CN111427934A - Method and system for mining association of abnormal event and context event thereof - Google Patents
Method and system for mining association of abnormal event and context event thereof Download PDFInfo
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Abstract
The invention relates to the technical field of production equipment maintenance, in particular to a method and a system for mining association of an abnormal event and a context event thereof. Wherein the method comprises the steps of: s1, acquiring an abnormal event table and a context event table; s2, obtaining a to-be-mined transaction table according to the abnormal event table and the context event table; s3, obtaining a mining result according to the transaction table to be mined, wherein the mining result is as follows: at least one of a context event most relevant to the abnormal event or a combination of high frequency occurring faults or a combination of high frequency occurring context events; and S4, displaying the digging result. According to the method, the context events most relevant to the abnormal events, the high-frequency fault combination or the high-frequency context event combination can be found out through processing the input abnormal event table and the context event table, and the method becomes an auxiliary tool for fault diagnosis and troubleshooting and has the advantages of simplicity in operation and low cost.
Description
Technical Field
The invention relates to the technical field of production equipment maintenance, in particular to a method and a system for mining association of an abnormal event and a context event thereof.
Background
The promotion of industrialization provides rich substances for human life, and simultaneously gradually becomes a killer mace threatening personal safety, and the topic of safe production is more and more concerned due to frequent production accidents. Therefore, in the daily production management process, the production equipment needs to be regularly checked for safety and discharged with hidden dangers, so that the production safety is improved, and the probability of safety accidents is reduced. After equipment fails or is abnormal, operation and maintenance personnel on an industrial field need to diagnose and troubleshoot the failure according to a manual or overhaul experience, and for new overhaul personnel or personnel participating in event analysis across the boundary, the analysis and troubleshooting process is full of challenges and needs to be supported and matched by industrial experts. Therefore, an intelligent system capable of mining operation and symptoms related to faults or related faults from pre-accumulated operation and maintenance knowledge is urgently needed on site to assist operation and maintenance personnel in fault diagnosis, so that dependence on field experts is reduced, the difficulty of troubleshooting is reduced, and the availability of equipment is finally improved.
Disclosure of Invention
The invention aims to provide a method and a system for mining association of an abnormal event and a context event thereof, so as to solve the problem that the maintenance process is complex after a production device fails.
In order to solve the technical problems, the technical scheme of the invention is as follows:
according to one aspect of the invention, a method for mining association between an abnormal event and a context event thereof is provided, which comprises the following steps:
s1, acquiring an abnormal event table and a context event table;
s2, obtaining a to-be-mined transaction table according to the abnormal event table and the context event table;
s3, obtaining a mining result according to the transaction table to be mined, wherein the mining result is as follows: at least one of a context event most relevant to the abnormal event or a combination of high frequency occurring faults or a combination of high frequency occurring context events;
and S4, displaying the digging result.
Further, step S2 specifically includes:
and grouping and filtering the abnormal event table and the context event table by using one or more of a time correlation method, a BOM correlation method and a PID correlation method to obtain a to-be-mined transaction table.
Further, step S3 specifically includes:
and processing the transaction table to be mined by utilizing any one mining mode of the frequent mode, the frequent sequence mode with time intervals or the frequent sequence mode without time intervals to obtain a mining result.
Further, step S3 specifically includes:
and processing the transaction table to be mined by adopting any one of an FP-Growth algorithm, a BIDE algorithm or an EMEMISP algorithm to obtain a mining result.
Further, the method further comprises:
and S5, sending the mining result to the terminal.
According to another aspect of the present invention, there is provided a system for mining association between an abnormal event and its context event, including:
the acquisition module is used for acquiring the abnormal event table and the context event table;
the transaction organization module is used for obtaining a transaction table to be mined according to the abnormal event table and the context event table;
the mining module is used for obtaining a mining result according to the transaction table to be mined, wherein the mining result is as follows: at least one of a context event most relevant to the abnormal event or a combination of high frequency occurring faults or a combination of high frequency occurring context events;
and the display module is used for displaying the excavation result.
Further, the transaction organization module is specifically configured to:
and grouping and filtering the abnormal event table and the context event table by using one or more of a time correlation method, a BOM correlation method and a PID correlation method to obtain a to-be-mined transaction table.
Further, the mining module is specifically configured to:
and processing the transaction table to be mined by utilizing any one mining mode of the frequent mode, the frequent sequence mode with time intervals or the frequent sequence mode without time intervals to obtain a mining result.
