CN106709605A - Method for early warning of boiler fire tube corrosion based on decision tree system - Google Patents

Method for early warning of boiler fire tube corrosion based on decision tree system Download PDF

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Publication number
CN106709605A
CN106709605A CN201611239788.7A CN201611239788A CN106709605A CN 106709605 A CN106709605 A CN 106709605A CN 201611239788 A CN201611239788 A CN 201611239788A CN 106709605 A CN106709605 A CN 106709605A
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CN
China
Prior art keywords
decision tree
tree system
boiler
fire tube
corrosion
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Pending
Application number
CN201611239788.7A
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Chinese (zh)
Inventor
刘海涛
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Hunan Kun Yu Network Technology Co Ltd
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Hunan Kun Yu Network Technology Co Ltd
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Application filed by Hunan Kun Yu Network Technology Co Ltd filed Critical Hunan Kun Yu Network Technology Co Ltd
Priority to CN201611239788.7A priority Critical patent/CN106709605A/en
Publication of CN106709605A publication Critical patent/CN106709605A/en
Pending legal-status Critical Current

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    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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

Abstract

The present invention discloses a method for early warning of boiler fire tube corrosion based on a decision tree system. The method comprises the following steps: (1) the boiler room environment and boiler operation parameter data A are obtained, a critical value B of the boiler fire tube corrosion is obtained, and an error rate table T of fire tube corrosion is established; (2) a decision tree system and a comparison system are established, and logical matching between the decision tree system and the comparison system are carried out; (3) an electronic sensor obtains real-time boiler fire tube data and the data is transmitted to the decision tree system so as to obtain a probability of high and low corrosion rates of fire tube corrosion; (4) if P is greater than 0.8, then the boiler fire tube is corroded, so that the center console issues an alarm, and if P is less than 0.8, the boiler fire tube is normal; and (5) after receiving the alarm issued by the center console, the boiler staff carry out confirmation, and if the boiler fire tube is corroded after confirmation, it means that the decision tree system makes wrong determination, so that the decision tree system is modified. According to the method disclosed by the present invention, automatic determination of the boiler fire tube corrosion is realized, and the determination is accurate.

