CN111768076A - Monitoring alarm signal clustering method taking power grid event as center - Google Patents
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Abstract
The invention discloses a method for clustering monitoring alarm signals by taking a power grid event as a center, which comprises the following steps of: step 1: extracting characteristic information to form a standard signal characteristic point mapping model based on a standard signal in the monitoring information specification; step 2: based on expert experience of the power system, a power grid incident model is constructed according to logic of manual analysis of power grid events; and step 3: performing event model deduction and improvement based on the historical power grid event analysis report, the historical monitoring data and the measurement data; and 4, step 4: carrying out structured analysis on the monitoring alarm information, and matching with a standard signal; and 5: performing event analysis by combining with the power grid topology and the operation mode, and performing multi-dimensional cross validation by combining with the remote measurement information; step 6: and clustering the monitoring alarm signals related to the event based on the analysis result of the event. The invention effectively reduces the processing and confirming time of the monitoring alarm signal, lightens the working pressure of operators and improves the monitoring management level of the power grid.
Description
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to an intelligent monitoring and analyzing technology for power grid events.
Background
With the popularization of the rapid development and regulation and control integrated mode of the power grid in China and the gradual integration of large operation and large maintenance systems, the dispatching mechanism increases the function of centralized monitoring of equipment, the production command center of the power grid dispatching center has more prominent effect, and higher requirements are provided for the quality of relevant data of power grid monitoring. The number of the transformer substations which are monitored in a centralized manner by the control center is increased sharply, the monitoring signals of the primary equipment and the secondary equipment are monitored in a centralized manner by the dispatching master station, the number of the monitoring alarm signals generated in a single day reaches ten thousand levels, and the signals are out of standard and not standardized due to different professional levels and different local experience of maintainers. In the face of massive signals, the judgment is completely carried out by depending on manual experience, the problem that the alarm information cannot be effectively monitored exists, and the perception key information of the power grid event can be missed due to omission. In recent years, artificial intelligence theories and technologies such as an expert system and natural language processing in the field of artificial intelligence are increasingly mature, and technical feasibility is provided for building intelligent perception of the operation state of a power grid and autonomously analyzing power grid events.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a method for clustering monitoring alarm signals by taking a power grid event as a center, so that the processing and confirming time of the monitoring alarm signals is shortened, the working pressure of operators is relieved, and the monitoring management level of a power grid is improved.
In order to solve the technical problems, the invention adopts the following technical scheme: a method for clustering monitoring alarm signals by taking a power grid event as a center comprises the following steps:
step 1: extracting characteristic information to form a standard signal characteristic point mapping model based on a standard signal in the monitoring information specification;
step 2: based on expert experience of the power system, a power grid incident model is constructed according to logic of manual analysis of power grid events;
and step 3: performing event model deduction and improvement based on the historical power grid event analysis report, the historical monitoring data and the measurement data;
and 4, step 4: carrying out structured analysis on the monitoring alarm information, and matching with a standard signal;
and 5: performing event analysis by combining with the power grid topology and the operation mode, and performing multi-dimensional cross validation by combining with the remote measurement information;
step 6: and clustering the monitoring alarm signals related to the event based on the analysis result of the event.
Preferably, the implementation method of step 1 includes the following steps:
(1) extracting different feature points to make a mapping table according to standard signals in the centralized monitoring information specification, and establishing an alarm signal matching model;
(2) and adjusting the weight of the feature points, and defining priority occupancy logic based on signal confidence.
