CN111784064A - Power plant equipment intelligent prediction maintenance method and system based on big data - Google Patents
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Abstract
The invention discloses a big data-based intelligent prediction maintenance method for power plant equipment, which comprises the following steps: step S1: building a fault search engine by using a computing mechanism; step S2: establishing a fault knowledge base and a corresponding maintenance method knowledge base in a fault search engine; step S3: inputting fault information in a fault search engine; step S4: the fault knowledge base screens out effective information according to the input fault information; step S5: and after the screening is finished, obtaining the corresponding maintenance method in the maintenance method knowledge base.
Description
Technical Field
The invention relates to the field of big data intelligent maintenance, in particular to a power plant equipment intelligent prediction maintenance method and system based on big data.
Background
The expert system of the power plant is a computer program expert system which can solve difficult problems in the field with human expert level in a specific field, and can integrate the extensive experience of experts and the special knowledge of the problem to be processed to form a knowledge point, so that people who are not familiar with a certain specialty can obtain the capability of reasoning through the expert system, obtain the required knowledge and can solve the problem like the experts or perform the work similar to the experts.
Modern power plant equipment and control systems are more and more complex, the requirement on maintenance personnel is high, the training period of qualified maintenance personnel is obviously prolonged, but the mobility of maintenance personnel of enterprises is high, some precious maintenance experiences cannot be stored along with the departure or retirement of the maintenance personnel, and the knowledge is greatly wasted, so that a system which is specially used for collecting the maintenance knowledge of power plant equipment maintenance experience storage equipment is established, the training and training of the power plant equipment maintenance personnel are promoted, and the powerful technical support is provided for the maintenance work of the power plant equipment.
The expert system is also an expert consulting system, is an intelligent computer system with a great amount of professional knowledge and experience, generally mainly refers to a software system, and the modern expert system is combined with a big data online equipment state analysis system to form an intelligent expert maintenance system of an industrial enterprise. The method organizes and stores the knowledge and thinking of human experts in the special field into a computer, so that the thinking process of the field experts can be simulated, and the computer can intelligently solve the actual problems like the human experts.
Intelligent maintenance that is initiated before a failure of a device or system is called predictive maintenance, which is the highest level of equipment repair. Advanced techniques and analytical models allow operators to detect complex patterns and predict unplanned events, and predictive maintenance techniques must be based on large amounts of heterogeneous data and reliable expert maintenance systems in order to achieve effective maintenance.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a power plant equipment intelligent prediction maintenance method based on big data, which is used for solving the technical problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
a power plant equipment intelligent prediction overhaul method based on big data comprises the following steps:
step S1: building a fault search engine by using a computing mechanism;
step S2: establishing a fault knowledge base and a corresponding maintenance method knowledge base in a fault search engine;
step S3: inputting fault information in a fault search engine;
step S4: the fault knowledge base screens out effective information according to the input fault information;
step S5: and after the screening is finished, obtaining the corresponding maintenance method in the maintenance method knowledge base.
According to the technical scheme, the method provided by the invention has the advantages that the fault search engine is constructed, the fault knowledge base and the corresponding maintenance method knowledge base are established in the search engine, a large amount of fault case information which is easy to appear in the power plant equipment is collected in the fault knowledge base, a large amount of power plant equipment maintenance methods are collected in the maintenance method knowledge base, and the corresponding maintenance method is retrieved through the fault case information in the fault knowledge base, so that the response can be quickly and accurately made to the equipment fault phenomenon, and powerful help can be provided for the training of maintenance personnel at ordinary times.
The invention is further configured to: in step S2, the fault knowledge base includes technical parameters of the unit equipment and manufacturer design rules.
Through the technical scheme, the fault problem which is easy to occur to the equipment can be quickly and accurately searched out by setting the technical parameters of the unit equipment and the design rules of manufacturers in the fault knowledge base.
The invention is further configured to: in step S2, the field test equipment fault data is extracted periodically and normalized to form a fault knowledge base.
By the technical scheme, the fault data of the field test equipment is periodically extracted and subjected to standardized processing, so that a systematic fault knowledge base is formed and the use is convenient.
The invention is further configured to: in step S2, the failed field test device is periodically overhauled, and the overhauling methods are integrated to form an overhauling method knowledge base.
