CN111080128A - Big data analysis and reliability evaluation management system for thermal power station metal equipment - Google Patents
Big data analysis and reliability evaluation management system for thermal power station metal equipment Download PDFInfo
- Publication number
- CN111080128A CN111080128A CN201911298681.3A CN201911298681A CN111080128A CN 111080128 A CN111080128 A CN 111080128A CN 201911298681 A CN201911298681 A CN 201911298681A CN 111080128 A CN111080128 A CN 111080128A
- Authority
- CN
- China
- Prior art keywords
- information
- equipment
- metal equipment
- thermal power
- analysis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000002184 metal Substances 0.000 title claims abstract description 74
- 238000011156 evaluation Methods 0.000 title claims abstract description 30
- 238000007405 data analysis Methods 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 claims abstract description 44
- 238000004458 analytical method Methods 0.000 claims abstract description 40
- 238000012545 processing Methods 0.000 claims abstract description 19
- 238000007726 management method Methods 0.000 claims description 47
- 238000007689 inspection Methods 0.000 claims description 33
- 230000008569 process Effects 0.000 claims description 29
- 238000013210 evaluation model Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000007621 cluster analysis Methods 0.000 claims description 6
- 238000009825 accumulation Methods 0.000 claims description 3
- 238000007418 data mining Methods 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 19
- 238000010438 heat treatment Methods 0.000 description 16
- 230000007547 defect Effects 0.000 description 12
- 238000012423 maintenance Methods 0.000 description 11
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 11
- 239000000463 material Substances 0.000 description 9
- 238000001514 detection method Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 8
- 238000011160 research Methods 0.000 description 7
- 238000012795 verification Methods 0.000 description 6
- 238000013461 design Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 5
- 238000009826 distribution Methods 0.000 description 4
- 238000009434 installation Methods 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 238000010248 power generation Methods 0.000 description 4
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000007639 printing Methods 0.000 description 3
- 238000010561 standard procedure Methods 0.000 description 3
- 238000012384 transportation and delivery Methods 0.000 description 3
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 2
- 239000003245 coal Substances 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000013523 data management Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000008570 general process Effects 0.000 description 2
- 229910052739 hydrogen Inorganic materials 0.000 description 2
- 239000001257 hydrogen Substances 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 238000005192 partition Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000003466 welding Methods 0.000 description 2
- 238000003326 Quality management system Methods 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 229910021529 ammonia Inorganic materials 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000013021 overheating Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention provides a thermal power station metal equipment big data analysis and reliability evaluation management system, which comprises: the system comprises a metal equipment database module, a metal equipment performance analysis module, an announcement module, a learning module, a permission management module and a document processing module. The method can record the full life cycle data of the equipment or the unit, provide contrast data for the equipment of the same type, improve the supervision technical capability and the technical service capability, accumulate big data and provide a data base for further big data mining and analysis.
Description
Technical Field
The invention belongs to the technical field of information management, and particularly relates to a thermal power station metal equipment big data analysis and reliability evaluation management system.
Background
Thermal power plant boilers, pressure vessels and pressure pipelines belong to special equipment safety supervision regulations, TSG 0001-2012 boiler safety technology supervision regulations, and TSG 21-2016 fixed pressure vessel safety technology supervision regulations, and belong to equipment related to life safety and high harmfulness. Meanwhile, standards such as TSG 21-2016 fixed pressure vessel safety technology supervision procedures, DL 612 plus 2017 power industrial boiler pressure vessel supervision procedures, DL 647 plus 2004 power station boiler pressure vessel supervision procedures, DL/T438 plus 2016 heat power plant metal technology supervision procedures and the like also provide strict requirements for the design, manufacture, installation, use, inspection, modification, repair and service life evaluation of the boiler, the pressure vessel, the four pipelines, the turbine metal equipment and the generator metal equipment, and carry out whole-process technical supervision management, find problems in time and adopt effective technical supervision measures, thereby reducing and avoiding the failure of metal supervised components in the processes.
The research result of the document 'computer management system for metal parts of power station' is to realize the management of the ledger computer for the metal supervision parts in a power plant in Shanxi province. It has great functional and functional limitations. A management system of the document "implementation of a plant metal technology supervision and management system based on the J2EE platform" was developed by the institute of electrical power testing, jiangsu. The system takes the equipment as a center, and makes a maintenance plan based on information such as a detection method, an acceptance standard, maintenance conditions and defect conditions of the equipment and the like; performing inspection under the guidance of an inspection plan, an inspection method and the like and forming an inspection report; forming an inspection opinion book aiming at the problems found in the inspection and automatically generating corresponding defect information; overhauling the equipment according to the defect information; the closed-loop processing of the equipment defects is realized by processing the inspection opinion books or eliminating the defects; and finally, making a scrapped record of the scrapped equipment. The system can perform comprehensive query in the information range of the power plant equipment, and does not have the function of retrieving the same type of equipment in all the data ranges of the power plants.