Preferably, the implementation method of step 2 includes the following steps:
(1) analyzing reasons for sending the monitoring signals based on scheduling rules and artificial experiences by combining power grid operation data and various factors influencing the power grid;
(2) analyzing the generation source of the monitoring signal, establishing an eventing model of each event, distinguishing different wiring modes and operation modes, and establishing a standard event model library matched with different feature point modes;
(3) extracting displacement information in the remote signaling information aiming at a maintenance debugging event, analyzing the operation mode by combining with the power grid topology, identifying equipment at a maintenance window, and extracting and filtering maintenance debugging signals;
(4) aiming at a fault trip event, searching for opening information of relevant equipment in an effective time interval by taking a trip outlet signal of each equipment as an analysis starting point; after the effective tripping event is confirmed, further searching for a tripping related signal, and analyzing and deducing a fault tripping process;
(5) aiming at the power grid operation associated signals, extracting switching-on and switching-off information of a switch, a disconnecting link and a grounding switch, identifying the associated signals related to each operation by using an associated signal rule base, merging operation events according to main equipment related to each operation minor event, and checking information by using an operation ticket and an overhaul application;
(6) and aiming at an AVC control event, the integral judgment is carried out by combining the switch on-off operation related to the voltage regulating equipment and combining remote control operation information and AVC operation records according to the power grid topology.
Preferably, the implementation method of step 3 includes the following steps:
(1) performing big data calculation on a scene event set of each factor and result which may appear in the power grid by using an equivalent modeling technology, and screening a combination with strong relevance and high confidence coefficient to form a knowledge base;
(2) and (4) summarizing, verifying and promoting the standard event model library based on the monitoring situation model library, and perfecting the rules and characteristic points of the power grid events under various voltage levels, wiring modes and operation modes.
Preferably, the implementation method of step 4 includes the following steps:
(1) carrying out structural analysis on the monitoring alarm signal, identifying the alarm signal and carrying out information stripping, wherein the stripped information comprises a station, a voltage grade, a main equipment name, a spacing number, a set number and a protection model;
(2) and reserving the alarm signals after the structural grid model description is removed, and acquiring the standard signals corresponding to the alarm signals based on the standard signal characteristic point mapping model.
Preferably, the implementation method of step 5 includes the following steps:
(1) based on the analysis result of the monitoring alarm signal, the operation state of the associated equipment is judged by combining the operation mode analysis of the power grid topology;
(2) carrying out state superposition of remote signaling alarm signal equipment displacement information on a periodically-butted synchronous historical state section;
(3) performing cross identification confirmation on the running state of the equipment by combining the telemetering information;
(4) and performing event analysis and identification on the power grid events in a layered manner by using a standard event model library according to the sequence of overhaul debugging, accident tripping, operation association and AVC control events.
Preferably, the implementation method of step 6 includes the following steps:
(1) for each power grid event, clustering signals related to the event by taking key signals of the event as clustering starting points and combining judgment standard factors in clustering analysis, wherein the judgment standard factors comprise signal generation time, topological relation of equipment to which the signals belong and association degree of the signals and the starting signals;
(2) and (4) aiming at the monitoring alarm signals clustered and collected to a certain power grid event, the monitoring alarm signals are not repeatedly analyzed or classified and removed from the original alarm information record.
Based on an expert system and a natural language processing technology in the field of artificial intelligence, the invention presents massive and disordered monitoring signals in an event form by performing structured analysis and cross identification and cleaning on monitoring related data, and is more concise and convenient for operation management of operators in the form of organized events, so that the operators concentrate attention on a small amount of effective alarms really feeding back equipment problems, the processing and confirming time of the monitoring alarm signals is effectively reduced, the working pressure of the operators is reduced, and the monitoring management level of a power grid is improved.
The following detailed description of the present invention will be provided in conjunction with the accompanying drawings.
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The invention is further described with reference to the accompanying drawings and the detailed description below:
FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a method for clustering monitoring alarm signals centered on a grid event includes the following steps:
step 1: extracting characteristic information to form a standard signal characteristic point mapping model based on a standard signal in the monitoring information specification;
step 2: based on expert experience of the power system, a power grid incident model is constructed according to logic of manual analysis of power grid events;
and step 3: performing event model deduction and improvement based on the historical power grid event analysis report, the historical monitoring data and the measurement data;
and 4, step 4: carrying out structured analysis on the monitoring alarm information, and matching with a standard signal;
and 5: performing event analysis by combining with the power grid topology and the operation mode, and performing multi-dimensional cross validation by combining with the remote measurement information;
step 6: and clustering the monitoring alarm signals related to the event based on the analysis result of the event.
