions of certain data and the fluctuation conditions of all data, so that the predicted fault results are more accurate, and the problem of false alarm and missing report can be reduced to a certain extent.
Step S105: and outputting abnormal prompt information according to the abnormal parameters and the abnormal prompt rules.
Specifically, the corresponding prompt strategy is called according to the abnormal parameters, when the abnormality occurs, the data detected by the industrial instrument is unavailable, the data detected by the industrial instrument is marked with a reliability through different abnormal parameters, the self-checking data and the detection data are marked through the abnormal conditions and the association relation of various data, and in the subsequent data use, the data are screened according to different requirements through the reliability marks. And adopting different abnormality prompting modes, namely the abnormality prompting information, for different abnormality conditions, namely different abnormality parameters. The database is pre-stored with a corresponding table of prompt information of abnormal parameters, and when abnormality occurs, the corresponding prompt information is called and prompted according to the abnormal parameters. For example, when the first abnormality occurs in the working current, the corresponding prompt information is called and output, and then the data detected in the abnormal stage is marked with reliability. The first abnormality of the working current may be that the working current detection module of the working instrument fails, but the abnormality of the module cannot be determined only according to data analysis, but when the current is abnormal, the data of the voltage may be affected, besides the reliability marking of the current data, the reliability marking of the voltage data in the same time period is needed, the reliability marking of the related data is also different according to the difference of fluctuation conditions reflected by the fluctuation data of the parameters, and the correspondence relation between the fluctuation conditions and the data reliability of the corresponding parameters and the related parameters is prestored in a database.
Judging the faults of the instruments through fluctuation data, setting corresponding fault parameters for the faults of each instrument, determining the fault type of the instrument according to data analysis, and then determining the fault parameters, wherein the fault parameters and the corresponding fault processing rules are prestored in a database, and calling the corresponding fault processing rules through the fault parameters to finish abnormal prompt and data marking. And marking the reliability of the data of the industrial instrument according to the analysis result, and providing a data basis for the subsequent fault analysis or process flow data analysis and the like.
Referring to fig. 2, an instrument fault diagnosis system 200 according to an embodiment of the present application includes:
a data acquisition module 201, configured to acquire self-checking data and detection data of an industrial instrument;
the data screening module 202 is configured to determine fault analysis data according to a data selection rule and the self-checking data;
a fluctuation calculation module 203, configured to determine fluctuation data of the fault analysis data according to the fault analysis data and a data fluctuation calculation rule;
an anomaly determination module 204, configured to determine anomaly parameters according to the fluctuation data and the fault analysis rule;
the anomaly prompt module 205 is configured to label the self-detection data and the detection data according to the anomaly parameters and the anomaly prompt rule, and output anomaly prompt information.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the foregoing method embodiment for the specific working process of the described module, which is not described herein again.
The embodiment of the application discloses electronic equipment. Referring to fig. 3, the electronic device includes a Central Processing Unit (CPU) 301 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage portion 307 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other by a bus. An input/output (I/O) interface 304 is also connected to the bus.
The following components are connected to the I/O interface 304: an input section 305 including a keyboard, a mouse, and the like; an output portion 306 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage portion 307 including a hard disk and the like; and a communication section 308 including a network interface card such as a LAN card, a modem, or the like. The communication section 308 performs communication processing via a network such as the internet. A driver 309 is also connected to the I/O interface 304 as needed. A removable medium 310 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 309 as needed, so that a computer program read out therefrom is installed into the storage section 307 as needed.
In particular, according to embodiments of the present application, the process described above with reference to flowchart fig. 1 may be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such embodiments, the computer program may be downloaded and installed from a network via the communication portion 308, and/or installed from the removable media 310. The above-described functions defined in the apparatus of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 301.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the application referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or their equivalents is possible without departing from the spirit of the application. Such as the above-mentioned features and the technical features having similar functions (but not limited to) applied for in this application are replaced with each other.
Claims (10)
1. A method of diagnosing a meter fault, comprising:
acquiring self-checking data and detection data of an industrial instrument, wherein the self-checking data are working operation data of the industrial instrument, and the detection data are data of the industrial instrument for detecting equipment or products in a production process;
screening the self-checking data according to a data selection rule to determine fault analysis data;
determining fluctuation data of the fault analysis data according to the fault analysis data and the data fluctuation calculation rule;
determining abnormal parameters according to the fluctuation data and the fault analysis rule;
and outputting abnormal prompt information according to the abnormal parameters and the abnormal prompt rules.