Although the research and application of the equipment management and defect management system are more in China, the research for comprehensively utilizing the information of the monitored components of the multiple power generation enterprises, applying the information to technical supervision and technical service work and guiding the overhaul and management of other power plant equipment by utilizing the analysis result of the comprehensive information is not available for multiple power generation enterprises. The researched literature shows that most of the developed information systems are developed based on the B/S system or based on the SQL database, the processing of unstructured data is difficult, and the retrieval or management efficiency cannot meet the requirement of the project. Therefore, there is an advancement and necessity to develop a Lucene-based metal supervision device information system.
Disclosure of Invention
The big data analysis and reliability evaluation management system for the metal equipment is based on national and industrial standards, supports equipment technical supervision data, inspection data generated in the overhaul process, failure analysis data, maintenance and replacement data and the like, and guides related technical supervision standards, supervision and evaluation methods, inspection and analysis methods, overhaul and inspection plans and quality acceptance standards to realize the management, retrieval and analysis of the whole life cycle information of the thermal power plant supervision equipment.
A thermal power station metal equipment big data analysis and reliability evaluation management system comprises:
the metal equipment database module is used for managing information related to all metal equipment related to the power station, wherein the information comprises basic information, process information and dynamic information of the metal equipment, supervision and inspection information of the metal equipment, instrument information, reports and report information;
the metal equipment performance analysis module retrieves all relevant operating parameters from the metal equipment database module according to the analyzed performance, analyzes the importance of different parameters through clustering calculation, obtains the influence degree by using a big data mode, and predicts the possible occurrence condition of the operating equipment through analysis in combination with the condition of the operating equipment;
the announcement module is used for announcing the latest information;
the learning module is used for the user to exchange learning and download related contents;
the authority management module is used for managing the authority of the user;
and the document processing module is used for processing the inquired document information.
Furthermore, the information recording mode in the metal equipment database module comprises CAD drawings, text documents, pictures, video data and scanning parts, and the retrieval of the contents can be realized.
Furthermore, the system can also realize the input, the retrieval, the presentation and the document processing of the related data information.
Furthermore, the metal equipment performance analysis module realizes the performance analysis of the heated surface pipe, the high-temperature pipeline and the high-temperature fastener.
Further, performing cluster analysis on all cases of failure analysis of the thermal power station metal equipment of the information input in the system to obtain an operation condition threshold value with high probability of failure, and performing early warning on the power plant when the operation parameter exceeds the threshold value; and meanwhile, with the accumulation of failure cases, the early warning threshold values of all the devices are automatically corrected and perfected.
And further, analyzing the possibility of failure of each equipment parameter by combining failure cases of each thermal power station metal equipment of the information recorded in the system, and giving an early warning to parts with the same or similar properties.
Further, the system can utilize the established life evaluation model to evaluate the life of the metal equipment; performing cluster analysis on the occurred failure cases, and correcting the existing service life evaluation model to obtain a service life evaluation model which is closer to the reality; and counting the service life conditions of the equipment under different operation conditions, and mastering the influence degree of the operation parameters on the service life of the equipment so as to correct the service life evaluation model.
Further, the analysis and statistics in the system may be presented as list entries and the location of the plant to which the equipment belongs is shown by a map.
Further, the system realizes comparison of the running information of the plurality of devices.
Further, the system utilizes a cloud server to manage information.
The invention has the beneficial effects that: the system utilizes the cloud server to perform data management, multitask and distributed operation, and adopts the content delivery network CDN to perform task deployment, so that the high efficiency of cloud service is fully embodied. The metal monitoring equipment information system of the thermal power plant can be applied to thermal power plant equipment management, new energy equipment management and power supply side equipment management, not only can record the full life cycle data of equipment or units, but also can provide comparison data for equipment of the same type, improve the monitoring technical capability and technical service capability, accumulate big data and provide a data basis for further big data mining and analysis. The metal supervision equipment information system of the power plant can manage and analyze technical supervision reports of the power plant. The system realizes online data sharing of monthly reports and quarterly reports, automatically records report data of individual power plants, can analyze quarterly and annual data, and fully excavates report data value. The power plant metal monitoring equipment information system has the functions of user role and authority management, learning material management and notice management. User role and authority management is the basic guarantee of system data safety, stability, timeliness and accuracy, learning management is mainly used for professional technical communication of individual users, and bulletin management is a window for issuing technical supervision reports, industry dynamic information and the like.
Drawings
FIG. 1 is a block diagram of a system of the present invention;
FIG. 2 is a schematic diagram of a general process for full-text retrieval of an information retrieval module in the system of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
The big data analysis and reliability evaluation management system for the metal equipment is based on national and industrial standards, supports equipment technical supervision data, inspection data generated in the overhaul process, failure analysis data, maintenance and replacement data and the like, and guides related technical supervision standards, supervision and evaluation methods, inspection and analysis methods, overhaul and inspection plans and quality acceptance standards to realize the management, retrieval, analysis and evaluation of the whole life cycle information of the supervision equipment of the thermal power plant.
