CN108803569A - Station boiler diagnostic expert system and its method for diagnosing faults - Google Patents
Station boiler diagnostic expert system and its method for diagnosing faults Download PDFInfo
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- CN108803569A CN108803569A CN201810596324.4A CN201810596324A CN108803569A CN 108803569 A CN108803569 A CN 108803569A CN 201810596324 A CN201810596324 A CN 201810596324A CN 108803569 A CN108803569 A CN 108803569A
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- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000003745 diagnosis Methods 0.000 claims abstract description 26
- 208000024891 symptom Diseases 0.000 claims abstract description 7
- 230000008569 process Effects 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 4
- 238000011161 development Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 230000006641 stabilisation Effects 0.000 claims description 2
- 238000011105 stabilization Methods 0.000 claims description 2
- 238000005259 measurement Methods 0.000 abstract description 2
- 230000002159 abnormal effect Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000010977 unit operation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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- 230000015556 catabolic process Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
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Abstract
Station boiler diagnostic expert system and its method for diagnosing faults.Some thermal power plant's operation troubles are the reason is that less intuitive at present, the parameter and state variable that its system all monitors are up to hundreds of thousands of, will from thousands of a measurement parameters, state variable and warning message failure judgement occur position, it can be said that very difficult thing.Present invention composition includes being mounted on the DCS data collecting system modules that boiler parameter is acquired on boiler controller system, the DCS data collecting systems module is connect with real-time dataBase system module, the real-time dataBase system module is connect with overload alarm module, the overload alarm module is connect with fault diagnosis module, and the fault diagnosis module is connect with diagnostic knowledge database module, failure symptom database module, knowledge base update module, failure predication diagnostic module, subscriber interface module respectively.The present invention is applied to station boiler fault diagnosis.
Description
Technical field：
The present invention relates to a kind of station boiler diagnostic expert system and its method for diagnosing faults.
Background technology：
The diagnosis of thermal power plant's operation troubles at present depends primarily on operations staff to some observed parameters in equipment running process
Analysis, by virtue of experience explain failure occur the reason of, therefore, the correctness of fault diagnosis is heavily dependent on operation
The micro-judgment of personnel.But some failure causes are less intuitive, and the parameter and state variable that system all monitors are reachable
It is hundreds of thousands of, it failure judgement occurs from thousands of a measurement parameters, state variable and warning message position, it can be said that
Very difficult thing.There are many factor for influencing the boiler working of a furnace, and the relationship between each factor is also very complicated, to accurately describe this
A little uncertain factors are highly difficult.We can only judge whether boiler operatiopn is normal to the judgement of the station boiler working of a furnace at present,
The abnormal conditions of the working of a furnace further can not be judged.
Invention content：
In order to overcome the above problem of the existing technology, the object of the present invention is to provide a kind of station boiler diagnostic expert systems
And its method for diagnosing faults.
Above-mentioned purpose is realized by following technical scheme：
A kind of station boiler diagnostic expert system, composition include：The DCS data of boiler parameter are acquired on boiler controller system
Acquisition system module, the DCS data collecting systems module are connect with real-time dataBase system module, the real time data
Library system module is connect with overload alarm module, and the overload alarm module is connect with fault diagnosis module, the failure
Diagnostic module is examined with diagnostic knowledge database module, failure symptom database module, knowledge base update module, failure predication respectively
Disconnected module, subscriber interface module connection.
The method for diagnosing faults of the station boiler diagnostic expert system, this method for diagnosing faults include three steps:
（1）It determines monitoring content, the characteristic signal data of extraction system state, passes through the analysis acquisition pair to characteristic signal data
As the information of working condition, and carry out characteristic signal selection；
（2）Failure cause sign is extracted from detected characteristic signal, by determining that each information corresponds to the analysis of signal
State, the foundation as fault diagnosis；
（3）It is diagnosed fault according to failure cause sign, the pre- of certain failures is made according to the Change and Development trend for having related parameter
It surveys, realizes that Knowledge based engineering diagnostic reasoning, reasoning are diagnosed automatically based on inference machine, inference machine is according to the current information of boiler
With past account of the history, the related rule in activated knowledge library, that is, using the experience of expert, asked by expert's mode of thinking
The reasoning process of solution problem, inference machine is as follows:1. reading is currently inserted into the fact, the premise phase with the indirect rule in knowledge base
Match, and will be in the conclusion deposit database of the rule of successful match；2. using conclusion that previous step obtains as the new fact with know
The premise for knowing the direct rule in library matches;3. if the fact in database reaches a kind of state of stabilization, i.e., again without new
The fact when generating, terminate reasoning process, export suggestion or the conclusion of expert system.