The specific implementation method of the step 1 comprises the following steps:
according to standard signals in national network company enterprise standard Q/GDW 11398 and 2015 Transformer substation equipment monitoring information Specification, equipment types, equipment numbers, voltage levels, interval types, equipment actions and the like are extracted as feature points, and the feature points mainly select equipment information of equipment which sends signals and operation contents corresponding to alarms, wherein the equipment information is necessary for understanding signal definitions. For a standard signal 'switch oil pressure low opening and closing total locking', the classification equipment type is a switch or a hydraulic mechanism, and the characteristic points are 'oil pressure', 'locking', 'opening/closing', 'total locking'. The subsequent matching standard signals are classified according to intervals and equipment, feature points are used as matching core contents, matching rules are predefined to make a mapping table, and an alarm signal matching model is established, wherein the mapping table is mapped to the main transformer high-voltage side such as the main transformer high-voltage side, the main transformer variable height side and the main variable height side.
And according to the signal priority occupation with high information confidence, forming mapping of an actual monitoring information point table and a standard signal point table, adjusting the weight of the feature points by combining the experience of expert personnel, and defining priority occupation logic based on the signal confidence.
The specific implementation method of the step 2 comprises the following steps:
based on scheduling rules and artificial experiences, the reasons for sending the monitoring signals are analyzed by combining the alarm signals, load information, operation conditions, environmental factors and other electric network operation data and various factors influencing the electric network.
Analyzing the generation source of the monitoring signal, establishing an eventing model of various events such as overhaul debugging, fault tripping, operation companion and the like, distinguishing different wiring modes and operation modes, and establishing a standard event model library matched with different characteristic point modes.
And extracting displacement information in the remote signaling information aiming at the maintenance debugging event, analyzing the operation mode by combining with the power grid topology, identifying equipment at a maintenance window, and extracting and filtering maintenance debugging signals.
Aiming at a fault trip event, searching for opening information of relevant equipment in an effective time interval by taking a trip outlet signal of each equipment as an analysis starting point; after the effective tripping event is confirmed, signals of a reclosing exit, an interval accident total, a total station accident total, closing and the like are further searched, and the fault tripping process is analyzed and deduced.
Aiming at the power grid operation accompanying signals, based on the associated equipment, equipment topology, power grid operation mode and signal meaning of the monitoring signals, in combination with the action time and reset time of the signals, establishing an accompanying signal rule base, extracting switching-on and switching-off information of a switch, a disconnecting link and a grounding switch, identifying the associated signals related to each operation by using the accompanying signal rule base, and merging the operation events according to the main equipment associated with each operation minor event.
And aiming at an AVC control event, the integral judgment is carried out by combining the switch on-off operation related to voltage regulating equipment such as a capacitor and an electric reactor and combining remote control operation information and AVC operation records according to the power grid topology.
The specific implementation method of the step 3 is as follows:
the method comprises the steps of selecting historical monitoring alarm data and historical logs in a past period of time to test by using an equivalent modeling correlation technology, carrying out big data calculation on a situation event set of various factors and results which may appear in a power grid if historical data of a certain area in the past year is used, carrying out event judgment according to a predefined event model which is possibly combined with alarm signals generated when an event occurs, comparing the event model with the historical logs to determine whether the judged events such as tripping, overhauling and AVC actions occur or not, calculating a reliability according to a real occurrence rate/the total number of the judged events as a basis, screening combinations of the event model, which are accurate, strong in relevance and high in confidence coefficient, and forming a knowledge base according to the judgment basis, thereby perfecting a standard event model base.
Based on events such as power grid tripping, power grid abnormal defects, equipment maintenance and debugging, automatic voltage regulation of AVC and the like generally concerned by equipment monitoring services, a monitoring scene model base is formed by combining service attention points, and the standard event model base is summarized, verified and promoted, so that rules and characteristic points of power grid events under various voltage levels, wiring modes and operation modes are perfected.