The metal equipment of the power station comprises various types, for example, a thermal power station, and comprises a boiler body, a superheater system, steam turbine body equipment, a generator body, four pipelines, a machine pipeline, a furnace pipeline, a pressure vessel, a feed pump steam turbine body and the like. The boiler body includes: the system comprises a steam drum, a header, a desuperheater and a connecting pipeline in the range of a boiler (comprising a water wall system, a centralized downcomer, a water supply pipe of the steam drum or a steam-water separator, an economizer recycle, a water wall outlet pipe, a water wall inlet pipe and a boiler water pump outlet pipeline, wherein the superheater system comprises a saturated steam connecting pipe, a superheated steam connecting pipe, a reheater system connecting pipeline and the like). The steam turbine body equipment includes: the steam turbine comprises a steam turbine rotor (comprising a high-pressure rotor, a middle-pressure rotor, a low-pressure rotor and a middle shaft), a movable blade, a cylinder (a high-pressure inner cylinder, a low-pressure inner cylinder and a low-pressure outer cylinder), a steam chamber, a high-pressure steam guide pipe, a medium-pressure steam guide pipe, a high-pressure partition sleeve, a medium-pressure partition sleeve, a low-. The generator body includes: the generator comprises a generator large shaft, a protective ring, a fan blade, a generator bearing bush and a generator sealing bush. The four large pipes include: main steam pipeline and high-pressure side, reheater hot-section pipeline and low-side, reheater cold-section pipeline and high-side low-pressure side, and high-pressure water-feeding pipeline. The external pipeline of the machine and the furnace comprises: high-temperature and high-pressure steam-water pipelines outside the machine and the furnace, hydrogen pipelines and oil pipelines, high-temperature and high-pressure thermotechnical instrument tubes and the like, wherein the temperature is greater than or equal to 400 ℃ or the pressure is greater than or equal to 5.88 Mpa. The pressure vessel includes: a thermodynamic system pressure container, a hydrogen storage tank, a liquid ammonia storage tank, an ammonia buffer tank, a peripheral low-pressure container and the like. The feed pump turbine body includes: rotor, movable vane, cylinder and bolt. The management of the above metal equipment is a complex task.
As shown in fig. 1, the invention provides a main block diagram of a thermal power plant metal equipment big data analysis and reliability evaluation management system. The system of the invention utilizes the cloud server to manage data information, perform multitask and distributed operation, and adopts the content delivery network CDN to perform task deployment, thereby fully embodying the high efficiency of cloud service. The final application database of the system comprises data accumulated by more than 50 thermal power plants with 150 machine sets for decades. Therefore, the retrieval function of the information system is required to be very fast, efficient and accurate. The massive data information managed by the system can be used for related technical personnel to analyze data and provide reference for the good operation of equipment.
The invention discloses a thermal power station metal equipment big data analysis and reliability evaluation management system which comprises the following modules.
And the metal equipment database module is used for managing the metal equipment information of a plurality of units of the thermal power plant, establishing a basic information ledger of the power plant, and managing the process information generated by the metal equipment at each stage, including measurement data, equipment states and the like of overhaul and daily inspection.
The database comprises basic information, process information and dynamic information of the equipment, supervision information of the metal equipment, instrument and equipment information, report forms and the like. The module classifies the managed information by category.
The basic information of the equipment comprises basic information of unit design, installation and commissioning of all the equipment recorded by establishing a basic information ledger of the equipment, such as a boiler technical register book, a summary of calculation results of discharge amount of a boiler safety valve, a host machine use instruction and the like. And collecting intrinsic information of equipment brief introduction, operation temperature, pressure, material, specification, strength calculation, thermodynamic calculation, component structure and the like.
The equipment process information and dynamic information comprise process information (including equipment defect record, accident record, inspection record, replacement record, over-temperature record and over-pressure record, and service life evaluation based on wall thickness reduction) generated by equipment in each stage of design, manufacture, installation, overhaul, maintenance, technical transformation, replacement, service life evaluation and the like. Sources for recording this information are: design data, installation data, maintenance and inspection process data (such as a notice of defect inspection, welding process and heat treatment process) and reports, maintenance and replacement data (such as process scheme and welding process assessment), service life assessment reports and the like. The method also comprises the latest data of equipment, new processes, new materials, new technologies, production experience and the like.
The metal equipment supervision information comprises information of standard procedures of countries, industries and the like involved in the technical supervision range, such as information of whether updated standard procedures exist or not, which standard procedures are invalidated and the like. Meanwhile, the main standards and terms related to different types of equipment of the unit are recorded and can be listed in the retrieval result. According to the actual working condition, the rule standards are divided into four types of thermal power, power grids, new energy and special equipment, so that the thermal power generation system is convenient for technicians to use. The information system also contains the relevant information about metal supervision, such as typical structures, typical defects, failure modes, common materials and the like which are common to the main monitored components of the unit. But also the quality system requirements required for the detection of the different components. At present, the metal equipment relates to a plurality of management systems, the network access material detection, the general component metal detection and the like follow the quality management system of the electric academy of sciences, and simultaneously the requirements of CNAS and CMA systems are required to be met, and the requirements of the CNAS and CMA systems on procedure standard execution, report formats, test block use and the like are different. Regular inspection and other work of special equipment such as boilers, pressure vessels and the like of thermal power plants conform to a comprehensive inspection organization system of the special equipment of the national quality supervision bureau, and the system requirements are greatly different from the requirements of the prior system.