Beneficial effects of the present invention：
1. the present invention is effectively monitored boiler controller system operational safety state, following target can be realized：（1）Reduce failure hair
Raw probability；（2）Quick diagnosis boiler plant failure；（3）Improve boiler plant reliability/availability；（4）It is unplanned to reduce boiler
It shuts down；The present invention can eliminate in time causes equipment to run abnormal reason.Operations staff is set to make by expert system
Correctly judge and take necessary measure.And can fast and effeciently analyze cause of accident and its need the countermeasure taken,
Shorten the repair time, it is very fast to find that failure leaks potential rule and reason, the possibility that failure occurs repeatedly is efficiently reduced, is dropped
Low failure frequency improves unit availability.
This station boiler diagnostic expert system accumulates the knowwhy of brainstrust and abundant production experience,
Using signal acquisition, data analysis as foundation, when equipment occurs abnormal, can be compared by means of computer monitor and diagnostic system
Where timely and accurately automatic decision goes out reason, operations staff is instructed to debug in time, improve unit operation safety and
Reliability is effectively prevented the generation of major accident, boiler breakdowns generation before or just there is omen when, from thermal parameter
Variation timely forecast that take resolute measure rapidly, the generation prevented accident releases hidden danger in time, and it is unnecessary to avoid
Shutdown, improve economy of power plant benefit.
The major function that station boiler diagnostic expert system of the present invention is completed includes:1. determining fault type, 2. find out
Failure cause;3. the possible consequence of appraisal, the state of the art and its ability to work that 4. general comment diagnoses;5. proposing carrying for diagnosis object
6. view explains proposed reason, diagnostic experiences is 7. accumulated, to improve given advice accuracy.It establishes and is suitble to power plant
Knowledge base model, analysis of cases model can analyze operating status, fault point, the failure of boiler main and auxiliaries equipment in real time
Reason and the treating method that debugging is provided for operation maintenance personnel, to realize the early stage to boiler plant failure and exception
The optimization operation of station boiler unit highly effective and safe is realized in early warning and diagnosis.
Station boiler diagnostic expert system of the present invention includes several expert diagnosis modules, and each diagnostic module includes special in factory
The diagnosis algorithm and model that team of family designs according to unit feature, can increase diagnostic module newly according to actual conditions, are repaiied
Change.System carries out distributed data cleaning according to expert diagnosis algorithm, to the Power Plant operation data in database, excavates,
Judge set state and predict potential faults that may be present, and immediately by boiler controller system state, parameter visualization be shown to it is flat
Platform front end page provides rationalization operation behaviour if it find that set state exception or there are hidden danger, will automatically generate diagnosis report
Work is suggested, so that operations staff is with reference to use.
Description of the drawings：
Attached drawing 1 is the system structure diagram of the present invention.
Specific implementation mode：
Embodiment 1：
A kind of station boiler diagnostic expert system, composition include：Acquire the DCS data collecting system modules of boiler parameter, institute
The DCS data collecting systems module stated is connect with real-time dataBase system module, the real-time dataBase system module with it is super
Limit alarm module connection, the overload alarm module connect with fault diagnosis module, the fault diagnosis module respectively with
Diagnostic knowledge database module, failure symptom database module, knowledge base update module, failure predication diagnostic module, Yong Hujie
Face mould block connects.
Data collecting system module:The various parameters of boiler are acquired by the sensor on boiler controller system.
Real-time dataBase system module:Real time data and historical data for storing boiler controller system various parameters.It is failure
The primary information resource of diagnosis.
Failure symptom database module:Be used to store generated in need during reasoning and operational process it is all
Failure symptom is true.
Diagnostic knowledge database module:Knowledge for storing expert and the related knowledge with diagnosis.
Overload alarm module：Compare the design value in the actual measured value and knowledge base of data unit operation, obtains boiler
Operating states of the units, and result is put into failure symptom database, and alarming.