Further, the mining module is specifically configured to:
and processing the transaction table to be mined by adopting any one of an FP-Growth algorithm, a BIDE algorithm or an EMEMISP algorithm to obtain a mining result.
Further, the system further comprises:
and the sending module is used for sending the mining result to the terminal.
The scheme of the invention at least comprises the following beneficial effects:
according to the scheme of the invention, the context events most relevant to the abnormal events, the high-frequency fault combination or the high-frequency context event combination can be found out through processing the pre-accumulated abnormal event table and the context event table, so that the fault diagnosis and troubleshooting auxiliary tool has the advantages of simplicity in operation and low cost.
Drawings
FIG. 1 is a step diagram of a method for mining association between an abnormal event and a context event thereof according to an embodiment of the present invention;
FIG. 2 is a device connection diagram of an abnormal event and its context event association mining system according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of a nuclear power plant;
FIG. 4 is a flowchart of a method for mining association between an abnormal event and a context event thereof according to an embodiment of the present invention;
FIG. 5 is a schematic view showing an axial locus in a 8-shape;
fig. 6 is a graph of 13 relationships for AB events.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment of the invention provides an abnormal event and a method and a system for mining association of context events thereof.
As shown in fig. 1, an embodiment of the present invention provides a method for mining association between an abnormal event and a context event thereof, including the following steps:
s1, acquiring an abnormal event table and a context event table;
s2, obtaining a to-be-mined transaction table according to the abnormal event table and the context event table;
s3, obtaining a mining result according to the transaction table to be mined, wherein the mining result is as follows: at least one of a context event most relevant to the abnormal event or a combination of high frequency occurring faults or a combination of high frequency occurring context events;
and S4, displaying the digging result.
According to the method, the context events most relevant to the abnormal events, the high-frequency fault combination or the high-frequency context event combination can be found out through processing the pre-accumulated abnormal event table and the context event table, so that the method becomes an auxiliary tool for fault diagnosis and troubleshooting, and has the advantages of simplicity in operation and low cost.
In an optional embodiment of the present invention, step S2 specifically includes:
and grouping and filtering the abnormal event table and the context event table by using one or more of a time correlation method, a BOM correlation method and a PID correlation method to obtain a to-be-mined transaction table.
The time correlation method is to manually select the time range of the matching of the context events, and the format is from the front (back) x days to the front (back) y days of the occurrence of the abnormality. As shown in tables 1, 2, and 3, table 1 is a context event table, table 2 is an abnormal event table, table 3 is a transaction table to be mined by performing association and filtering according to tables 1 and 2 by using a time association method, and the matching time range of this example is the first 5 days of the occurrence of an abnormal event.
Table 1 context event table
TABLE 2 abnormal event Table
| Event sequence number | Time of occurrence of |
| 1 | 01-04 00:00 |
| 2 | 02-13 00:00 |
| … |
Table 3 transaction table to be mined
| Transaction ID | Event(s) | Device |
| 1 | Operation of changing water | Is free of |
| 1 | Rapid rise of seal leakage flow | Leakage flow of |
| 1 | High pressure hydrophobic flow rate decreases rapidly | |
| 1 | Slow decrease of seal leakage reflux | Charging capacity on |
| 1 | Upper charge flow rate slowly rising | RCP leakage reflux |
| 2 | Operation of changing water | Is free of |
| 2 | Fast sealing leakage flowRise up | Leakage flow of RCP |
Wherein, RCP-reactor coolant system, RCV-chemical and volume control system, AHP-high pressure heating water supply system, the following are the same.