The instrument and equipment information, namely the information of the instrument and equipment related to the metal equipment, comprises an instrument verification state, verification validity, instrument and equipment specifications, an operation manual, management program regulations, verification and calibration programs and the like, so that a technician can conveniently use the equipment and the equipment within the verification validity period during detection, the detection of the instrument outside the verification validity period is avoided, and the detection quality is improved.
Reports and report information. The method comprises two parts, namely uploaded monthly and quarterly reports of the thermal power plant. After the report is filled in by the system, the system automatically generates the report. The second is a technical supervision report, a supervision service report, a detection report and the like.
The information is recorded in the form of CAD drawings, text documents (WORD, TXT, PDF, EXCEL), pictures or video data, scans, etc.
And a metal equipment performance analysis module. A large amount of power station operation data are accumulated in the system, the real-time operation data of the power stations can be obtained in time, and the information can be obtained through a retrieval function. According to the retrieval of certain parameters (keywords), all relevant operating parameters can be derived from the system, the importance of different parameters is analyzed through clustering calculation, the influence degree is obtained by using a big data mode, and the possible conditions of the operating equipment can be predicted through analysis in combination with the conditions of the operating equipment, so that preventive measures can be made early and adjusted in real time.
The metal equipment performance analysis module can also be used for pushing the early warning event. If a certain device of a power plant has failure records, the device with similar attributes is early warned, and the early warning information is pushed as an event. The method has similar attributes, mainly aiming at material, specification, suppliers or tensile strength and the like, the influence of each attribute on actual effect is subjected to weight analysis according to data statistics, and the influence degree of different attributes on correlation is analyzed, so that the condition that the two characteristics are similar when the characteristics are the same is obtained. The input of the attributes is derived from the entry of initial data on one hand and reports on the other hand (the report part adopts the mode of automatic identification and manual modification)
Early warning information can be shown in the form of a map, for example, a power plant where equipment with certain faults is located and equipment of the power plant can be displayed, the possible faults can be displayed, namely, a distribution diagram of the power plant needing early warning and corresponding equipment can be visually displayed through the map, and accurate positioning can be achieved.
Aiming at metal parts such as a heated surface pipe, a high-temperature pipeline, a high-temperature fastener and the like which are frequently failed, performance analysis of the heated surface pipe, the high-temperature pipeline and the high-temperature fastener can be realized in a metal equipment performance analysis module.
The function of the module is illustrated by taking the performance analysis of the heating surface as an example.
(1) And a heating surface information retrieval function. As with the above search function, information parameters relating to the heated surface are obtained by searching using keywords or the like.
(2) Heating surface failure statistical function
The failure times of different failure modes under the condition of meeting the limited information can be subjected to statistical analysis to obtain the quantity and proportion of the failure times, and the influence degree and the main reasons of various factors (including materials, specifications, manufacturing units, operation parameters and the like) on various failures are determined through the analysis of big data.
The statistics is based on the retrieval and big data analysis functions of the system, corresponding statistics is completed through selection according to set statistical items, and visual display is carried out.
Statistics of failure times of different failure modes under different options with different attributes:
different attributes may be defined when counting. If the water wall can be defined as all water walls and water walls of northern groups, the statistics is carried out. The statistical conditions corresponding to different options under the attribute can be shown in a graph form. The abscissa may be different options, e.g., 20G, SA106B, to understand the texture, etc. The ordinate is the failure times, the different failure modes are displayed in the form of histogram superposition, and the actual number of times is marked in the display. In addition, clicking any numerical value can switch to the comparison list of the information of the screened failure parts under the limited condition.
Statistics of failure times in different power plant groups or different power plants: in a manner similar to (1) above.
A failure proportion pie chart of different units, different attributes, and different failure modes:
and counting the failure proportion of different units, different attributes and different failure modes, and displaying the failure proportion in a pie chart mode. The definition conditions and the comparison term thereof may be optional. The failure number value at any place corresponds to a comparison list of information of the failure parts screened under the limited condition. For example, a proportion graph of a unit (or a group) of water walls that have prolonged overheating of different materials.
The display of the distribution of the power plants involved in the system on a map can also be realized. When some search or some keyword screening is input, the power plants with the characteristics are displayed on the map. Preferably, the presentation of the map is selectable according to the needs of the user.
(3) Heating surface performance early warning function
Monitoring and early warning the wall temperature of the heating surface: according to the operation information transmitted by each power plant, design parameters are compared, the abnormal temperature condition of the heating surface is monitored, an alarm is given, and meanwhile, the operation information is stored and used as necessary data for subsequent analysis.
