CN106710160A - Decision-making tree system-based boiler clausilium smoke temperature early-warning method - Google Patents
Decision-making tree system-based boiler clausilium smoke temperature early-warning method Download PDFInfo
- Publication number
- CN106710160A CN106710160A CN201611238312.1A CN201611238312A CN106710160A CN 106710160 A CN106710160 A CN 106710160A CN 201611238312 A CN201611238312 A CN 201611238312A CN 106710160 A CN106710160 A CN 106710160A
- Authority
- CN
- China
- Prior art keywords
- boiler
- decision tree
- tree system
- cigarette temperature
- clausilium
- 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
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/002—Generating a prealarm to the central station
Landscapes
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Fire-Detection Mechanisms (AREA)
Abstract
The invention discloses a decision-making tree system-based boiler clausilium smoke temperature early-warning method. The method comprises the following steps of (1) obtaining the data A of the environment of a boiler room and the operation parameters of a boiler, then obtaining the smoke temperature critical value B of a boiler clausilium, and establishing a clausilium smoke temperature error rate table T; (2) establishing a decision-making tree system, establishing a contradistinction system, and logically matching the decision-making tree system with the contradistinction system; (3) acquiring the real-time high and low value data of the boiler clausilium smoke temperature by an electronic sensor, transmitting the data to the decision-making tree system, and obtaining a high and low value probability P of the clausilium smoke temperature; (4) if P is larger than 0.8, judging the boiler clausilium smoke temperature to be abnormal, and sending out an alarm prompt by a center console; (5) obtaining the alarm prompt sent from the center console by the boiler staff, confirming the boiler clausilium smoke temperature by the boiler staff, and correcting the decision-making tree system if the boiler staff confirms the normal state of the boiler clausilium smoke temperature and the misjudgement of the decision-making tree system. According to the technical scheme of the invention, the automatic judgment of the boiler clausilium smoke temperature can be realized, and the judgment of the boiler clausilium smoke temperature is accurate.
Description
Technical field
The invention belongs to early warning technology field, more particularly to a kind of Boiler smoke tube cigarette temperature early warning based on decision tree system
Method.
Background technology
Current domestic each smoke pipe cigarette temperature early warning system is provided with electronic sensor prompt system.Traditional electronic sensor
Principle is, by the low value high of smoke pipe cigarette temperature, to be perceived by electronic sensor and for the numerical value of each section timely to feed back to middle control
System.Work points out to learn the low value high of Boiler smoke tube cigarette temperature by the picture and text of central control system.But high temperature due to generator tube,
The corrosivity of stove water, a certain degree of influence is caused on electronic sensor so that cause mistake to estimate in smoke pipe cigarette temperature value of feedback
Value, or there is falsity, cause major accident occur with the judgement for causing boiler staff generation mistake.And sensitivity is high
Electronic sensor it is expensive, replacing is difficult, and is replaced as frequently as so that producing family's very headache.So current domestic cigarette
Pipe cigarette temperature early warning system cannot accurately react the smoke pipe cigarette temperature low value high of boiler.Most electronic sensor uses electricity before this
The principles of chemistry produce electrification to the free metal ion in water, and the low value high of smoke pipe cigarette temperature is pointed out by the transmission of electric signal.
But it is that underwater gold belongs to that ion motion is active to cause certain interference to result that furnace temperature is too high.
The content of the invention
The purpose of the present invention is that and overcomes the deficiencies in the prior art, there is provided a kind of boiler smoke based on decision tree system
Pipe cigarette temperature method for early warning, can immediate correction electronic sensor under circumstances data error, remind boiler staff's cigarette
The situation of pipe cigarette temperature so that staff obtains an accurate smoke pipe cigarette tender feeling condition to ensure the operation of boiler normal table,
To extend the life-span for using of electronic sensor, the maintenance cost of boiler is reduced, realizes the automatization judgement of Boiler smoke tube cigarette temperature,
Accuracy of judgement, no longer needs artificial judgment, mitigates the labour intensity of staff.