The BOM (Bill of Materials) correlation method is to specify abnormal event device screening variables, for example, as shown in fig. 3, a sealing abnormal leakage is a failure mode of a main pump device in an RCP in a nuclear island, thereby excluding AHP and RCV variables of a conventional island, which are specifically shown in tables 4 and 5:
table 4 transaction table to be mined before filtering
| Transaction ID | Event(s) | Device |
| 1 | Operation of changing water | Is free of |
| 1 | Rapid rise of seal leakage flow | Leakage flow of |
| 1 | High pressure hydrophobic flow rate decreases rapidly | |
| 1 | Sealing leakage return flowSlowly descends | Charging capacity on |
| 1 | Upper charge flow rate slowly rising | RCP leakage reflux |
| 2 | Operation of changing water | Is free of |
| 2 | Rapid rise of seal leakage flow | Leakage flow of RCP |
TABLE 5 transaction Table to be mined filtered by BOM correlation method
| Transaction ID | Event(s) | Device |
| 1 | Operation of changing water | Is free of |
| 1 | Rapid rise of seal leakage flow | Leakage flow of |
| 1 | Upper charge flow rate slowly rising | RCP leakage reflux |
| 2 | Operation of changing water | Is free of |
| 2 | Rapid rise of seal leakage flow | Leakage flow of RCP |
PID (Piping and Instrumentation Diagram) association, which is the association of equipment variables found by PID map. Example (c): the abnormal sealing leakage influences the leakage backflow flow rate of a variable RCP, and the abnormal sealing leakage influences the leakage backflow flow rate of the variable RCP and is related to a plurality of variables such as the charging flow rate on the RCV according to a PID (proportion integration differentiation) diagram, so the variables are reserved, and the detailed process is not repeated.
The user can select one or more of the abnormal event table and the context event table to group and filter according to needs to obtain the transaction table to be mined, so that the actual requirements are met, and the accuracy of the result is improved.
In an optional embodiment of the present invention, step S3 specifically includes:
and processing the transaction table to be mined by utilizing any one mining mode of the frequent mode, the frequent sequence mode with time intervals or the frequent sequence mode without time intervals to obtain a mining result.
According to the needs of users, different mining modes can be adopted to process the transaction table to be mined, so that a mining result is obtained, the method is more flexible, and the applicability is enhanced.
In an optional embodiment of the present invention, step S3 specifically includes:
and processing the transaction table to be mined by adopting any one of an FP-Growth algorithm, a BIDE algorithm or an EMEMISP algorithm to obtain a mining result.
As shown in fig. 1, in an alternative embodiment of the present invention, the method further includes:
and S5, sending the mining result to the terminal.
The terminal can be a fixed terminal or a mobile terminal, so that personnel in charge of monitoring equipment faults can check and receive the excavation result in time, fault diagnosis and troubleshooting are carried out in time, and production and personnel safety are improved.
As shown in fig. 2, an embodiment of the present invention provides a system for mining association between an abnormal event and a context event thereof, including:
the acquisition module is used for acquiring the abnormal event table and the context event table;
the transaction organization module is used for obtaining a transaction table to be mined according to the abnormal event table and the context event table;
the mining module is used for obtaining a mining result according to the transaction table to be mined, wherein the mining result is as follows: at least one of a context event most relevant to the abnormal event or a combination of high frequency occurring faults or a combination of high frequency occurring context events;
and the display module is used for displaying the excavation result.
According to the method, the context events most relevant to the abnormal events, the high-frequency fault combination or the high-frequency context event combination can be found out through processing the input abnormal event table and the context event table, and the method becomes an auxiliary tool for fault diagnosis and troubleshooting and has the advantages of simple system structure and low cost.
In an optional embodiment of the present invention, the transaction organization module is specifically configured to:
and grouping and filtering the abnormal event table and the context event table by using one or more of a time correlation method, a BOM correlation method and a PI D correlation method to obtain a to-be-mined transaction table.
In an optional embodiment of the present invention, the mining module is specifically configured to:
and processing the transaction table to be mined by utilizing any one mining mode of the frequent mode, the frequent sequence mode with time intervals or the frequent sequence mode without time intervals to obtain a mining result.
In an optional embodiment of the present invention, the mining module is specifically configured to:
and processing the transaction table to be mined by adopting any one of an FP-Growth algorithm, a BIDE algorithm or an EMEMISP algorithm to obtain a mining result.
In an optional embodiment of the invention, the system further comprises:
and the sending module is used for sending the mining result to the terminal.
The terminal can be a fixed terminal or a mobile terminal, so that personnel in charge of monitoring equipment faults can check and receive the excavation result in time, fault diagnosis and troubleshooting are carried out in time, and production and personnel safety are improved.
It should be noted that this system is a system corresponding to the method described in fig. 1, and all implementations of the illustrated method are applicable to the embodiment of this system, and the same technical effects can be achieved.
As shown in fig. 4, a specific workflow of the method for mining association between an abnormal event and a context event thereof in this embodiment is as follows:
industrial abnormal events, such as equipment failure/downtime events, can be obtained from a fault event list derived from a previous monitoring system and fault records of operation and inspection personnel; if the equipment is in the sub-health state, judging the abnormal state of the equipment according to the historical data according to the rule, such as the operation of the wind turbine generator by reducing the output; such as abnormal sequence patterns in industrial timing sequences, such as sudden pressure rise, sudden flow reduction, and sine signal wave-lacking heads.