Because the wall temperature measuring points of the power plant are limited in arrangement, a temperature field distribution diagram is established according to the positions of the measuring points and the measured temperature values.
Failure early warning based on operating conditions: performing cluster analysis on all cases of failure analysis of the thermal power station metal equipment of the information input in the system to obtain an operation condition threshold value with high probability of failure, and performing early warning on the power plant when the operation parameter exceeds the threshold value; and meanwhile, with the accumulation of failure cases, the early warning threshold values of all the devices are automatically corrected and perfected.
Failure early warning based on failure analysis: and analyzing the possibility of failure of each equipment parameter by combining the failure cases of each thermal power station metal equipment of the information recorded in the system, and giving an early warning to the components with the same or similar properties.
(4) Service life evaluation function of heating surface
And (3) life evaluation: and evaluating the service life of each heating surface by using the established service life evaluation model.
The occurrence of each failure has the following effect on the life of the heating surface: the initial value of the evaluation formula of the heating surface multiplied by the coefficient a, a is set to be 1, the normal service life evaluation is not influenced when no failure occurs, but the value is corrected after the failure occurs, and the influence degree is obtained according to the analysis of the large data of each failure and the service life of the heating surface. And updating the value a in time according to the change of the data, thereby re-evaluating the service life.
Lifetime correction based on failure analysis: and performing cluster analysis on the occurred heating surface failure cases, and correcting the existing service life evaluation model to obtain a service life evaluation model which is closer to the reality.
And (3) service life correction based on the operation condition: and counting the service life conditions of the equipment under different operating conditions, and mastering the influence degree of operating temperature, operating pressure and the like on the service life of the heating surface so as to correct the service life evaluation model.
The performance analysis, early warning, service life evaluation and the like of the high-temperature pipeline and the high-temperature fastener are similar to those of the heating surface, only the parameters, models and the like of the related metal parts are different, the specific steps are the same, and the detailed description is omitted here.
And an announcement module. The module is used for announcing latest information including technical supervision, regulation standards and the like, equipment faults, regulation standard updating, technical hotspots and other contents in China and regions. The information can be displayed to the login personnel in a mode that a popup window and the like are easy to know by the login personnel, and meanwhile, the information can be obtained through retrieval.
And a learning module. The module is mainly used for searching and learning the content of the related equipment by technicians, can record the learning progress in real time, and can be continuously carried out in the next learning. A download function can also be provided for the contents desired to be learned.
And the document processing module can perform processing such as printing, outputting, exporting and the like on the inquired document information. Such as printing the information content of a certain item of equipment, printing the search result, displaying the search result in different forms, exporting the search result and the like.
And a right management module. The module is used to manage user rights. People with different authorities can operate the system at different levels. The general personnel are only used and do not have the authority of management, editing and maintenance. And special personnel maintain, upgrade, expand data and the like the system. The administrator performs system management including authority allocation, data management, system maintenance, data dictionary management and the like.
The system also comprises functions of inputting, retrieving, presenting, processing documents and the like of the information.
(1) Recording: in the system, various information of the equipment can be added in a mode of directly adding entries, and file content identification can be carried out in forms of uploading reports, reports and the like, so that entries are input. Such as equipment supervision reports, inspection reports, etc.
(2) And (3) retrieval: the system can screen information meeting the conditions by setting the option boxes, can also carry out information retrieval by the search boxes, and has a full-text retrieval mode, so that the information in the information table can be retrieved, and the information in documents such as reports and reports can also be retrieved. The system can search various information such as text documents, pictures, videos, pictures and the like recorded in the system.
The function is mainly used for retrieving basic information and process information of the unit and the equipment at each stage. The system has a multi-dimensional comprehensive retrieval function, for example, the retrieval range can be limited within a certain power plant range or within a certain power generation group range, and accurate retrieval and fuzzy retrieval can be supported by limiting the occurrence time and information content (replacement, maintenance or crack and pore defect) of an event. The selection of the search term may be the name of the device or some terms of the information content of the device, such as keywords of the superheater, crack, etc. The method can directly search CAD electronic drawings and text documents, and can also search data such as pictures, videos and the like through keywords.
When searching general documents, not only the information title stored by the keyword search, but also the document content can be searched deeply in the document, and the related information can be searched in the massive database information. For example, by searching the name of the equipment of the primary desuperheater, basic information of the equipment of the primary desuperheater of all power plants in the database, contents related to the primary desuperheater contained in an inspection report or a failure analysis report, structural forms and defect types of common primary desuperheaters, and requirements and terms of the related regulation standards on the primary desuperheater can be searched. By comprehensive retrieval and limited retrieval range, the information of the concerned first-level desuperheater can be quickly acquired. And if the 'crack' key word is searched, all equipment inspection information, replacement information, maintenance information, service life assessment and the like of which the crack defect occurs are obtained.