By the technical scheme, the on-site test equipment with faults is periodically overhauled, and the overhauling method is comprehensively combined to form a systematic overhauling method knowledge base, so that the use is convenient.
The invention is further configured to: in step S3, the failure search engine searches for failure information in the form of keywords.
By the technical scheme, the search can be performed in the fault search engine in a keyword mode so as to optimize a quick search mode.
The invention is further configured to: in step S4, the effective information is sorted according to the similarity.
Through the technical scheme, the screened effective fault information is sorted according to the similarity, the fault condition of the equipment can be quickly and accurately searched, and the fault condition of the equipment is maintained.
The invention is further configured to: in step S5, the corresponding inspection methods obtained from the inspection method knowledge base are sorted according to the similarity.
Through the technical scheme, the retrieved corresponding maintenance methods are sequenced according to the similarity, so that the maintainers can quickly and accurately select the optimal maintenance method.
The invention also provides an intelligent prediction maintenance system of the intelligent prediction maintenance method of the power plant equipment based on the big data, which comprises the following steps: a fault identification part, a maintenance planning part, a data retrieval module, a sequencing module, a system maintenance module, a multimedia module, a help module and a network updating module, wherein,
the fault identification part comprises a fault knowledge base and a first data entry module, wherein the fault knowledge base is used for storing fault cases, and the first data entry module is used for entering data information of the fault cases into the fault knowledge base;
the maintenance planning part comprises a maintenance method knowledge base and a second data entry module, wherein the maintenance method knowledge base is used for storing the maintenance method, and the second data entry module is used for entering data information of the maintenance method into the maintenance method knowledge base;
the data retrieval module is used for inputting fault case information to retrieve a corresponding maintenance method;
the sorting module is used for sorting the input fault case information and the retrieved corresponding maintenance method according to the similarity respectively;
the system maintenance module comprises an equipment type information maintenance submodule, a unit information maintenance submodule, a user information maintenance submodule and a system data maintenance submodule, wherein the equipment type maintenance submodule is used for creating a logic grouping of equipment types, the unit information maintenance submodule is used for maintaining unit information of equipment case attributes, the user information maintenance submodule is used for maintaining account information of a system user, and the system data maintenance submodule is used for backing up and restoring system data;
the multimedia module is used for connecting field equipment maintenance and a system background through video equipment;
the help module is used for being connected with the system background through the display equipment;
the network updating module is used for updating the fault knowledge base and the maintenance method knowledge base on line and providing latest data support for maintenance personnel.
Through the technical scheme, the system can be used for maintaining, predicting and repairing the power plant equipment by collecting a large amount of theoretical knowledge about the maintenance of the power plant equipment and the maintenance experience of experts, can respond to the equipment failure phenomenon quickly and accurately, and can provide powerful technical assistance for the training of maintainers at ordinary times.
The invention is further configured to: the unit information comprises a sound parameter, a voltage parameter, a current parameter, an electric quantity parameter, a liquid level parameter, a temperature parameter, a vibration parameter, a swing parameter and a pressure flow parameter.
Through above-mentioned technical scheme, can monitor to a plurality of technical parameter of power plant equipment, when overhauing, the maintainer can carry out effectual retrieval maintenance according to power plant equipment's technical parameter.
In conclusion, the invention has the following beneficial effects:
(1) according to the method, a fault search engine is constructed, a fault knowledge base and a corresponding maintenance method knowledge base are established in the search engine, a large amount of fault case information which is easy to appear in the power plant equipment is collected in the fault knowledge base, a large amount of power plant equipment maintenance methods are collected in the maintenance method knowledge base, the corresponding maintenance method is retrieved through the fault case information in the fault knowledge base, response can be rapidly and accurately made to the equipment fault phenomenon, and powerful help can be provided for training of maintenance personnel at ordinary times;
(2) the method comprises the steps of periodically extracting fault data of field test equipment, carrying out standardized processing on the fault data to form a systematic fault knowledge base, and is convenient to use;
(3) the system can be used for maintaining, predicting and repairing the power plant equipment by collecting a large amount of theoretical knowledge and maintenance experience of experts about the maintenance of the power plant equipment, can quickly and accurately respond to the fault phenomenon of the equipment, can provide powerful technical assistance for the training of maintainers at ordinary times, can monitor a plurality of technical parameters of the power plant equipment, and can effectively retrieve and repair the power plant equipment according to the technical parameters of the power plant equipment during maintenance.