The context event associated with the abnormal event is an event before or after the abnormal event occurs, such as an operation and maintenance event: replacement of spare parts, and routine maintenance, such as water dilution operation of a coolant of a nuclear power main pump; an operation event: starting and stopping the unit, limiting the power and coping with abnormal operation; environmental events: weather time (strong wind, rainfall); a symptom event: the variation trend or the mode is analyzed from the time sequence, such as the vibration pass frequency value is slowly increased; the axis locus is in a 8 shape (see figure 5); sensor abnormality, etc. The invention aims to precipitate and even supplement the experience by recording, integrating and mining historical operation and maintenance events, operation records, abnormal event records and the like, thereby becoming an auxiliary tool for fault diagnosis and troubleshooting. The combination mode of mining abnormal events, such as fault chain, A, B, C always occurs together and in sequence. Association of exceptional events with context events: mining an abnormal symptom event caused by abnormality or an influence event generated after abnormality; the former is like vibration abnormality before gearbox bearing trouble, and then the unusual noise of hearing of temperature anomaly simultaneously appears, and the latter is like the coal pulverizer stifled mill risk, adopts to reduce the coal feeding, increases the wind operation once. Association between contexts: and before the replacement of spare parts of the wind power, performing shutdown operation and maintenance operation. Taking the # 1 device as an example, the context event table of the # 1 device is shown in table 6, the abnormal event table is shown in table 7, the to-be-mined transaction table is shown in table 8, and the filtered to-be-mined transaction table is shown in table 9:
table 61 # context event table for device
TABLE 71 abnormal event Table for # devices
Table 81 # device to mine transaction table
| Transaction ID | Context events | Representing a |
|
| 1 | Operation of changing water | a | |
| 1 | Rapid rise of seal | b | |
| 1 | Slow decrease of seal | c | |
| 1 | Upper charge flow rate slowly rising | d | |
| 2 | Operation of changing water | a | |
| 2 | Rapid rise of seal leakage flow | b |
Filtered to-be-mined transaction table for table 91 # device
| Transaction ID | Context event item set |
| 1 | a,c,d,b |
| 2 | a,b |
The method comprises the steps of setting parameters of MinSup-minimum support index, minimum frequency of occurrence of a result item set in a transaction, Min L en-minimum number of event items in a result event item set, obtaining a filtered transaction table to be mined, then mining results by adopting frequent pattern mining, wherein each event is treated independently without considering the occurrence sequence of context events, mining high-frequency abnormal event combinations in abnormal event types, and mining results by adopting an FP-Growth algorithm in frequent pattern mining, or mining results by adopting frequent sequence mining, wherein the mining results can be regarded as a sequence with sequence by considering the occurrence sequence of the context events, a BIDE algorithm is adopted in frequent pattern mining, mining results are obtained by adopting frequent sequence mining with time intervals, namely the occurrence time intervals and the mutual relations of the context events are considered, the frequent sequence mining with the time intervals adopts an EMISP algorithm, the modes can be selected according to the actual needs, and the frequent pattern mining is taken as an example, the parameters are set as MinSup-minimum support index, the occurrence frequency of the Min 2 in the Sup table 10 is represented as 2, and at least 352 in the Min 2 table, and the Min 2 represents at least one line of each event in the Min 2.
Table 10 frequent pattern mining table
| Transaction ID | Context event item set |
| 1 | a,b,c,d |
| 2 | d,b |
| 3 | a,b,c |
| 4 | b,c,d |
TABLE 11 frequent pattern mining results table
| Degree of support | Context event item set |
| 3 | b,c |
| 3 | b,d |
| 2 | a,b,c |
| 2 | b,c,d |
| 2 | c,d |
According to the abnormal event and the correlation mining method and system for the context event, the abnormal event can be monitored/mined according to the context event correlated with the industrial abnormal event, wherein the abnormal event comprises a fault record and an abnormal event record; context events encompass: operation and maintenance events, operational events, environmental events, symptom events, etc.; the method can perform time correlation, BOM table correlation and PID graph correlation on input abnormal events and context events to generate an abnormal event list to be mined; supporting frequent pattern mining of event sequences considering event time intervals, wherein two events comprise 13 relations as shown in fig. 6, the mining results of such events are exemplified as follows:
1. exceptional event is associated with exceptional event
Coal mill abnormal blocked grinding before thermal power generating unit reduced power operation abnormity
Abnormal coal clogging of coal mill
Abnormal operation of thermal power generating unit under reduced power
2. Operation versus symptom time
Contains for unit overhaul operation (abnormal noise operation before pump shaft centering maintenance operation before finding abnormal centering)
3. Relationship between symptoms
Abnormal wear anomaly of meets bearing due to bearing vibration rising finish bearing temperature rising
The horizontal line in the above represents the time progression of the event.