The core technology of information retrieval is full-text retrieval technology, which is an information retrieval means implemented according to the content of data rather than external features by using various computer data such as characters, voice, images and the like as processing objects. A query is created in the index containing a series of user search criteria that can assist people in the organization and management of large amounts of document material and enable people to quickly and easily locate whatever information they want. Lucene is currently the most popular open source full-text search toolkit and has found widespread use in many search projects.
The data is divided into two types in general: structured data and unstructured data. Structuring data: refers to data having a fixed format or a limited length, such as a database, metadata, and the like. Unstructured data: and data with an indefinite length or unfixed format, such as mails, word documents and the like. Unstructured data is generally referred to as full text data.
According to the classification of data, the search is also divided into two types: search for structured data: such as a search of the database, in SQL statements. And for example, searching metadata, such as searching the file name, the type and the modification time by using windows search, and the like. Search for unstructured data: file contents can be searched by windows search, and a large amount of content data can be searched by grow command under Linux, and Google and Baidu search.
There are two main methods for searching unstructured data, i.e., full-text data: one is sequential Scanning (Serial Scanning): so-called sequential scanning, for each document, scan from beginning to end, if the document contains the character string, the document is the file we are looking for, and then look at the next file until all files are scanned. This approach is slow for large numbers of files. Another method is to extract a part of information in the unstructured data, reorganize the information to make the information have a certain structure, and then search the data with a certain structure, thereby achieving the purpose of relatively fast search. This portion of information extracted from the unstructured data and then reorganized, we call the index. The process of first creating an index and then searching the index is called full text retrieval.
The general process of full text retrieval is that a computer index program scans each word in a document, establishes an index for the word, indicates the occurrence frequency and position of the word in the document, stores the information in an index file, and when a user inquires, the search program searches the index file established in advance according to the inquiry key word input by the user and then returns the search result to the user. This process is similar to our process of looking up a dictionary by retrieving a table of words. As shown in fig. 2.
As can be seen from the above figure, the full text search is roughly divided into two processes: an indexing process and a search process.
The indexing process is a process of creating an index by extracting information from all structured and unstructured data in the real world. The detailed process is as follows: the original document is transmitted to a word segmentation component, the word segmentation component divides the document into individual words, punctuation marks are removed, word stopping is removed, and word elements are obtained; transmitting the obtained word elements to a language processing component for language-dependent processing, wherein the processing result is called a word; and transmitting the obtained words to an index component, creating a dictionary, sequencing the dictionary according to the alphabetical order, and combining the same words to form a document inverted list.
The search process is a process of obtaining a checking request of a user, searching the created index, and then returning a result. The detailed process is as follows: a user inputs a query statement; and performing lexical analysis, syntactic analysis and language processing on the query sentence input by the user. The lexical analysis mainly identifies key words in the query sentence, then the grammar analysis forms the key words into a grammar tree according to grammar rules, and the language processing is the same as the indexing process; searching indexes by using the syntax tree to obtain documents conforming to the syntax tree; and sorting the results according to the obtained relevance of the documents and the query statement, and then returning the results to the user.
(3) Presenting: the system presents the retrieved items in the form of items, and simultaneously adds a comparison function to the important attention information so as to realize information comparison of parts with similar or same characteristics. Including unit information, inspection data information, supervisory information, etc.
For example, in the search, information of some units is searched, the obtained information includes information of power plants to which the units belong and groups to which the power plants belong, and also includes project names, manufacturers, capacity levels, pressure levels, models, types, production time, accumulated operation time, boiler combustion modes, designed coal types, actually used coal types, and the like, and the information can be displayed in a list form. And the distribution of each power plant is shown by a map. If the information of some units is to be compared, the compared units can be selected for list display.
For presentation of the verification data, the data to be presented is selected by retrieval. The method can be detailed to the inspection items and the inspection data conditions of a certain group, a certain power plant, a certain device (such as a main steam pipeline) (simultaneously displaying information of a supplier of the device and the like), and a certain position (namely, a point, a given roll name) and generate an inspection report, and the detailed inspection report can be viewed. The power plant location of the equipment concerned is displayed on a map. For example, for a main steam pipeline in a high-temperature pipeline, at a certain point (such as an outlet weld 1 of a main steam pipeline 1 of a Baotou three-power-plant # 1), the related inspection items comprise ovality, thickness, hardness, metallographic phase, inspection time, accumulated running time and the like, and the corresponding inspection report can be checked. The position information of the three factories in the packet header can also be visually displayed through a map.
Through retrieval, the supervision report can be queried to check detailed supervision report information of a certain device.
The system of the invention utilizes the cloud server to manage data information, perform multitask and distributed operation, and adopts the content delivery network CDN to perform task deployment, thereby fully embodying the high efficiency of cloud service. The final application database of the system comprises data accumulated by more than 50 thermal power plants with 150 machine sets for decades. Therefore, the retrieval function of the information system is required to be very fast, efficient and accurate. The massive data information managed by the system can be used for related technical personnel to analyze data and provide reference for the good operation of equipment.
