CN106774266A - A kind of boiler superheater early warning method for failure based on decision tree system - Google Patents
A kind of boiler superheater early warning method for failure based on decision tree system Download PDFInfo
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- CN106774266A CN106774266A CN201611239790.4A CN201611239790A CN106774266A CN 106774266 A CN106774266 A CN 106774266A CN 201611239790 A CN201611239790 A CN 201611239790A CN 106774266 A CN106774266 A CN 106774266A
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- Prior art keywords
- superheater
- decision tree
- boiler
- tree system
- failure
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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
- G05B23/0243—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 model based detection method, e.g. first-principles knowledge model
- G05B23/0245—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 model based detection method, e.g. first-principles knowledge model based on a qualitative model, e.g. rule based; if-then decisions
- G05B23/0248—Causal models, e.g. fault tree; digraphs; qualitative physics
Abstract
The invention discloses a kind of boiler superheater early warning method for failure based on decision tree system, comprise the following steps：Step (1), acquisition boiler room environment and boiler operating parameter data A, then boiler superheater failure critical value B is obtained, set up superheater failure error rate table T；Step (2)：Decision tree system is set up, contradistinction system is set up, decision tree system and contradistinction system are carried out into logic matches；Step (3)：Electronic sensor obtains real-time boiler superheater data transfer to decision tree system, obtains superheater failure low value probability P high；Step (4)：If P is more than 0.8, console provides alarm；Step (5)：Boiler staff is confirmed after obtaining the alarm that console sends, if it is confirmed that failure occurs in rear boiler superheater then illustrates decision tree system misjudgment, amendment decision tree system.The present invention realizes the automatization judgement of boiler superheater failure.
Description
Technical field
The invention belongs to early warning technology field, more particularly to a kind of boiler superheater failure based on decision tree system is pre-
Alarm method.
Background technology
Current domestic each superheater early warning failure system is provided with electronic sensor prompt system.Traditional electronic sensor
Device principle is, in the low value high that superheater fails, to be perceived by electronic sensor and timely feed back to the numerical value of each section
Central control system.Work points out to learn the low value high that boiler superheater fails by the picture and text of central control system.But due to generator tube
High temperature, the corrosivity of stove water, a certain degree of influence is caused on electronic sensor so that superheater failure value of feedback on make
Into wrong estimate, or there is falsity, cause major accident occur with the judgement for causing boiler staff generation mistake.And
Sensitivity electronic sensor high it is expensive, replacing is difficult, and is replaced as frequently as so that producing family's very headache.So at present
Domestic superheater early warning failure system cannot accurately react the superheater of boiler and fail low value high.Most electronics before this
Sensor produces electrification using electrochemical principle to the free metal ion in water, and superheater is pointed out by the transmission of electric signal
The low value high of failure.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 mistake based on decision tree system
Hot device early warning method for failure, can immediate correction electronic sensor under circumstances data error, remind boiler staff
The situation of superheater failure so that staff obtains an accurate superheater failure conditions to ensure boiler normal table
Operation, to extend the life-span for using of electronic sensor, reduce boiler maintenance cost, realize boiler superheater failure from
Dynamicization judges that 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 superheater early warning failure side based on decision tree system
Method, comprises the following steps：
Step (1), acquisition boiler room environment and boiler operating parameter data A, then obtain boiler superheater failure critical value
B, the mutual pace of learning in data A and critical value BCorrespondence goes out error rate table t, by the numerical quantization in error rate table t
To set up superheater failure error rate table T after the decimal between 0-1；
Step (2)：Superheater in step (1) fails error rate table T as decision tree system skeleton, sets up decision-making
Tree system, while the historical data for obtaining staff's artificial judgment boiler superheater failure low value high sets up contradistinction system, will
Decision tree system carries out logic and matches with contradistinction system；
Step (3)：Real-time boiler superheater data are obtained by electronic sensor, and is transmitted to decision tree system, decision-making
Superheater failure low value probability P high is obtained after tree system repeatedly training；
Step (4)：Decision tree system judges the size of superheater failure low value probability P high, if P is more than 0.8, illustrates
There is failure in boiler superheater, 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 superheater is normal, console will not provide alarm；
Step (5)：After boiler staff obtains the alarm that console sends, the actual superheater situation of boiler is entered
Row confirms that, if it is confirmed that rear boiler superheater normally then illustrates 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 failure occurs in rear boiler superheater then illustrates 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 superheater failure conditions.