To achieve these goals, the invention provides a kind of Boiler smoke tube Yan Wenyu police based on decision tree system
Method, comprises the following steps:
Step (1), acquisition boiler room environment and boiler operating parameter data A, then Boiler smoke tube cigarette temperature critical value B is obtained,
Mutual pace of learning in data A and critical value BCorrespondence goes out error rate table t, is by the numerical quantization in error rate table t
Smoke pipe cigarette temperature error rate table T is set up after decimal between 0-1;
Step (2):Smoke pipe cigarette temperature error rate table T in step (1) sets up decision tree as decision tree system skeleton
System, while the historical data for obtaining staff's artificial judgment Boiler smoke tube cigarette temperature low value high sets up contradistinction system, by decision-making
Tree system carries out logic and matches with contradistinction system;
Step (3):Real-time Boiler smoke tube cigarette temperature data are obtained by electronic sensor, and is transmitted to decision tree system, certainly
Smoke pipe cigarette temperature low value probability P high is obtained after the training of plan tree system repeatedly;
Step (4):Decision tree system judges the size of smoke pipe cigarette temperature low value probability P high, if P is more than 0.8, illustrates pot
Stove pipe cigarette temperature is abnormal, and result is transferred to console by decision tree system, and console provides alarm;If P is less than
0.8, then illustrate that Boiler smoke tube cigarette temperature is normal, console will not provide alarm;
Step (5):After boiler staff obtains the alarm that console sends, to the actual smoke pipe cigarette tender feeling condition of boiler
Confirmed, if it is confirmed that the normal then explanation decision tree system misjudgment of rear Boiler smoke tube cigarette temperature, now boiler staff
Correct result is inputed into contradistinction system, decision tree system is corrected after now contradistinction system is matched with decision tree system logic again
System;If it is confirmed that rear Boiler smoke tube cigarette temperature is abnormal then to illustrate decision tree system correct judgment;
Step (6):Repeat step (3)-(5), so constantly circulation constantly corrects decision tree system until decision tree system
Accuracy of judgement, no longer needs staff's artificial judgment Boiler smoke tube cigarette tender feeling condition.
Further, the formula of decision tree system meets in step (2):
Wherein:XSIt is feedback score, XBHIt is convolution constant, KXIt is the converse feedback number of plies, SOIt is vector convolution constant, KOHIt is fixed
Adopted vector constant collection, fpIt is subset probability, bHIt is counts, KhFor error in judgement is counted.
Beneficial effects of the present invention:The present invention can immediate correction electronic sensor under circumstances data error, carry
The situation of boiler staff's smoke pipe cigarette temperature of waking up so that staff family obtains an accurate smoke pipe cigarette tender feeling condition to ensure
The operation of boiler normal table, to extend the life-span for using of electronic sensor, reduces the maintenance cost of boiler, realizes boiler smoke
The automatization judgement of pipe cigarette temperature, accuracy of judgement no longer needs artificial judgment, mitigates the labour intensity of staff.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
The accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the flow chart of the embodiment of the present invention.
Specific embodiment
Invention is further illustrated below in conjunction with the accompanying drawings, but is not limited to the scope of the present invention.
Embodiment
As shown in figure 1, a kind of Boiler smoke tube cigarette temperature method for early warning based on decision tree system that the present invention is provided, including such as
Lower step:
Step (1), acquisition boiler room environment and boiler operating parameter data A, then Boiler smoke tube cigarette temperature critical value B is obtained,
Mutual pace of learning in data A and critical value BCorrespondence goes out error rate table t, is by the numerical quantization in error rate table t
Smoke pipe cigarette temperature error rate table T is set up after decimal between 0-1;
Boiler room environmental data includes:Boiler room size, there is a several usable boilers, the species of boiler, uses
Time, energy supply type etc..Each item data furnace temperature of boiler operatiopn, cigarette temperature, hydraulic pressure, vapour pressure, water inlet pump discharge, combustor temperature
Degree, air channel data, smoke pipe data etc..
Step (2):Smoke pipe cigarette temperature error rate table T in step (1) sets up decision tree as decision tree system skeleton
System, while the historical data for obtaining staff's artificial judgment Boiler smoke tube cigarette temperature low value high sets up contradistinction system, by decision-making
Tree system carries out logic and matches with contradistinction system;
Step (3):Real-time Boiler smoke tube cigarette temperature data are obtained by electronic sensor, and is transmitted to decision tree system, certainly
Smoke pipe cigarette temperature low value probability P high is obtained after the training of plan tree system repeatedly;
Step (4):Decision tree system judges the size of smoke pipe cigarette temperature low value probability P high, if P is more than 0.8, illustrates pot
Stove pipe cigarette temperature is abnormal, and result is transferred to console by decision tree system, and console provides alarm;If P is less than
0.8, then illustrate that Boiler smoke tube cigarette temperature is normal, console will not provide alarm;
Step (5):After boiler staff obtains the alarm that console sends, to the actual smoke pipe cigarette tender feeling condition of boiler
Confirmed, if it is confirmed that the normal then explanation decision tree system misjudgment of rear Boiler smoke tube cigarette temperature, now boiler staff
Correct result is inputed into contradistinction system, decision tree system is corrected after now contradistinction system is matched with decision tree system logic again
System;If it is confirmed that rear Boiler smoke tube cigarette temperature is abnormal then to illustrate decision tree system correct judgment;
Step (6):Repeat step (3)-(5), so constantly circulation constantly corrects decision tree system until decision tree system
Accuracy of judgement, no longer needs staff's artificial judgment Boiler smoke tube cigarette tender feeling condition.