The system can realize failure analysis reference and has early warning and guiding significance for the existing operating power plant. The statistics display module is flexible in statistics setting and complete in parameters, can display the relationship between each attribute and each parameter on the whole, can perform microscopic display, and can quickly find out the corresponding points to be concerned. The invention also provides abundant means and data for the correlation research of the metal equipment of the power plant and provides reference for improving the safety of the equipment. And the research on the influence of failure on the service life of the heating surface and the research on the influence of deep peak regulation on the service life of the bolt have functions and research functions, so that the system capacity is exerted to the maximum extent.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A thermal power station metal equipment big data analysis and reliability evaluation management system comprises:
the metal equipment database module is used for managing information related to all metal equipment related to the power station, wherein the information comprises basic information, process information and dynamic information of the metal equipment, supervision and inspection information of the metal equipment, instrument information, reports and report information;
the metal equipment performance analysis module retrieves all relevant operating parameters from the metal equipment database module according to the analyzed performance, analyzes the importance of different parameters through clustering calculation, obtains the influence degree by using a big data mode, and predicts the possible occurrence condition of the operating equipment through analysis in combination with the condition of the operating equipment;
the announcement module is used for announcing the latest information;
the learning module is used for the user to exchange learning and download related contents;
the authority management module is used for managing the authority of the user;
and the document processing module is used for processing the inquired document information.
2. The thermal power station metal equipment big data analysis and reliability evaluation management system according to claim 1, characterized in that: the information recording mode in the metal equipment database module comprises CAD drawings, text documents, pictures, video data and scanning parts, and the searching of the contents can be realized.
3. The thermal power station metal equipment big data analysis and reliability evaluation management system according to claim 1, characterized in that: the system can also realize the input, the retrieval, the presentation and the document processing of the related data information.
4. The thermal power station metal equipment big data analysis and reliability evaluation management system as claimed in claim 1, wherein the metal equipment performance analysis module implements performance analysis of a heated surface pipe, a high temperature pipeline and a high temperature fastener.
5. The thermal power station metal equipment big data analysis and reliability evaluation management system according to claim 4, characterized in that: performing cluster analysis on all cases of failure analysis of the thermal power station metal equipment of the information input in the system to obtain an operation condition threshold value with high probability of failure, and performing early warning on the power plant when the operation parameter exceeds the threshold value; and meanwhile, with the accumulation of failure cases, the early warning threshold values of all the devices are automatically corrected and perfected.
6. The thermal power station metal equipment big data analysis and reliability evaluation management system according to claim 4 is characterized in that the possibility of failure of each thermal power station metal equipment parameter is analyzed in combination with failure cases of each thermal power station metal equipment of the information recorded in the system, and early warning is provided for parts with the same or similar properties.
7. The thermal power station metal equipment big data analysis and reliability evaluation management system according to claim 1, characterized in that the system can use the established life evaluation model to evaluate the life of metal equipment; performing cluster analysis on the occurred failure cases, and correcting the existing service life evaluation model to obtain a service life evaluation model which is closer to the reality; and counting the service life conditions of the equipment under different operation conditions, and mastering the influence degree of the operation parameters on the service life of the equipment so as to correct the service life evaluation model.
8. The thermal power plant metal equipment big data analysis and reliability evaluation management system as claimed in claim 1, wherein analysis and statistics in the system can be presented in list items, and the location of the power plant to which the equipment belongs is shown through a map.
9. The thermal power station metal equipment big data analysis and reliability evaluation management system according to claim 1, characterized in that the system realizes comparison of a plurality of equipment operation information.