The specific implementation method of the step 4 comprises the following steps:
the method comprises the steps of carrying out structural analysis on a monitoring alarm signal, identifying information such as station, voltage grade, main equipment name, interval number, set, protection model and the like in the alarm signal, and stripping, wherein the information comprises 'XX substation 1# to A phase body air-cooled control loop fault alarm', and stripping interference information to remove certain station information and certain equipment information due to specific sources of signals and leave core information of 'body air-cooled control loop fault alarm' representing the real definition of the signals, wherein the core information is irrelevant to specific individual equipment and can represent the meaning of the signals.
And reserving the alarm signal after the structural grid model description is removed, and acquiring a standard signal corresponding to the alarm signal based on a standard signal characteristic point mapping model, for example, the standard signal corresponding to the main transformer body air cooling control loop fault.
The specific implementation method of the step 5 is as follows:
the same alarm signal has different meanings in different operation states, so that the operation state of associated equipment (for example, isolating switches on two sides of a circuit breaker can be obtained according to a primary wiring diagram) is judged by combining the operation mode analysis of power grid topology based on the analysis result of the monitoring alarm signal, and the state superposition of displacement information of remote signaling alarm signal equipment is carried out on a butt joint synchronous historical state section, so that the initial section needs to be regularly synchronized due to the possible problems of missed sending and mistaken sending of remote signaling.
For the equipment state in the power grid production management system, there is also a possibility of error, and it is necessary to combine the telemetering information to perform cross identification and confirmation on the equipment operation state, for example, a switch in an off state, the current, active power and idle power of which should be 0, and by checking the telemetering information, the reliability of equipment state judgment can be greatly increased.
And performing event analysis and identification on the power grid events in a hierarchical manner by using a standard event model library according to the sequence of overhaul debugging, accident tripping, operation association and AVC control events, and removing the identified alarm signals from an analysis table to avoid influencing subsequent statistical analysis.
The specific implementation method of the step 6 comprises the following steps:
for each power grid event, the key signals of the event analysis are taken as clustering starting points, for example, for a trip event, signals with 'control loop disconnection' can be often taken as judging starting points of the occurrence of a suspected trip event, the eventing models of different types of events are different, so that the starting signals are different and need to be selected according to the eventing models, and the monitoring alarm signals related to the events are clustered by taking the comprehensive factors such as the signal occurrence time, the topological relation of equipment to which the signals belong, the association degree of the signals and the starting signals and the like as judging standards in the clustering analysis.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that the invention is not limited thereto, and may be embodied in other forms without departing from the spirit or essential characteristics thereof. Any modification which does not depart from the functional and structural principles of the present invention is intended to be included within the scope of the claims.
Claims (7)
1. A method for clustering monitoring alarm signals by taking a power grid event as a center is characterized by comprising the following steps:
step 1: extracting characteristic information to form a standard signal characteristic point mapping model based on a standard signal in the monitoring information specification;
step 2: based on expert experience of the power system, a power grid incident model is constructed according to logic of manual analysis of power grid events;
and step 3: performing event model deduction and improvement based on the historical power grid event analysis report, the historical monitoring data and the measurement data;
and 4, step 4: carrying out structured analysis on the monitoring alarm information, and matching with a standard signal;
and 5: performing event analysis by combining with the power grid topology and the operation mode, and performing multi-dimensional cross validation by combining with the remote measurement information;
step 6: and clustering the monitoring alarm signals related to the event based on the analysis result of the event.
2. The method for clustering monitoring alarm signals centered on grid events according to claim 1, wherein: the implementation method of the step 1 comprises the following steps:
(1) extracting different feature points to make a mapping table according to standard signals in the centralized monitoring information specification, and establishing an alarm signal matching model;
(2) and adjusting the weight of the feature points, and defining priority occupancy logic based on signal confidence.