Fault diagnosis module:Using the sign fact as foundation, using the knowledge in diagnostic knowledge base, the diagnosis of complete paired fault
Task, and diagnostic result is exported.Its major function is diagnostic reasoning and diagnostic interpretation, according to the final judging result of system,
Provide the handling suggestion for specific fault.
Knowledge base update module:Be responsible for the knowledge in maintenance knowledge library, enable knowledge base constantly enrich and improve.
Failure predication diagnostic module:It will be according to the letter of sample information and the obtained sign factbase after signal is analyzed
Breath is compared with the rule in diagnostic knowledge base, then analyzes that obtain may be by the accident of generation.
Subscriber interface module:For the interaction of technical staff and diagnostic system, the tissue to knowledge and update are completed.
Embodiment 2：
The method for diagnosing faults of above-mentioned station boiler diagnostic expert system, this method for diagnosing faults include three steps:
（1）Determine that monitoring content, the characteristic signal data of extraction system state, the state of these characteristic signal data homologous rays are close
Cut phase is closed, and the state of system, the information that characteristic signal data include can be efficiently identified out by the characteristic signal data of system
Amount is more, it is higher to the effective value of fault diagnosis, these characteristic signal data include boiler pressure, temperature, flow
Etc. various analog quantitys and switching value data, by obtaining the information of object working condition to the analysis of characteristic signal data, go forward side by side
Row characteristic signal is chosen；
（2）Failure cause sign is extracted from detected characteristic signal, by determining that each information corresponds to the analysis of signal
State, the foundation as fault diagnosis；
（3）It is diagnosed fault according to failure cause sign, this is also the core of diagnosis process, is become according to there is the Change and Development of related parameter
Gesture makes the prediction of certain failures, realizes that Knowledge based engineering diagnostic reasoning, reasoning are diagnosed automatically based on inference machine, reasoning
Machine is according to the current information of boiler and past account of the history, the related rule in activated knowledge library, that is, utilizes expert's
Experience, by expert's mode of thinking Solve problems, the reasoning process of inference machine is as follows:1. reading is currently inserted into the fact, with knowledge base
In the premise of indirect rule match, and will successful match rule conclusion deposit database in；2. previous step is obtained
Conclusion match as the premise of the new fact and the direct rule in knowledge base;3. if the fact in database reaches one
The stable state of kind, i.e., when being generated again without the new fact, end reasoning process exports suggestion or the conclusion of expert system.
Claims (2)
1. a kind of station boiler diagnostic expert system, composition include：The DCS numbers of boiler parameter are acquired on boiler controller system
According to acquisition system module, it is characterized in that：The DCS data collecting systems module is connect with real-time dataBase system module, institute
The real-time dataBase system module stated is connect with overload alarm module, and the overload alarm module connects with fault diagnosis module
Connect, the fault diagnosis module respectively with diagnostic knowledge database module, failure symptom database module, knowledge base update mould
Block, failure predication diagnostic module, subscriber interface module connection.
2. the method for diagnosing faults of station boiler diagnostic expert system according to claim 1, it is characterized in that：This failure is examined
Disconnected method includes three steps:
（1）It determines monitoring content, the characteristic signal data of extraction system state, passes through the analysis acquisition pair to characteristic signal data
As the information of working condition, and carry out characteristic signal selection；
（2）Failure cause sign is extracted from detected characteristic signal, by determining that each information corresponds to the analysis of signal
State, the foundation as fault diagnosis；
（3）It is diagnosed fault according to failure cause sign, the pre- of certain failures is made according to the Change and Development trend for having related parameter
It surveys, realizes that Knowledge based engineering diagnostic reasoning, reasoning are diagnosed automatically based on inference machine, inference machine is according to the current information of boiler
With past account of the history, the related rule in activated knowledge library, that is, using the experience of expert, asked by expert's mode of thinking
The reasoning process of solution problem, inference machine is as follows:1. reading is currently inserted into the fact, the premise phase with the indirect rule in knowledge base
Match, and will be in the conclusion deposit database of the rule of successful match；2. using conclusion that previous step obtains as the new fact with know
The premise for knowing the direct rule in library matches;3. if the fact in database reaches a kind of state of stabilization, i.e., again without new
The fact when generating, terminate reasoning process, export suggestion or the conclusion of expert system.