Description

A kind of flue tube corrosion method for early warning based on decision tree system
Technical field
The invention belongs to early warning technology field, more particularly to a kind of flue tube corrosion early warning based on decision tree system Method.
Background technology
Current domestic each fire tube corrosion early warning system is provided with electronic sensor prompt system.Traditional electronic sensor Principle is, in the low value high for corroding fire tube, 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 that flue tube corrodes 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 fire tube corrosion 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 country's fire The fire tube that pipe corrosion early warning system cannot accurately react boiler corrodes low value high.Most electronic sensor uses electricity before this The principles of chemistry produce electrification to the free metal ion in water, the low value high for pointing out fire tube to corrode 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 fire based on decision tree system Pipe corrodes method for early warning, can immediate correction electronic sensor under circumstances data error, remind boiler staff fire The situation of pipe corrosion so that staff obtains an accurate fire tube corrosion 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 flue tube corrosion, Accuracy of judgement, no longer needs artificial judgment, mitigates the labour intensity of staff.
To achieve these goals, the pre- police are corroded the invention provides a kind of flue tube based on decision tree system Method, comprises the following steps：
Step (1), acquisition boiler room environment and boiler operating parameter data A, then flue tube corrosion 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 Fire tube corrosion error rate table T is set up after decimal between 0-1；
Step (2)：Fire tube in step (1) corrodes error rate table T as decision tree system skeleton, sets up decision tree System, while the historical data for obtaining staff's artificial judgment flue tube corrosion low value high sets up contradistinction system, by decision-making Tree system carries out logic and matches with contradistinction system；
Step (3)：Real-time flue tube data are obtained by electronic sensor, and is transmitted to decision tree system, decision tree Fire tube is obtained after system repeatedly training corrode low value probability P high；
Step (4)：Decision tree system judges that fire tube corrodes the size of low value probability P high, if P is more than 0.8, illustrates pot Stove fire pipe corrodes, and result is transferred to console by decision tree system, and console provides alarm；If P is less than 0.8, say Bright flue tube is normal, and console will not provide alarm；
Step (5)：After boiler staff obtains the alarm that console sends, the actual fire tube situation of boiler is carried out Confirm, if it is confirmed that rear flue tube normally then illustrates decision tree system misjudgment, now boiler staff will correctly tie Fruit inputs to contradistinction system, and decision tree system is corrected after now contradistinction system is matched with decision tree system logic again；If Flue tube corrosion then illustrates decision tree system correct judgment after confirmation；
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 flue tube corrosion 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 that boiler staff fire tube of waking up corrodes so that staff family obtains an accurate fire tube corrosion 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 fire The automatization judgement of pipe corrosion, 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 flue tube corrosion 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 flue tube corrosion 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 Fire tube corrosion 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..Boiler operating parameter data include：Furnace temperature, cigarette temperature, hydraulic pressure, vapour pressure, water inlet pump discharge, burning Machine temperature, air channel data, fire tube etc..
Step (2)：Fire tube in step (1) corrodes error rate table T as decision tree system skeleton, sets up decision tree System, while the historical data for obtaining staff's artificial judgment flue tube corrosion low value high sets up contradistinction system, by decision-making Tree system carries out logic and matches with contradistinction system；
Step (3)：Real-time flue tube data are obtained by electronic sensor, and is transmitted to decision tree system, decision tree Fire tube is obtained after system repeatedly training corrode low value probability P high；
Step (4)：Decision tree system judges that fire tube corrodes the size of low value probability P high, if P is more than 0.8, illustrates pot Stove fire pipe corrodes, and result is transferred to console by decision tree system, and console provides alarm；If P is less than 0.8, say Bright flue tube is normal, and console will not provide alarm；
Step (5)：After boiler staff obtains the alarm that console sends, the actual fire tube situation of boiler is carried out Confirm, if it is confirmed that rear flue tube normally then illustrates decision tree system misjudgment, now boiler staff will correctly tie Fruit inputs to contradistinction system, and decision tree system is corrected after now contradistinction system is matched with decision tree system logic again；If Flue tube corrosion then illustrates decision tree system correct judgment after confirmation；
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 flue tube corrosion 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 fire tube The situation of corrosion so that staff family obtains an accurate fire tube corrosion 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 flue tube corrosion, 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 flue tube based on decision tree system corrodes method for early warning, it is characterised in that comprise the following steps：
Step (1), acquisition boiler room environment and boiler operating parameter data A, then flue tube corrosion critical value B is obtained, according to Mutual pace of learning in data A and critical value BCorrespondence go out error rate table t, by the numerical quantization in error rate table t be 0-1 it Between decimal after set up fire tube corrosion error rate table T；
Step (2)：Fire tube in step (1) corrodes error rate table T as decision tree system skeleton, sets up decision tree system System, while the historical data for obtaining staff's artificial judgment flue tube corrosion low value high sets up contradistinction system, by decision tree System carries out logic and matches with contradistinction system；
Step (3)：Real-time flue tube data are obtained by electronic sensor, and is transmitted to decision tree system, decision tree system Fire tube is obtained after repetition training and corrodes low value probability P high；
Step (4)：Decision tree system judges that fire tube corrodes the size of low value probability P high, if P is more than 0.8, illustrates boiler fire Pipe corrodes, and result is transferred to console by decision tree system, and console provides alarm；If P is less than 0.8, pot is illustrated Stove fire pipe is normal, and console will not provide alarm；
Step (5)：After boiler staff obtains the alarm that console sends, the actual fire tube situation of boiler is carried out really Recognize, if it is confirmed that rear flue tube normally then illustrates decision tree system misjudgment, now boiler staff is by correct result Contradistinction system is inputed to, decision tree system is corrected after now contradistinction system is matched with decision tree system logic again；If really Recognize rear flue tube corrosion and then 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 flue tube corrosion condition is no longer needed.
2. a kind of flue tube based on decision tree system according to claim 1 corrodes method for early warning, it is characterised in that The formula of decision tree system meets in step (2)：
dX S d t = ( 1 - f p ) b H X B H - k h ( X S X B H K X + X S X B H ) ( S O K O H + S O ) X B H ;
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.
CN201611239788.7A 2016-12-28 2016-12-28 Method for early warning of boiler fire tube corrosion based on decision tree system Pending CN106709605A (en)

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Application Number Priority Date Filing Date Title
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Citations (6)

* Cited by examiner, † Cited by third party
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 Forecasting Method in conjunction with BP neutral net Yu two parameters of Weibull

Patent Citations (6)

* Cited by examiner, † Cited by third party
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 Forecasting Method in conjunction with BP neutral net Yu two parameters of Weibull

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