3. The method for clustering monitoring alarm signals centered on grid events according to claim 1, wherein: the implementation method of the step 2 comprises the following steps:
(1) analyzing reasons for sending the monitoring signals based on scheduling rules and artificial experiences by combining power grid operation data and various factors influencing the power grid;
(2) analyzing the generation source of the monitoring signal, establishing an eventing model of each event, distinguishing different wiring modes and operation modes, and establishing a standard event model library matched with different feature point modes;
(3) extracting displacement information in the remote signaling information aiming at a maintenance debugging event, analyzing the operation mode by combining with the power grid topology, identifying equipment at a maintenance window, and extracting and filtering maintenance debugging signals;
(4) aiming at a fault trip event, searching for opening information of relevant equipment in an effective time interval by taking a trip outlet signal of each equipment as an analysis starting point; after the effective tripping event is confirmed, further searching for a tripping related signal, and analyzing and deducing a fault tripping process;
(5) aiming at the power grid operation associated signals, extracting switching-on and switching-off information of a switch, a disconnecting link and a grounding switch, identifying the associated signals related to each operation by using an associated signal rule base, merging operation events according to main equipment related to each operation minor event, and checking information by using an operation ticket and an overhaul application;
(6) and aiming at an AVC control event, the integral judgment is carried out by combining the switch on-off operation related to the voltage regulating equipment and combining remote control operation information and AVC operation records according to the power grid topology.
4. The method for clustering monitoring alarm signals centered on grid events according to claim 1, wherein: the implementation method of the step 3 comprises the following steps:
(1) performing big data calculation on a scene event set of each factor and result which may appear in the power grid by using an equivalent modeling technology, and screening a combination with strong relevance and high confidence coefficient to form a knowledge base;
(2) and (4) summarizing, verifying and promoting the standard event model library based on the monitoring situation model library, and perfecting the rules and characteristic points of the power grid events under various voltage levels, wiring modes and operation modes.
5. The method for clustering monitoring alarm signals centered on grid events according to claim 4, wherein: the implementation method of the step 4 comprises the following steps:
(1) carrying out structural analysis on the monitoring alarm signal, identifying the alarm signal and carrying out information stripping, wherein the stripped information comprises a station, a voltage grade, a main equipment name, a spacing number, a set number and a protection model;
(2) and reserving the alarm signals after the structural grid model description is removed, and acquiring the standard signals corresponding to the alarm signals based on the standard signal characteristic point mapping model.
6. The method for clustering monitoring alarm signals centered on grid events according to claim 5, wherein: the implementation method of the step 5 comprises the following steps:
(1) based on the analysis result of the monitoring alarm signal, the operation state of the associated equipment is judged by combining the operation mode analysis of the power grid topology;
(2) carrying out state superposition of remote signaling alarm signal equipment displacement information on a periodically-butted synchronous historical state section;
(3) performing cross identification confirmation on the running state of the equipment by combining the telemetering information;
(4) and performing event analysis and identification on the power grid events in a layered manner by using a standard event model library according to the sequence of overhaul debugging, accident tripping, operation association and AVC control events.