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| CN201810596324.4A CN108803569A (en) | 2018-06-11 | 2018-06-11 | Station boiler diagnostic expert system and its method for diagnosing faults |
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| CN201810596324.4A CN108803569A (en) | 2018-06-11 | 2018-06-11 | Station boiler diagnostic expert system and its method for diagnosing faults |
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Family
ID=64088274
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Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110045211A (en) * | 2019-05-16 | 2019-07-23 | 集美大学 | A kind of unmanned ships and light boats fault diagnosis filter method |
| CN110059359A (en) * | 2019-03-21 | 2019-07-26 | 江苏东方国信工业互联网有限公司 | System and method for controlling furnace body process based on big data analysis |
| CN110672198A (en) * | 2019-08-26 | 2020-01-10 | 华电电力科学研究院有限公司 | Boiler flue gas and air system vibration fault diagnosis method |
| CN111312420A (en) * | 2020-03-02 | 2020-06-19 | 上海交通大学 | Fault diagnosis method and device |
| CN111459700A (en) * | 2020-04-07 | 2020-07-28 | 华润电力技术研究院有限公司 | Method and apparatus for diagnosing device failure, diagnostic device, and storage medium |
| CN111474870A (en) * | 2019-01-23 | 2020-07-31 | 庄瑛 | Fault diagnosis and detection system based on machine learning |
| CN111722953A (en) * | 2020-06-17 | 2020-09-29 | 成都美迅检测设备有限公司 | Fault diagnosis method, fault diagnosis device, electronic equipment and storage medium |
| CN112633614A (en) * | 2021-01-15 | 2021-04-09 | 东方电气集团科学技术研究院有限公司 | Real-time fault degree diagnosis system and method based on feature extraction |
| CN113091309A (en) * | 2021-03-08 | 2021-07-09 | 浙江大学 | Heat conduction oil circulation fault diagnosis system |
| CN113298133A (en) * | 2021-05-18 | 2021-08-24 | 沈阳航空航天大学 | Supercritical unit boiler tube burst fault diagnosis method |
| CN113485262A (en) * | 2021-06-29 | 2021-10-08 | 华能(浙江)能源开发有限公司玉环分公司 | SVM-based fault analysis method for fuel system of thermal power plant |
| CN113505282A (en) * | 2021-06-11 | 2021-10-15 | 国网浙江省电力有限公司嘉兴供电公司 | Expert system-based high-voltage circuit breaker state identification method and system |
| CN113592247A (en) * | 2021-07-06 | 2021-11-02 | 安徽海螺信息技术工程有限责任公司 | Method for diagnosing, analyzing and positioning faults of waste heat power generation system |
| CN113609299A (en) * | 2021-10-11 | 2021-11-05 | 浙江浙能技术研究院有限公司 | Fault diagnosis library establishment method based on ant colony algorithm and feature recombination |
| CN114064911A (en) * | 2021-09-30 | 2022-02-18 | 中国核电工程有限公司 | Modeling method and system for expert knowledge base of intelligent diagnostic system of nuclear power plant |
| CN114139297A (en) * | 2021-10-09 | 2022-03-04 | 昆明嘉和科技股份有限公司 | Expert diagnostic system based on pump equipment state monitoring and big data analysis |
| CN115167324A (en) * | 2022-08-22 | 2022-10-11 | 上海外高桥第三发电有限责任公司 | A system and method for early warning, diagnosis and intervention of equipment failure in thermal power station |
| WO2025039220A1 (en) * | 2023-08-23 | 2025-02-27 | 宁波厚德能源科技有限公司 | System for analyzing boiler fault and diagnosis method |
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Cited By (26)
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| CN111474870A (en) * | 2019-01-23 | 2020-07-31 | 庄瑛 | Fault diagnosis and detection system based on machine learning |
| CN110059359A (en) * | 2019-03-21 | 2019-07-26 | 江苏东方国信工业互联网有限公司 | System and method for controlling furnace body process based on big data analysis |
