Up to now, Huaneng Huaiyin Second Power Generation Co., LTD. (hereinafter referred to as Huaneng Huaiyin Power Plant) has gone through three phases of construction, with a total installed capacity1760MWAmong them, the first phase of the project2×220MWThe second-phase expansion project has been shut down2×330MWThe third-phase expansion project is in operation2×330MWIn operation. "Selection6The No. 1 unit was taken as the research object and carried out Intelligent fault early warning for thermal power plant equipmentResearch work on diagnostic systems.
1 The current status of condition-based maintenance in power plants
Huaneng Huaiyin Power Plant is one of the earliest power production enterprises in China to implement the point inspection and maintenance system. However, with the continuous deepening of condition-based maintenance, it is facing many problems that need to be urgently solved: the means of condition monitoring and diagnosis of transfer equipment are single and the efficiency is low. The collection of equipment monitoring data still mainly relies on on-site manual monitoring and recording by inspectors. The software and hardware levels of condition monitoring urgently need to be improved. Without a stable team of specialists as technical support; There is a lack of systematic root cause analysis of the failure mechanism of transfer equipment. With the transformation of the personnel structure, the number of equipment management personnel is decreasing day by day, and the proportion of young workers is increasing. They lack experience in dealing with difficult and complicated problems, and the experience of the masters is difficult to quantify and pass on. Without a dedicated analysis and diagnosis software platform as a means, the brain can't keep up with the pace. The inspection personnel and maintenance personnel cannot cooperate, and effective measures cannot be taken at the nascent stage of faults. It is difficult to carry out refined and optimized maintenance, and the reduction in maintenance costs is not significant. The current vibration testers only have the functions of ordinary acquisition instruments but lack on-site automatic transmission and intelligent diagnosis capabilities. The intelligence and accuracy of equipment diagnosis are not high.
2 Research intentMeaning and expected results
2.1 Research significance
Through the construction of an intelligent equipment fault early warning and diagnosis system management platform, covering the equipment management of main equipment such as boilers, steam turbines, and transformers as well as important auxiliary equipment, a cross-professional, multi-disciplinary, multi-technology, and multi-functional big data information platform is established. Through the vertical sorting and horizontal integration of various professional application systems and the application of the specialist diagnosis system, real-time monitoring of equipment, intelligent diagnosis, comprehensive assessment, and auxiliary production decision-making are achieved. The high integration and in-depth mining of big data are realized to assist in operation, avoid information silos, improve equipment reliability, reduce maintenance costs, alleviate the labor intensity of workers, and innovate equipment management models.
2.2 Expected outcomes
Establish a management platform for intelligent equipment fault early warning and diagnosis systems, and form a big data analysis, diagnosis and optimization center. Realize digital management of equipment; On the intelligent fault early warning and diagnosis system management platform for transfer machines, various intelligent analysis, diagnosis and decision-making systems for boilers, steam turbines and transformers are gradually developed to achieve full life cycle maintenance management of equipment and integrated intelligent operation and maintenance. Enhance the safety and reliability of equipment operation, increase the utilization rate of equipment, extend the service life of equipment, and reduce maintenance time and costs.
3 Research content
The intelligent equipment fault early warning and diagnosis system management platform is based on information technology means such as the Internet, Internet of Things, big data analysis and cloud computing, establishing an intelligent integrated platform to achieve offline and online monitoring, remote monitoring, data analysis, fault diagnosis, three-dimensional visualization management and full life cycle management of equipment. It mainly covers: the leakage early warning and life management system module for the "four tubes" of boilers; Online vibration monitoring module for steam turbine generator sets Online vibration monitoring module for important auxiliary equipment Infrared imaging monitoring module Lubricating oil monitoring module Turning machine rolling bearing life management module Automatic regulation system status monitoring module.
4 Function Introduction
4.1 Integrated platform
The integrated platform is centered on the analysis of equipment operation status, uses status assessment and risk assessment as means, and integrates highly integrated information management software such as intelligent specialist diagnosis database, big data mining, and cloud computing, which is consistent with the overall information planning architecture of the power plant.
Integrated platform
4.2 Boiler "four-tube" leakage early warning and life management system module
The module integrates the inspection data of the boiler anti-wear and explosion-proof system.PIReal-time data, metal test data and other multi-faceted data types, with mathematical model calculation and trend analysis, enable full life cycle management of boiler systems and equipment. At the same time, standardize the boiler ledger, standardize the plans and records, achieve lean management and accurate control, improve the loopholes in the original boiler anti-wear and explosion-proof management of the power plant, optimize and improve the maintenance content, and realize the reliability of anti-wear and explosion-proof of the power plant boilerInformationization and digitalization.
Module features: Create an engineering-level data model for metal visualization in power plants; Establish a management process based on the standardization of metal supervision in power stations to achieve the standardization of management processes. A standardized work content model has been established, achieving the graphical and tabular presentation of work content. Form a knowledge base for metal technology supervision specialists to provide information and decision support for achieving comprehensive equipment management.