The formula of decision tree system meets in step (2):
Wherein:XSIt is feedback score, XBHIt is convolution constant, KXIt is the converse feedback number of plies, SOIt is vector convolution constant, KOHIt is fixed
Adopted vector constant collection, fpIt is subset probability, bHIt is counts, KhFor error in judgement is counted.
The present invention can immediate correction electronic sensor under circumstances data error, remind boiler staff's smoke pipe
The situation of cigarette temperature so that staff family obtains an accurate smoke pipe cigarette tender feeling condition to ensure the operation of boiler normal table,
To extend the life-span for using of electronic sensor, the maintenance cost of boiler is reduced, realizes the automatization judgement of Boiler smoke tube cigarette temperature,
Accuracy of judgement, no longer needs artificial judgment, mitigates the labour intensity of staff.
General principle of the invention, principal character and advantages of the present invention has been shown and described above.The technology of the industry
Personnel it should be appreciated that the present invention is not limited to the above embodiments, simply explanation described in above-described embodiment and specification this
The principle of invention, various changes and modifications of the present invention are possible without departing from the spirit and scope of the present invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appending claims and its
Equivalent is defined.
Claims (2)
1. a kind of Boiler smoke tube cigarette temperature method for early warning based on decision tree system, it is characterised in that comprise the following steps:
Step (1), acquisition boiler room environment and boiler operating parameter data A, then Boiler smoke tube cigarette temperature critical value B is obtained, according to
Mutual pace of learning in data A and critical value BCorrespondence goes out error rate table t, is 0-1 by the numerical quantization in error rate table t
Between decimal after set up smoke pipe cigarette temperature error rate table T;
Step (2):Smoke pipe cigarette temperature error rate table T in step (1) sets up decision tree system as decision tree system skeleton
System, while the historical data for obtaining staff's artificial judgment Boiler smoke tube cigarette temperature low value high sets up contradistinction system, by decision tree
System carries out logic and matches with contradistinction system;
Step (3):Real-time Boiler smoke tube cigarette temperature data are obtained by electronic sensor, and is transmitted to decision tree system, decision tree
Smoke pipe cigarette temperature low value probability P high is obtained after system repeatedly training;
Step (4):Decision tree system judges the size of smoke pipe cigarette temperature low value probability P high, if P is more than 0.8, illustrates boiler smoke
Pipe cigarette temperature is abnormal, and result is transferred to console by decision tree system, and console provides alarm;If P is less than 0.8,
Illustrate that Boiler smoke tube cigarette temperature is normal, console will not provide alarm;
Step (5):After boiler staff obtains the alarm that console sends, the actual smoke pipe cigarette tender feeling condition of boiler is carried out
Confirm, if it is confirmed that rear Boiler smoke tube cigarette temperature is normal then to illustrate decision tree system misjudgment, now boiler staff will just
True result inputs to contradistinction system, and decision tree system is corrected after now contradistinction system is matched with decision tree system logic again;
If it is confirmed that rear Boiler smoke tube cigarette temperature is abnormal then to illustrate decision tree system correct judgment;
Step (6):Repeat step (3)-(5), so constantly circulation constantly corrects decision tree system until decision tree system judges
Accurately, staff's artificial judgment Boiler smoke tube cigarette tender feeling condition is no longer needed.