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 superheater of waking up fails so that staff family obtains an accurate superheater failure conditions, comes
Ensure the operation of boiler normal table, to extend the life-span for using of electronic sensor, reduce the maintenance cost of boiler, realize pot
The automatization judgement of stove superheater failure, 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 superheater early warning method for failure based on decision tree system that the present invention is provided, including
Following steps：
Step (1), acquisition boiler room environment and boiler operating parameter data A, then obtain boiler superheater failure critical value
B, the mutual pace of learning in data A and critical value BCorrespondence goes out error rate table t, by the numerical quantization in error rate table t
To set up superheater failure error rate table T after the 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, superheater etc..
Step (2)：Superheater in step (1) fails error rate table T as decision tree system skeleton, sets up decision-making
Tree system, while the historical data for obtaining staff's artificial judgment boiler superheater failure low value high sets up contradistinction system, will
Decision tree system carries out logic and matches with contradistinction system；
Step (3)：Real-time boiler superheater data are obtained by electronic sensor, and is transmitted to decision tree system, decision-making
Superheater failure low value probability P high is obtained after tree system repeatedly training；
Step (4)：Decision tree system judges the size of superheater failure low value probability P high, if P is more than 0.8, illustrates
There is failure in boiler superheater, 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 superheater is normal, console will not provide alarm；
Step (5)：After boiler staff obtains the alarm that console sends, the actual superheater situation of boiler is entered
Row confirms that, if it is confirmed that rear boiler superheater normally then illustrates 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 failure occurs in rear boiler superheater then illustrates 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 superheater failure conditions.
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 overheat
The situation of device failure so that staff family obtains an accurate superheater failure conditions to ensure boiler normal table
Operation, to extend the life-span for using of electronic sensor, reduces the maintenance cost of boiler, realizes the automatic of boiler superheater failure
Change and judge, 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 superheater early warning method for failure 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 obtain boiler superheater failure critical value B, root
According to the 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 superheater failure error rate table T；
Step (2)：Superheater in step (1) fails 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 boiler superheater failure 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 superheater data are obtained by electronic sensor, and is transmitted to decision tree system, decision tree system
Superheater failure low value probability P high is obtained after system repetition training；
Step (4)：Decision tree system judges the size of superheater failure low value probability P high, if P is more than 0.8, illustrates boiler
There is failure in superheater, and result is transferred to console by decision tree system, and console provides alarm；If P is less than 0.8,
Then explanation boiler superheater is normal, and console will not provide alarm；
Step (5)：After boiler staff obtains the alarm that console sends, the actual superheater situation of boiler is carried out really
Recognize, if it is confirmed that rear boiler superheater 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
There is failure then explanation decision tree system correct judgment in boiler superheater after confirmation；
Step (6)：Repeat step (3)-(5), so constantly circulation constantly corrects decision tree system until decision tree system judges
Accurately, staff's artificial judgment superheater failure conditions are no longer needed.
2. a kind of boiler superheater early warning method for failure based on decision tree system according to claim 1, its feature exists
In 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.
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| CN201611239790.4A CN106774266A (en) | 2016-12-28 | 2016-12-28 | A kind of boiler superheater early warning method for failure based on decision tree system |
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Citations (6)
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| 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 |
| CN102435893A (en) * | 2011-11-04 | 2012-05-02 | 国电南京自动化股份有限公司 | Oil-immersed transformer fault diagnosis method based on self-adaptive genetic algorithm |
| CN103309342A (en) * | 2012-03-15 | 2013-09-18 | 华北计算机系统工程研究所 | Safety verification scheme aiming at industrial control system |
| WO2014022154A1 (en) * | 2012-08-02 | 2014-02-06 | Siemens Corporation | Building a failure-predictive model from message sequences |
| US20150339586A1 (en) * | 2015-07-31 | 2015-11-26 | Brighterion, Inc. | Method for calling for preemptive maintenance and for equipment failure prevention |
| 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 |
-
2016
- 2016-12-28 CN CN201611239790.4A patent/CN106774266A/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 |
| CN102435893A (en) * | 2011-11-04 | 2012-05-02 | 国电南京自动化股份有限公司 | Oil-immersed transformer fault diagnosis method based on self-adaptive genetic algorithm |
| CN103309342A (en) * | 2012-03-15 | 2013-09-18 | 华北计算机系统工程研究所 | Safety verification scheme aiming at industrial control system |
| WO2014022154A1 (en) * | 2012-08-02 | 2014-02-06 | Siemens Corporation | Building a failure-predictive model from message sequences |
| US20150339586A1 (en) * | 2015-07-31 | 2015-11-26 | Brighterion, Inc. | Method for calling for preemptive maintenance and for equipment failure prevention |
| 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 |
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Application publication date: 20170531 |