10. The thermal power station metal equipment big data analysis and reliability evaluation management system is characterized in that the system utilizes a cloud server to manage information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911298681.3A CN111080128A (en) | 2019-12-17 | 2019-12-17 | Big data analysis and reliability evaluation management system for thermal power station metal equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911298681.3A CN111080128A (en) | 2019-12-17 | 2019-12-17 | Big data analysis and reliability evaluation management system for thermal power station metal equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111080128A true CN111080128A (en) | 2020-04-28 |
Family
ID=70315104
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911298681.3A Pending CN111080128A (en) | 2019-12-17 | 2019-12-17 | Big data analysis and reliability evaluation management system for thermal power station metal equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111080128A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112232695A (en) * | 2020-10-28 | 2021-01-15 | 西安热工研究院有限公司 | Full-life-cycle dynamic management method for main metal parts of thermal power generating unit |
| CN114167025A (en) * | 2021-10-15 | 2022-03-11 | 中国大唐集团科学技术研究总院有限公司华北电力试验研究院 | Thermal power generating unit pressure-bearing member temperature tube seat safety evaluation system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134006A (en) * | 2014-08-04 | 2014-11-05 | 昆明理工大学 | Power device dynamic threshold setting method based on historical data clustering |
| CN104361419A (en) * | 2014-09-10 | 2015-02-18 | 国家电网公司 | Electric transmission and transformation equipment state monitoring defect management system and method based on regulation and control integration |
| CN106156485A (en) * | 2016-06-16 | 2016-11-23 | 广州供电局有限公司 | Method for diagnosing fault of power transformer and device |
| CN106936627A (en) * | 2016-09-28 | 2017-07-07 | 清华大学 | A kind of thermal power generating equipment performance monitoring method based on big data analysis mining |
| CN108885628A (en) * | 2016-03-28 | 2018-11-23 | 三菱电机株式会社 | Data analysing method candidate's determination device |
| CN110543500A (en) * | 2019-08-23 | 2019-12-06 | 国家电网有限公司 | Power transmission and transformation equipment health assessment platform based on big data |
-
2019
- 2019-12-17 CN CN201911298681.3A patent/CN111080128A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134006A (en) * | 2014-08-04 | 2014-11-05 | 昆明理工大学 | Power device dynamic threshold setting method based on historical data clustering |
| CN104361419A (en) * | 2014-09-10 | 2015-02-18 | 国家电网公司 | Electric transmission and transformation equipment state monitoring defect management system and method based on regulation and control integration |
| CN108885628A (en) * | 2016-03-28 | 2018-11-23 | 三菱电机株式会社 | Data analysing method candidate's determination device |
| CN106156485A (en) * | 2016-06-16 | 2016-11-23 | 广州供电局有限公司 | Method for diagnosing fault of power transformer and device |
| CN106936627A (en) * | 2016-09-28 | 2017-07-07 | 清华大学 | A kind of thermal power generating equipment performance monitoring method based on big data analysis mining |
| CN110543500A (en) * | 2019-08-23 | 2019-12-06 | 国家电网有限公司 | Power transmission and transformation equipment health assessment platform based on big data |
Non-Patent Citations (1)
| Title |
|---|
| 张艳飞; 郭洋; 孙云飞: "基于Lucene技术的金属设备信息检索系统", 《山东电力技术》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112232695A (en) * | 2020-10-28 | 2021-01-15 | 西安热工研究院有限公司 | Full-life-cycle dynamic management method for main metal parts of thermal power generating unit |
| CN114167025A (en) * | 2021-10-15 | 2022-03-11 | 中国大唐集团科学技术研究总院有限公司华北电力试验研究院 | Thermal power generating unit pressure-bearing member temperature tube seat safety evaluation system |
| CN114167025B (en) * | 2021-10-15 | 2024-04-26 | 天津大唐国际盘山发电有限责任公司 | Thermal power generating unit pressure-bearing member temperature tube seat safety evaluation system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109189946B (en) | Method for converting equipment fault statement description into knowledge graph expression | |
| CN108154291B (en) | Power supply plant performance index evaluation method and system based on strategy library | |
| CN110941725A (en) | Knowledge graph-based hydroelectric generating set fault diagnosis method and system | |
| CN111080128A (en) | Big data analysis and reliability evaluation management system for thermal power station metal equipment | |
| CN112836972A (en) | IT equipment fault defect processing system and fault defect processing method | |
| CN108133316A (en) | A kind of detection method of electric power supply plant power equipment familial defect | |
| CN112818008A (en) | Intelligent diagnosis method, system, medium and electronic equipment for nuclear power debugging faults | |
| CN114398442A (en) | Data-driven information processing system | |
| Wang et al. | Deep analysis of power equipment defects based on semantic framework text mining technology | |
| CN110826735A (en) | Electric power SCADA intelligent multidimensional query and maintenance method | |
| Nusraningrum et al. | Analysis of Overall equipment effectiveness (OEE) on engine power plant performance | |
| Suleimenov et al. | Synthesis of the equipment health management system of the turbine units' of thermal power stations | |
| Jagtap et al. | Requirements and use of in‐service information in an engineering redesign task: Case studies from the aerospace industry | |
| JP7423911B2 (en) | Monitoring support system and monitoring support method | |
| CN112489194A (en) | Method and system for displaying account information and boiler four-tube information in three-dimensional scene | |
| AU2022201697B2 (en) | Method and system of multi-objective and multi-dimensional online joint monitoring for nuclear turbine | |
| CN114077599B (en) | Method, device and system for constructing transformer sensing quantity database supporting dynamic data repair | |
| EP3819729B1 (en) | Human machine interface generation for industrial monitoring | |
| CN112783480B (en) | Intelligent monitoring disc implementation method based on Web interface factory-level monitoring information system | |
| CN114595943A (en) | Mechanical equipment portrait generation method | |
| Morar et al. | Data mining and knowledge reuse for the initial systems design and manufacturing: Aero-engine service risk drivers | |
| RU2704583C1 (en) | Aircraft gas turbine engine test method | |
| EP3540542A1 (en) | Method and apparatus for providing an instantiated industrial semantic model for an industrial infrastructure | |
| CN112765232B (en) | Data management method and system for major equipment of nuclear power plant | |
| JP2002303565A (en) | Evaluation system of remaining life of turbine |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200428 |
|
| RJ01 | Rejection of invention patent application after publication |