| CN110045211A (en) * | 2019-05-16 | 2019-07-23 | 集美大学 | A kind of unmanned ships and light boats fault diagnosis filter method |
| CN110672198A (en) * | 2019-08-26 | 2020-01-10 | 华电电力科学研究院有限公司 | Boiler flue gas and air system vibration fault diagnosis method |
| CN110672198B (en) * | 2019-08-26 | 2021-08-17 | 华电电力科学研究院有限公司 | Boiler flue gas and air system vibration fault diagnosis method |
| CN111312420A (en) * | 2020-03-02 | 2020-06-19 | 上海交通大学 | Fault diagnosis method and device |
| CN111459700A (en) * | 2020-04-07 | 2020-07-28 | 华润电力技术研究院有限公司 | Method and apparatus for diagnosing device failure, diagnostic device, and storage medium |
| CN111459700B (en) * | 2020-04-07 | 2023-04-28 | 华润电力技术研究院有限公司 | Equipment fault diagnosis method, diagnosis device, diagnosis equipment and storage medium |
| CN111722953A (en) * | 2020-06-17 | 2020-09-29 | 成都美迅检测设备有限公司 | Fault diagnosis method, fault diagnosis device, electronic equipment and storage medium |
| CN112633614A (en) * | 2021-01-15 | 2021-04-09 | 东方电气集团科学技术研究院有限公司 | Real-time fault degree diagnosis system and method based on feature extraction |
| CN112633614B (en) * | 2021-01-15 | 2024-07-23 | 东方电气集团科学技术研究院有限公司 | Real-time fault degree diagnosis system and method based on feature extraction |
| CN113091309A (en) * | 2021-03-08 | 2021-07-09 | 浙江大学 | Heat conduction oil circulation fault diagnosis system |
| CN113091309B (en) * | 2021-03-08 | 2022-02-22 | 浙江大学 | Heat conduction oil circulation fault diagnosis system |
| CN113298133A (en) * | 2021-05-18 | 2021-08-24 | 沈阳航空航天大学 | Supercritical unit boiler tube burst fault diagnosis method |
| CN113298133B (en) * | 2021-05-18 | 2023-09-26 | 沈阳航空航天大学 | Fault diagnosis method for boiler tube burst in supercritical unit |
| CN113505282A (en) * | 2021-06-11 | 2021-10-15 | 国网浙江省电力有限公司嘉兴供电公司 | Expert system-based high-voltage circuit breaker state identification method and system |
| CN113485262B (en) * | 2021-06-29 | 2022-04-29 | 华能(浙江)能源开发有限公司玉环分公司 | SVM-based fault analysis method for fuel system of thermal power plant |
| CN113485262A (en) * | 2021-06-29 | 2021-10-08 | 华能(浙江)能源开发有限公司玉环分公司 | SVM-based fault analysis method for fuel system of thermal power plant |
| CN113592247A (en) * | 2021-07-06 | 2021-11-02 | 安徽海螺信息技术工程有限责任公司 | Method for diagnosing, analyzing and positioning faults of waste heat power generation system |
| CN114064911A (en) * | 2021-09-30 | 2022-02-18 | 中国核电工程有限公司 | Modeling method and system for expert knowledge base of intelligent diagnostic system of nuclear power plant |
| CN114139297A (en) * | 2021-10-09 | 2022-03-04 | 昆明嘉和科技股份有限公司 | Expert diagnostic system based on pump equipment state monitoring and big data analysis |
| CN114139297B (en) * | 2021-10-09 | 2024-04-23 | 昆明嘉和科技股份有限公司 | Expert diagnosis system based on machine pump equipment state monitoring and big data analysis |
| CN113609299B (en) * | 2021-10-11 | 2021-12-28 | 浙江浙能技术研究院有限公司 | Fault diagnosis library establishment method based on ant colony algorithm and feature recombination |
| CN113609299A (en) * | 2021-10-11 | 2021-11-05 | 浙江浙能技术研究院有限公司 | Fault diagnosis library establishment method based on ant colony algorithm and feature recombination |
| CN115167324A (en) * | 2022-08-22 | 2022-10-11 | 上海外高桥第三发电有限责任公司 | A system and method for early warning, diagnosis and intervention of equipment failure in thermal power station |
| WO2025039220A1 (en) * | 2023-08-23 | 2025-02-27 | 宁波厚德能源科技有限公司 | System for analyzing boiler fault and diagnosis method |
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