7. The method of claim 6 for clustering grid event-centric monitoring alarm signals, wherein: the implementation method of the step 6 comprises the following steps:
(1) for each power grid event, clustering signals related to the event by taking key signals of the event as clustering starting points and combining judgment standard factors in clustering analysis, wherein the judgment standard factors comprise signal generation time, topological relation of equipment to which the signals belong and association degree of the signals and the starting signals;
(2) and (4) aiming at the monitoring alarm signals clustered and collected to a certain power grid event, the monitoring alarm signals are not repeatedly analyzed or classified and removed from the original alarm information record.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112580993A (en) * | 2020-12-23 | 2021-03-30 | 云南电网有限责任公司昆明供电局 | Power grid equipment fault probability analysis method |
| CN112906389A (en) * | 2021-02-04 | 2021-06-04 | 云南电网有限责任公司昆明供电局 | Fault trip discrimination method based on multi-dimensional data analysis |
| CN113065978A (en) * | 2021-03-15 | 2021-07-02 | 国网江苏省电力有限公司南通供电分公司 | Analysis method for monitoring information defect event |
| CN113097981A (en) * | 2021-03-15 | 2021-07-09 | 国网江苏省电力有限公司南通供电分公司 | Method for judging missing of monitoring alarm signal of transformer substation |
| CN113268590A (en) * | 2021-04-06 | 2021-08-17 | 云南电网有限责任公司昆明供电局 | Power grid equipment running state evaluation method based on equipment portrait and integrated learning |
| CN113435332A (en) * | 2021-06-28 | 2021-09-24 | 国网福建省电力有限公司福州供电公司 | Method for generating irregular signal monitoring information event |
| CN113743717A (en) * | 2021-07-26 | 2021-12-03 | 南方电网深圳数字电网研究院有限公司 | Reminding method, equipment and storage medium based on classification technology |
| CN113962051A (en) * | 2021-09-27 | 2022-01-21 | 北京科东电力控制系统有限责任公司 | Electric power system main and auxiliary equipment event monitoring modeling method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120296607A1 (en) * | 2011-05-19 | 2012-11-22 | Accenture Global Services Limited | Intelligent grid communication network management system and methods |
| CN108847968A (en) * | 2018-06-04 | 2018-11-20 | 国网浙江省电力有限公司 | Monitoring accident, anomalous event identification and multidimensional analysis method |
| CN109657912A (en) * | 2018-11-15 | 2019-04-19 | 国网浙江省电力有限公司金华供电公司 | A kind of visual power grid risk management and control method and system |
-
2020
- 2020-05-28 CN CN202010465852.3A patent/CN111768076B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120296607A1 (en) * | 2011-05-19 | 2012-11-22 | Accenture Global Services Limited | Intelligent grid communication network management system and methods |
| CN108847968A (en) * | 2018-06-04 | 2018-11-20 | 国网浙江省电力有限公司 | Monitoring accident, anomalous event identification and multidimensional analysis method |
| CN109657912A (en) * | 2018-11-15 | 2019-04-19 | 国网浙江省电力有限公司金华供电公司 | A kind of visual power grid risk management and control method and system |
Non-Patent Citations (1)
| Title |
|---|
| 耿艳;崔慧军;于洋;张奇;: "基于辅助决策专家库的设备健康水平预测研究", 华北电力技术, no. 11 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112580993A (en) * | 2020-12-23 | 2021-03-30 | 云南电网有限责任公司昆明供电局 | Power grid equipment fault probability analysis method |
| CN112580993B (en) * | 2020-12-23 | 2022-08-26 | 云南电网有限责任公司昆明供电局 | Power grid equipment fault probability analysis method |
| CN112906389A (en) * | 2021-02-04 | 2021-06-04 | 云南电网有限责任公司昆明供电局 | Fault trip discrimination method based on multi-dimensional data analysis |
| CN112906389B (en) * | 2021-02-04 | 2022-08-26 | 云南电网有限责任公司昆明供电局 | Fault trip discrimination method based on multi-dimensional data analysis |
| CN113065978A (en) * | 2021-03-15 | 2021-07-02 | 国网江苏省电力有限公司南通供电分公司 | Analysis method for monitoring information defect event |
| CN113097981A (en) * | 2021-03-15 | 2021-07-09 | 国网江苏省电力有限公司南通供电分公司 | Method for judging missing of monitoring alarm signal of transformer substation |
| CN113268590A (en) * | 2021-04-06 | 2021-08-17 | 云南电网有限责任公司昆明供电局 | Power grid equipment running state evaluation method based on equipment portrait and integrated learning |
| CN113435332A (en) * | 2021-06-28 | 2021-09-24 | 国网福建省电力有限公司福州供电公司 | Method for generating irregular signal monitoring information event |
| CN113743717A (en) * | 2021-07-26 | 2021-12-03 | 南方电网深圳数字电网研究院有限公司 | Reminding method, equipment and storage medium based on classification technology |
| CN113962051A (en) * | 2021-09-27 | 2022-01-21 | 北京科东电力控制系统有限责任公司 | Electric power system main and auxiliary equipment event monitoring modeling method |
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