2. The method for diagnosing a meter fault according to claim 1, wherein the screening the self-test data according to the data selection rule to determine fault analysis data comprises:
classifying the self-checking data according to a preset classification rule;
and screening the self-checking data of each class according to a preset data time range, and determining fault analysis data.
3. The meter trouble shooting method of claim 2 wherein said determining fluctuation data of said trouble analysis data based on said trouble analysis data and a data fluctuation calculation rule comprises:
the fluctuation data comprises parameter fluctuation data and overall fluctuation data;
calculating standard deviation of fault analysis data of each type, wherein the standard deviation is the parameter fluctuation data;
calculating the parameter discrete coefficient of each type of fault analysis data;
and calculating overall fluctuation data according to a preset coefficient selection rule and the parameter discrete coefficient.
4. The meter fault diagnosis method according to claim 3, wherein the determining of the abnormality parameter according to the fluctuation data and the fault analysis rule includes:
the abnormal parameters comprise a first abnormal parameter and a second abnormal parameter;
when the parameter fluctuation data is not in the fluctuation preset range, judging whether the overall fluctuation data is in the overall fluctuation range or not;
if not, the detection module corresponding to the parameter fluctuation data has a first abnormality;
if yes, a second abnormality exists in the production flow corresponding to the fluctuation data;
and determining a first abnormal parameter and a second abnormal parameter according to a preset abnormal parameter corresponding table, the first abnormality and the second abnormality.
5. The meter trouble shooting method of claim 4 further comprising:
the anomaly parameters include a third anomaly parameter;
when the parameter fluctuation data is in a fluctuation preset range, judging whether the self-checking data acquired in real time is in a self-checking threshold range or not;
if not, a third abnormality exists in the detection module corresponding to the self-checking data;
and determining a third abnormal parameter according to a preset abnormal parameter corresponding table and the third abnormality.
6. The instrument fault diagnosis method according to claim 1, wherein the outputting of the abnormality notification information according to the abnormality parameter and abnormality notification rule includes:
according to the abnormal parameters, a corresponding prompt strategy is called;
and marking the self-checking data and the detection data according to the prompt strategy and outputting corresponding abnormal prompt information.
7. The meter trouble shooting method of claim 6 wherein the indicia is a confidence label for the self-test data and the test data.
8. An instrument fault diagnosis system, comprising:
the data acquisition module (201) is used for acquiring self-checking data and detection data of the industrial instrument;
the data screening module (202) is used for determining fault analysis data according to data selection rules and the self-checking data;
a fluctuation calculation module (203) for determining fluctuation data of the failure analysis data according to the failure analysis data and a data fluctuation calculation rule;
an anomaly determination module (204) for determining anomaly parameters based on the fluctuation data and fault analysis rules;
and the abnormality prompting module (205) is used for marking the self-checking data and the detection data according to the abnormality parameters and the abnormality prompting rules and outputting abnormality prompting information.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program capable of being loaded by the processor and performing the method according to any of claims 1 to 7.
10. A computer readable storage medium, characterized in that a computer program is stored which can be loaded by a processor and which performs the method according to any one of claims 1 to 7.
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| CN202310274773.8A CN116337135A (en) | 2023-03-18 | 2023-03-18 | Instrument fault diagnosis method, system, electronic equipment and readable storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116579768A (en) * | 2023-07-12 | 2023-08-11 | 南京华天科技发展股份有限公司 | Power plant on-line instrument operation and maintenance management method and system |
| CN117490765A (en) * | 2023-11-02 | 2024-02-02 | 岳阳长炼机电工程技术有限公司 | Water inlet monitoring system for interlocking instrument |
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2023
- 2023-03-18 CN CN202310274773.8A patent/CN116337135A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116579768A (en) * | 2023-07-12 | 2023-08-11 | 南京华天科技发展股份有限公司 | Power plant on-line instrument operation and maintenance management method and system |
| CN116579768B (en) * | 2023-07-12 | 2023-09-12 | 南京华天科技发展股份有限公司 | Power plant on-line instrument operation and maintenance management method and system |
| CN117490765A (en) * | 2023-11-02 | 2024-02-02 | 岳阳长炼机电工程技术有限公司 | Water inlet monitoring system for interlocking instrument |
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