2. a kind of Boiler smoke tube cigarette temperature method for early warning based on decision tree system according to claim 1, it is characterised in that
The formula of decision tree system meets in step (2):
Wherein:XSIt is feedback score, XBHIt is convolution constant, KXIt is the converse feedback number of plies, SOIt is vector convolution constant, KOHFor define to
Amount constant collection, fpIt is subset probability, bHIt is counts, KhFor error in judgement is counted.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201611238312.1A CN106710160A (en) | 2016-12-28 | 2016-12-28 | Decision-making tree system-based boiler clausilium smoke temperature early-warning method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201611238312.1A CN106710160A (en) | 2016-12-28 | 2016-12-28 | Decision-making tree system-based boiler clausilium smoke temperature early-warning method |
Publications (1)
Publication Number | Publication Date |
---|---|
CN106710160A true CN106710160A (en) | 2017-05-24 |
Family
ID=58896889
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201611238312.1A Pending CN106710160A (en) | 2016-12-28 | 2016-12-28 | Decision-making tree system-based boiler clausilium smoke temperature early-warning method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN106710160A (en) |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1841422A (en) * | 2005-02-08 | 2006-10-04 | 神马科技公司 | Method and apparatus for optimizing operation of a power generating plant using artificial intelligence techniques |
US20090125155A1 (en) * | 2007-11-08 | 2009-05-14 | Thomas Hill | Method and System for Optimizing Industrial Furnaces (Boilers) through the Application of Recursive Partitioning (Decision Tree) and Similar Algorithms Applied to Historical Operational and Performance Data |
CN102831269A (en) * | 2012-08-16 | 2012-12-19 | 内蒙古科技大学 | Method for determining technological parameters in flow industrial process |
CN105787563A (en) * | 2014-12-18 | 2016-07-20 | 中国科学院沈阳自动化研究所 | Self-learning mechanism-base fast matching fuzzy reasoning method |
CN106054104A (en) * | 2016-05-20 | 2016-10-26 | 国网新疆电力公司电力科学研究院 | Intelligent ammeter fault real time prediction method based on decision-making tree |
CN106125714A (en) * | 2016-06-20 | 2016-11-16 | 南京工业大学 | Failure rate prediction method combining BP neural network and two-parameter Weibull distribution |
-
2016
- 2016-12-28 CN CN201611238312.1A patent/CN106710160A/en active Pending
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1841422A (en) * | 2005-02-08 | 2006-10-04 | 神马科技公司 | Method and apparatus for optimizing operation of a power generating plant using artificial intelligence techniques |
US20090125155A1 (en) * | 2007-11-08 | 2009-05-14 | Thomas Hill | Method and System for Optimizing Industrial Furnaces (Boilers) through the Application of Recursive Partitioning (Decision Tree) and Similar Algorithms Applied to Historical Operational and Performance Data |
CN102831269A (en) * | 2012-08-16 | 2012-12-19 | 内蒙古科技大学 | Method for determining technological parameters in flow industrial process |
CN105787563A (en) * | 2014-12-18 | 2016-07-20 | 中国科学院沈阳自动化研究所 | Self-learning mechanism-base fast matching fuzzy reasoning method |
CN106054104A (en) * | 2016-05-20 | 2016-10-26 | 国网新疆电力公司电力科学研究院 | Intelligent ammeter fault real time prediction method based on decision-making tree |
CN106125714A (en) * | 2016-06-20 | 2016-11-16 | 南京工业大学 | Failure rate prediction method combining BP neural network and two-parameter Weibull distribution |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20230111440A1 (en) | METHODS AND INTERNET OF THINGS (IoT) SYSTEMS FOR GAS PURIFICATION MANAGEMENT IN STORAGE AND DISTRIBUTION STATION FOR SMART GAS | |
CN106802646A (en) | A kind of boiler tube bursting fault early warning method based on decision tree system | |
CN106845689A (en) | A kind of boiler feed temperature method for early warning based on decision tree system | |
CN106682777A (en) | Boiler flue gas tube blockage early warning method based on decision tree system | |
CN106710161A (en) | Decision tree system-based boiler water-cooling wall corrosion early warning method | |
CN106710160A (en) | Decision-making tree system-based boiler clausilium smoke temperature early-warning method | |
CN106646234A (en) | Boiler main motor fault early-warning method based on decision tree system | |
CN106765294A (en) | A kind of boiler combustion machine insufficient method for early warning of burning based on decision tree system | |
CN106596090A (en) | Decision-making-tree-system-based early warning method for boiler steam valve fault | |
CN106781342A (en) | A kind of boiler air preheater fault early warning method based on decision tree system | |
CN106683351A (en) | Boiler flue gas desulfurization equipment fault warning method based on decision tree system | |
CN106997692B (en) | Hybrid navigation mark alarm intelligent detection method | |
CN106679953A (en) | Boiler regulating gate failure early-warning method based on decision tree system | |
CN106682422A (en) | Boiler fire tube scale early-warning method based on decision tree system | |
CN106710162A (en) | Boiler furnace scaling early warning method based on decision tree system | |
CN106779236A (en) | A kind of boiler drum deformation method for early warning based on decision tree system | |
CN106709657A (en) | Decision tree system-based boiler intake pump failure early warning method | |
CN106710163A (en) | Boiler air pressure early warning method based on decision-making tree system | |
CN106774265A (en) | A kind of boiler small fire defective valve method for early warning based on decision tree system | |
CN106778009A (en) | A kind of boiler furnace incrustation scale method for early warning based on decision tree system | |
CN106845690A (en) | A kind of boiler water level method for early warning based on decision tree system | |
CN106682778A (en) | Boiler flue pressure warning method based on decision tree system | |
CN106774266A (en) | A kind of boiler superheater early warning method for failure based on decision tree system | |
CN106779234A (en) | A kind of coal-saving apparatus for boiler early warning method for failure based on decision tree system | |
CN106768956A (en) | A kind of boiler down-comer fault early warning method based on decision tree system |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
WD01 | Invention patent application deemed withdrawn after publication | ||
WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20170524 |