Drawings
FIG. 1 is a flow chart of a method for intelligent predictive overhaul of a power plant facility based on big data in accordance with the present invention;
FIG. 2 is a system block diagram of a big data based intelligent predictive overhaul system for power plant equipment according to the present invention.
Reference numerals: 1. a failure identification section; 11. a fault knowledge base; 12. a first data entry module; 2. a maintenance planning part; 21. a maintenance method knowledge base; 22. a second data entry module; 3. a data retrieval module; 4. a sorting module; 5. a system maintenance module; 51. a device type information maintenance submodule; 52. a unit information maintenance submodule; 53. a user information maintenance submodule; 54. a system data maintenance submodule; 6. a multimedia module; 7. a help module; 8. and a network updating module.
Detailed Description
In order to make the objects, technical effects and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The present invention will be described in detail below with reference to the accompanying drawings.
The electric power big data are holographic time scale measurement data related to power distribution network equipment, come from a time sequence database system, and have very high-efficient storage speed, query retrieval efficiency and data compression ratio aiming at a time sequence database system for processing massive, real-time and high-frequency acquisition data, so that the defects of a relational database can be effectively overcome, and the application requirements of massive concurrent data real-time processing in the fields of traditional industrial automation, emerging internet of things, cloud computing and the like are met.
Referring to fig. 1, a flowchart of a method for intelligent predictive maintenance of a power plant device based on big data according to the present invention is shown, where the present invention provides an intelligent predictive maintenance method of a power plant device based on big data, which may include the following steps:
step S1: building a fault search engine by using a computing mechanism; the fault search engine can be a bar search engine, so that the fault search engine is convenient for a maintainer to retrieve.
Step S2: establishing a fault knowledge base and a corresponding maintenance method knowledge base in a fault search engine;
in step S2, the failure knowledge base includes technical parameters of the unit equipment and manufacturer design rules, so that the failure problem that the equipment is prone to occur can be quickly and accurately retrieved by setting the technical parameters of the unit equipment and the manufacturer design rules in the failure knowledge base;
in step S2, the fault data of the field test device may be extracted periodically and standardized to form a fault knowledge base, so that the field test device with the fault may be periodically overhauled, and the overhaul method may be integrated to form a systematic overhaul method knowledge base, which is convenient to use;
in step S2, the failed field test device may be periodically overhauled, and the overhauling methods may be integrated to form an overhauling method knowledge base, so that the failed field test device may be periodically overhauled, and the overhauling methods may be integrated to form a systematic overhauling method knowledge base, which is convenient to use.
In the failure knowledge base, the capacity and level of the failure knowledge base are determined by the amount of knowledge, and the capacity and level of the failure knowledge base are still determined by the correctness and integrity of the knowledge, so that the failure knowledge base should have the capability of automatically correcting incorrect and incomplete knowledge, and knowledge refinement is an essential step in the knowledge acquisition process. Practice proves that the performance and the operation efficiency of the fault knowledge base can be obviously improved after the initial knowledge base is refined.
Step S3: inputting fault information in a fault search engine; in step S3, the fault information may be retrieved by keywords in the fault search engine, so as to optimize the quick retrieval method.
Step S4: the fault knowledge base screens out effective information according to the input fault information; in step S4, the screened valid information may be sorted according to the similarity, so that the fault condition of the device may be quickly and accurately retrieved, the fault condition of the device may be maintained, and the maintenance time of the maintenance personnel may be saved.
Step S5: after the screening is completed, the corresponding maintenance methods in the maintenance method knowledge base are obtained, wherein in step S5, the corresponding maintenance methods obtained from the maintenance method knowledge base can also be sorted according to the similarity, so that the maintainer can quickly and accurately select the best maintenance method to effectively maintain the equipment failure.