Meanwhile, the digging can be carried out without considering the time interval or the sequence of the time; frequent pattern mining of events between industrial exceptions and exceptions, between exceptions and context events, and context is supported.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. An association mining method for abnormal events and context events thereof is characterized by comprising the following steps:
s1, acquiring an abnormal event table and a context event table;
s2, obtaining a to-be-mined transaction table according to the abnormal event table and the context event table;
s3, obtaining a mining result according to the transaction table to be mined, wherein the mining result is as follows: at least one of a context event most relevant to the abnormal event or a combination of high frequency occurring faults or a combination of high frequency occurring context events;
and S4, displaying the digging result.
2. The method for mining association between abnormal events and context events thereof according to claim 1, wherein step S2 specifically includes:
and grouping and filtering the abnormal event table and the context event table by using one or more of a time correlation method, a BOM correlation method and a PID correlation method to obtain a to-be-mined transaction table.
3. The method for mining association between abnormal events and context events thereof according to claim 2, wherein step S3 specifically includes:
and processing the transaction table to be mined by utilizing any one mining mode of the frequent mode, the frequent sequence mode with time intervals or the frequent sequence mode without time intervals to obtain a mining result.
4. The method for mining association between abnormal events and context events thereof according to claim 3, wherein step S3 specifically includes:
and processing the transaction table to be mined by adopting any one of an FP-Growth algorithm, a BIDE algorithm or an EMEMISP algorithm to obtain a mining result.
5. The method of claim 4, further comprising the steps of:
and S5, sending the mining result to the terminal.
6. An association mining system for abnormal events and context events thereof, comprising:
the acquisition module is used for acquiring the abnormal event table and the context event table;
the transaction organization module is used for obtaining a transaction table to be mined according to the abnormal event table and the context event table;
the mining module is used for obtaining a mining result according to the transaction table to be mined, wherein the mining result is as follows: at least one of a context event most relevant to the abnormal event or a combination of high frequency occurring faults or a combination of high frequency occurring context events;
and the display module is used for displaying the excavation result.
7. The system of claim 6, wherein the transaction organization module is specifically configured to:
and grouping and filtering the abnormal event table and the context event table by using one or more of a time correlation method, a BOM correlation method and a PID correlation method to obtain a to-be-mined transaction table.
8. The system of claim 7, wherein the mining module is specifically configured to:
and processing the transaction table to be mined by utilizing any one mining mode of the frequent mode, the frequent sequence mode with time intervals or the frequent sequence mode without time intervals to obtain a mining result.
9. The system of claim 8, wherein the mining module is specifically configured to:
and processing the transaction table to be mined by adopting any one of an FP-Growth algorithm, a BIDE algorithm or an EMEMISP algorithm to obtain a mining result.
10. The system of claim 9, further comprising:
and the sending module is used for sending the mining result to the terminal.
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| WO2022032695A1 (en) * | 2020-08-14 | 2022-02-17 | Cisco Technology, Inc. | Advanced policy driven context aware packet capture and analysis |
| CN112257423A (en) * | 2020-10-21 | 2021-01-22 | 北京工业大数据创新中心有限公司 | Equipment symptom information acquisition method and device and equipment operation and maintenance system |
| CN112257423B (en) * | 2020-10-21 | 2024-01-26 | 北京工业大数据创新中心有限公司 | Equipment symptom information acquisition method and device and equipment operation and maintenance system |
| CN114356642A (en) * | 2022-03-11 | 2022-04-15 | 军事科学院系统工程研究院网络信息研究所 | Abnormal event automatic diagnosis method and system based on process mining |
| CN114356642B (en) * | 2022-03-11 | 2022-05-17 | 军事科学院系统工程研究院网络信息研究所 | Abnormal event automatic diagnosis method and system based on process mining |
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