Module Function: Graphic model navigation. Based on 3D digital model technology, a visual intelligent safety pre-control system for power station boilers is constructed to achieve visual management of anti-wear and anti-explosion. Basic management. By applying mature database development technology, a refined, visualized, real-time, networked and standardized database identifier is established for a large amount of information data in boiler management work, and each module is classified, stored, indexed and associated. Eventually, a stable, flexible and powerful basic data ledger is developed. Data information management. Establish an operation and maintenance as well as safety pre-control management system by means of information technology. Assign responsibilities to individuals layer by layer through authorization. By sorting out the main links of boiler anti-wear and explosion-proof work and metal inspection, and using digital means for real-time tracking, establish a long-term operation and maintenance information management system for equipment. Statistical analysis of data information. Provide a complete knowledge system, including metal testing, maintenance items, welding processes, maintenance records, maintenance strategies and diagnostic analysis, to form a "holistic" and complete status information of the system and equipment. Evaluation of boiler condition, risk and service life. Continuously track and predict the health status, deterioration damage and life loss of equipment, achieve real-time management of equipment durability, and realize the safety, reliability, long-term monitoring and long-term optimization management of key high-temperature components (equipment, system, unit level) of boilers.
4.3 Online vibration monitoring module for steam turbine generator sets
Online vibration monitoring of steam turbine generator sets
Three-dimensional visualization dynamic real-time monitoring and management. Through real-time calculation of the dynamic and static clearances of each shaft system of the generator set, combined with the three-dimensional actual effect diagram, the changes in the clearances of the bearings and the shaft system are visually displayed. Combined with the vibration level of the unit, the status is automatically identified based on the ratio of the minimum clearance to the designed clearance at the corresponding position, comprehensively reflecting the rotor lifting height, the position of the rotor in the bearing, and the oil film thickness, and displayed in different colors. It can vividly, accurately and reliably identify common faults such as abrasion, looseness, imbalance, misalignment, oil film vorticity, steam flow excitation and component detachment. It is equipped with a color alarm function and can automatically push out the faulty part.
Intelligent fault diagnosis. Based on the fault mechanism and on-site diagnosis experience, establish a fault diagnosis knowledge base for the unit. Analyze the necessary and sufficient conditions for the existence of common faults in steam turbine generator sets, such as mass imbalance, component detachment, large shaft bending, misalignment, rotor friction, loosening, electromagnetic force imbalance, oil film oscillation, steam flow excitation, foundation vibration and resonance, throughTSISystem parameter collection is carried out to establish a vibration fault model for steam turbine generators. By automatically identifying signal characteristics and relying on signal analysis results, different types and natures of faults are strictly distinguished. The credibility of common vibration faults is automatically calculated to achieve precise fault location, and the severity and changing trend of faults are directly displayed on the screen.
Operation guidance and fault handling. Based on the intrinsic connections among the vibration, rotational speed, load and temperature of the unit, it automatically provides suggestions for starting, stopping and continuing the operation of the unit, guiding the unit to start quickly and safely and reducing the start-up time of the unit. It can calculate each item when turning the wheelThe deflection of the shaft section, the position of the high point and the changing trend, determine whether there is any transient or permanent bending of the shaft.
4.4 Online vibration monitoring module for important auxiliary equipment
Real-time online monitoring. The operating status of the equipment is displayed in real time. Through the main monitoring screen of the equipment, the actual operating status of the unit can be understood.
Equipment library management. Establish the overall framework of the equipment and various technical ledgers of equipment from the three-dimensional perspectives of equipment, equipment location and equipment type, and conduct comprehensive management of basic information, maintenance history, cost information, parts list and other information of the equipment.
Intelligent device fault diagnosis. By establishing a vibration analysis and diagnosis model, on the one hand, the detection data is automatically analyzed through model rules to rate the equipment status; on the other hand, accurate equipment fault diagnosis results are formed through the automatic diagnosis of the specialist diagnosis database.
Equipment maintenance management. Managing the data from regular inspections not only enables analysts to compare and analyze the data from different devices at different times but also generates trend charts reflecting the changes in equipment status to track the development of potential equipment issues.
Online vibration monitoring of important auxiliary equipment
4.5 Infrared imaging monitoring module
Infrared diagnostic technology is an effective technical means for monitoring electrical equipment. The use of infrared technology to detect and diagnose electrical equipment has the advantages of no power outage, no sampling, no contact, being intuitive, accurate, highly sensitive and having a wide range of applications. It can detect various equipment defects and plays an important role in improving the reliability of equipment operation. This module realizes equipment ledger management, inspection task management, inspection data management, and diagnostic rule management. It automatically performs structured processing, classified storage, and in-depth integration of the data from each inspection.
Intelligent diagnosis. It is equipped with built-in diagnostic standards. During inspection, the diagnostic algorithm can be called on the spot to intelligently diagnose the current status of the equipment and provide diagnostic conclusions such as "normal, general defect, serious defect, and urgent defect". Based on the diagnostic conclusions, it automatically builds a solution and saves the diagnostic conclusions and solutions in the current inspection task.