According to the technical scheme, the intelligent prediction maintenance method of the power plant equipment based on the big data comprises the steps of constructing a fault search engine, establishing a fault knowledge base and a corresponding maintenance method knowledge base in the search engine, collecting a large amount of fault case information which is easy to appear in the power plant equipment in the fault knowledge base, collecting a large amount of power plant equipment maintenance methods in the maintenance method knowledge base, retrieving the corresponding maintenance methods through the fault case information in the fault knowledge base, quickly and accurately responding to equipment fault phenomena, and providing powerful help for training maintenance personnel at ordinary times.
Referring to fig. 2, a system block diagram of a big data-based intelligent predictive maintenance system for power plant equipment according to the present invention is shown, and as shown in the figure, the present invention further provides a big data-based intelligent predictive maintenance system for power plant equipment, which may include a fault identification part 1, a maintenance planning part 2, a data retrieval module 3, a sorting module 4, a system maintenance module 5, a multimedia module 6, a help module 7, and a network update module 8.
The fault identification part 1 may include a fault knowledge base 11 and a first data entry module 12, wherein the fault knowledge base 11 may be used for storing fault cases, and the first data entry module 12 may be used for entering data information of the fault cases into the fault knowledge base 11.
The fault case data information in the fault knowledge base 11 is from fault case data information input by a maintainer when the system is in an initial state, the maintainer adds the fault case data information during the operation of the system, the maintainer deletes the fault case data information during the operation of the system, the maintainer adds a new reason during the operation of the system, and the system learns and corrects the fault case in the fault knowledge base 11 according to the fault case which occurs during the operation of the system.
The overhaul planning part 2 may include an overhaul method knowledge base 21 and a second data entry module 22, where the overhaul method knowledge base 21 may be used to store the overhaul method, and the second data entry module 22 may be used to enter data information of the overhaul method into the overhaul method knowledge base 21.
The data retrieval module 3 may be configured to input fault case information to retrieve a corresponding repair method.
The sorting module 4 may be configured to sort the input fault case information and the retrieved corresponding overhaul method according to the similarity.
The system maintenance module 5 may include an apparatus type information maintenance submodule 51, an entity information maintenance submodule 52, a user information maintenance submodule 53, and a system data maintenance submodule 54, where the apparatus type maintenance submodule 51 may be used to create a logical grouping of apparatus types, the entity information maintenance submodule 52 may be used to maintain entity information of apparatus instance attributes, the user information maintenance submodule 53 may be used to maintain system user account information, and the system data maintenance submodule 54 may be used to backup and restore system data.
Wherein, unit information can include sound parameter, voltage parameter, electric current parameter, electric quantity parameter, liquid level parameter, temperature parameter, vibration degree parameter, throw degree parameter and pressure flow parameter, borrow this, can monitor to a plurality of technical parameter of power plant equipment, when overhauing, the maintainer can carry out effectual retrieval maintenance according to power plant equipment's technical parameter.
The multimedia module 6 can be used for field device maintenance and system background connection through a video device.
The help module 7 may be used for background connection with the system via the display device.
The network update module 8 may be used to update the failure knowledge base 11 and the repair method knowledge base 21 online, so as to provide up-to-date data support for the repair staff.
In conclusion, the system can be used for maintaining, predicting and repairing the power plant equipment by collecting a large amount of theoretical knowledge about the maintenance of the power plant equipment and the maintenance experience of experts, can quickly and accurately respond to the equipment failure phenomenon, and can provide powerful technical help for the training of maintainers at ordinary times.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (9)
1. The intelligent prediction maintenance method of the power plant equipment based on big data is characterized by comprising the following steps:
step S1: building a fault search engine by using a computing mechanism;
step S2: establishing a fault knowledge base (11) and a corresponding overhauling method knowledge base (21) in a fault search engine;
step S3: inputting fault information in a fault search engine;
step S4: the fault knowledge base (11) screens out effective information according to the input fault information;
step S5: and after the screening is finished, obtaining the corresponding maintenance method in the maintenance method knowledge base (21).
2. The intelligent big data-based power plant equipment prediction overhaul method according to claim 1, wherein in step S2, the fault knowledge base (11) comprises technical parameters of the unit equipment and manufacturer design rules.
3. The intelligent prediction overhaul method for power plant equipment based on big data as claimed in claim 1, wherein in step S2, the field test equipment fault data is extracted according to a certain periodicity, and the fault data is standardized to form a fault knowledge base (11).
4. The intelligent big data-based power plant equipment troubleshooting method of claim 3 wherein in step S2 periodic troubleshooting is performed on the failed field test equipment and the troubleshooting methods are integrated to form a troubleshooting method knowledge base (21).
5. The intelligent big data-based power plant equipment troubleshooting method of claim 1 wherein in step S3, the fault information is retrieved in the form of a keyword in a fault search engine.
6. The intelligent big data-based power plant equipment overhaul method according to claim 5, wherein in step S4, the screened effective information is sorted according to the similarity.
7. The intelligent big data-based power plant equipment troubleshooting method of claim 6 wherein in step S5 the corresponding troubleshooting methods obtained from the troubleshooting method knowledge base (21) are ranked by similarity.
8. An intelligent predictive service system using the big data based intelligent predictive service method for power plant equipment according to any one of claims 1-7, comprising: a fault identification part (1), a maintenance planning part (2), a data retrieval module (3), a sequencing module (4), a system maintenance module (5), a multimedia module (6), a help module (7) and a network updating module (8), wherein,
the fault identification part (1) comprises a fault knowledge base (11) and a first data entry module (12), wherein the fault knowledge base (11) is used for storing fault cases, and the first data entry module (12) is used for entering data information of the fault cases into the fault knowledge base (11);
the overhaul planning part (2) comprises an overhaul method knowledge base (21) and a second data entry module (22), wherein the overhaul method knowledge base (21) is used for storing the overhaul method, and the second data entry module (22) is used for entering data information of the overhaul method into the overhaul method knowledge base (21);
the data retrieval module (3) is used for inputting fault case information to retrieve a corresponding maintenance method;
the sorting module (4) is used for sorting the input fault case information and the retrieved corresponding maintenance methods according to the similarity respectively;
the system maintenance module (5) comprises an equipment type information maintenance submodule (51), a unit information maintenance submodule (52), a user information maintenance submodule (53) and a system data maintenance submodule (54), wherein the equipment type maintenance submodule (51) is used for creating a logic group of equipment types, the unit information maintenance submodule (52) is used for maintaining unit information of equipment case attributes, the user information maintenance submodule (53) is used for maintaining account information of system users, and the system data maintenance submodule (54) is used for backing up and restoring system data;
the multimedia module (6) is used for connecting field equipment maintenance and system background through video equipment;
the help module (7) is used for connecting with the system background through a display device;
and the network updating module (8) is used for updating the fault knowledge base (11) and the overhaul method knowledge base (21) on line and providing latest data support for overhaul personnel.
9. The intelligent predictive service system of claim 8 wherein the unit of information includes a sound parameter, a voltage parameter, a current parameter, a power parameter, a liquid level parameter, a temperature parameter, a vibration parameter, a swing parameter, and a pressure flow parameter.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113392988A (en) * | 2021-05-10 | 2021-09-14 | 贵州乌江水电开发有限责任公司乌江渡发电厂 | Maintenance file management method for paperless operation of hydraulic power plant |
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| CN102765643A (en) * | 2012-05-31 | 2012-11-07 | 天津大学 | Elevator fault diagnosis and early-warning method based on data drive |
| CN103902731A (en) * | 2014-04-16 | 2014-07-02 | 国家电网公司 | Intelligent information maintenance method based on knowledge base inquiry |
| CN105302112A (en) * | 2015-10-23 | 2016-02-03 | 中国电子科技集团公司第十研究所 | Intelligent fault diagnosis system for ICNI system |
| CN105590146A (en) * | 2016-02-29 | 2016-05-18 | 上海带来科技有限公司 | Power plant device intelligent prediction overhaul method and power plant device intelligent prediction overhaul system based on big data |
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| CN113392988A (en) * | 2021-05-10 | 2021-09-14 | 贵州乌江水电开发有限责任公司乌江渡发电厂 | Maintenance file management method for paperless operation of hydraulic power plant |
| CN113392988B (en) * | 2021-05-10 | 2023-06-09 | 贵州乌江水电开发有限责任公司乌江渡发电厂 | Maintenance file management method for paperless operation of hydropower plant |
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