Intelligent prediction. The system is equipped with a built-in structured data engine, based onBased on the historical data from previous inspections, the historical temperature or temperature rise curve of the equipment is visually displayed on site, and the possible future temperature or temperature rise trend of the equipment is predicted.
Intelligent management. Realize equipment ledger management, inspection task management and inspection data management.
4.6 Lubricating oil monitoring module
Full life cycle control of lubricating oil. Establish a comprehensive lubricant management system. By implementing full-process management, from design, procurement, transportation and storage, installation, testing, operation and maintenance, recycling and disposal, etc., sort out the important work at key nodes, incorporate key tasks into the management process, and form an effective closed-loop management of lubricants.
Operation control. Task arrangement is carried out in accordance with the regulations of the power plant. Sampling and testing of oil products are conducted by equipment, monitoring points, and periodic push and reminder to the testing personnel. The test results are entered and the test status is viewed in real time in the system, thereby reflecting the status of the oil products.
Data visualization. Relevant technical test data or equipment ledger information as well as work tasks can be viewed on the graphic ledger. The status of oil products is expressed in different colors, and defect events are marked and prompted to achieve tracking management.
Intelligent evaluation of lubrication. It provides a variety of specialist diagnostic libraries and multiple algorithm tools, automatically pushes diagnostic results and analysis decisions, assists managers and technicians in completing the process control of lubricating oil work in power plants, and improves the intelligent management level of lubricating oil.
4.7 Transfer machine rolling bearing life management module
The life loss of the rolling bearings of each auxiliary machine is classified into four states: green represents the normal state, yellow represents the relatively severe state, orange represents the severe state, and red represents the very severe state. By integrating factors such as the design life time of the bearing, actual operating time, temperature, temperature rise, vibration, lost life, quality, rationality of the supporting structure, lubrication method, operating status and operating environment, and through background calculation and discrimination of the remaining life of the rolling bearing, the final life status of the rolling bearing is displayed.
4.8 Automatic regulation system status monitoring module
There are two types of fan regulation in thermal power plants: static blade adjustable and dynamic blade adjustable. Currently, the blade structure of dynamic blade adjustable fans has a very high failure rate due to reasons such as jamming and oil quality. Fan tripping often occurs, and even non-shutdown events of the unit occur, which has a significant impact on the safe production of the unit. For thermal power plants, wind turbines are very important auxiliary equipment. Generally, the six major wind turbines are arranged in pairs. Under stable operating conditions, the current and air volume of two fans are basically stable. If the motor current or the opening degree of the damper of one fan in operation shows a significant deviation (empirical value), it can be determined that the air volume regulation characteristics of a certain fan are abnormal. At this time, timely warning and prompt elimination of equipment faults will play a positive role in preventing non-shutdown events of fan regulation system failures. The system collects the opening degree, current, regulated quantity and set value signals of the six major fans, performs calculations and outputs alarm signals.
5 Technical key
Equipment management is carried out through 3D visualization to achieve 3D models and equipment status data of important equipment, specialist diagnosis, and full life cycle management. Develop an intelligent fault early warning and diagnosis system for transfer machines, form a knowledge base for specialist diagnosis, achieve status assessment, risk assessment and life assessment of key equipment, and form intelligent decision-making. Sort out the interrelationships among various specialties and systems, break down data silos, establish an integrated management platform, achieve a cross-specialty and cross-system working mode, and ensure that all subsystems operate under the same working environment, standards, norms and models.
6 Closing remarks
Through the research and application of the intelligent fault early warning and diagnosis system management platform for transfer, it helps power plants rapidly and comprehensively improve the reliability of equipment and the output level of units. Reduce or eliminate accidents involving non-shutdown, load reduction and environmental protection emissions. Significantly reduce operation and maintenance costs and workload (operation and maintenance costs are lower than the average of the previous three years)30％";" Help power plants implement life management, improve safety performance, and reasonably extend the service life of equipment; By implementing optimized maintenance, predictive maintenance management is achieved. The main auxiliary machines are subject to condition-based maintenance, and regular maintenance is cancelled, thereby reducing maintenance costs. By enhancing management efficiency and improving personnel quality, it helps power plants quickly cultivate a large number of new equipment management specialists. Remote monitoring and diagnostic services have become the new normal. The continuous enrichment and sharing of equipment and databases will completely break away from the reliance on individual specialists and become a valuable asset for enterprises. Comprehensively support the innovation and management of equipment management models in power plants.
Beijing BKC Technology Co., Ltd. is committed to providing integrated solutions for the construction and operation and maintenance control of intelligent power plants. The "Intelligent Management Solution for Equipment Reliability" and the "Intelligent Control Solution for Operation Optimization" provided have been adopted both at home and abroad200Many power generation enterprises have been successfully put into use and continue to show results. The company was established in2002In that year, a national high-tech enterprise listed on the New Third Board, with registered capital1Yi possesses a series of independent intellectual property rights, including invention patents and software Copyrights.
