CN107895194A - A kind of nuclear power plant's main coolant system method for diagnosing faults - Google Patents
A kind of nuclear power plant's main coolant system method for diagnosing faults Download PDFInfo
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Abstract
The present invention relates to a kind of nuclear power plant's main coolant system method for diagnosing faults, model construction module, BPA makers, evidence fusion module and decision-making diagnostor are sequentially connected in order, the data that the model of module construction is calculated by BPA makers are used as the input of evidence fusion module, and result is merged out by evidence fusion module and is sent to decision-making diagnostor, result of determination is finally exported by decision-making diagnostor.Evidence using failure symptom as identification target, fusion results failure judgement type is obtained with the method for combining evidences, the present invention reaches from the angle of evidence to the uncertain analytical table that carries out of fault diagnosis, hypothesis collection is constantly reduced by the accumulation of evidence, and " will not know " and " uncertain " distinguishes, so that diagnostic result is more objective, accurate.BPA is generated using Triangular Fuzzy Number, is easy to analyze by the way that concrete numerical value is quantitatively calculated, is calculated relatively simple；Secondly there is stronger flexibility, contributes to analysis personnel to carry out decision-making.
Description
Technical field
The present invention relates to a kind of fault diagnosis technology, more particularly to a kind of nuclear power plant's main coolant system fault diagnosis side
Method.
Background technology
Nuclear energy is the safe and clean and efficient energy.China is economical to meet electricity needs, Optimization of Energy Structure, promotion
Sustainable development, just Nuclear Power Development industry energetically.At present, nuclear power generating sets 24 are being built, 2654.9 ten thousand kilowatts of installed capacity, built
Scale the first in the world, account for the whole world and building the 37.77% of nuclear power generating sets installed capacity.Nuclear Safety is Jiao of concern all the time
Point, the security of nuclear power plant, the safety of surrounding resident and nuclear power staff should be able to be ensured solidly, so must be maximum
Degree reduces the possibility that potential Nuclear Power Accident occurs.Study and improve nuclear power fault diagnosis system, be to improve nuclear power plant's operation
The needs of reliability, play an important role and meaning to Nuclear Safety and economical operation.In nuclear power system, main coolant system is
One of wherein important part, it is also known as reactor coolant loop, is by reactor, cooling medium pump, voltage-stablizer, steaming
Vapour generator and pipeline, valve press some closure cooling loops in parallel of its capacity composition.Its main function is cooling heap
Core and by heat transmission caused by reactor core to steam generator to produce steam, while can effectively be prevented as second barrier
Radioactive substance leaks out.
Fault diagnosis system is the system for ensureing that Nuclear Safety stable operation is important, and it is the knowledge to equipment multiple faults pattern
Other process, comprising substantial amounts of uncertain during this.Traditional fault diagnosis technology oneself through with ripe theoretical foundation and
Some actual operating experiences, but in nuclear power plant's main coolant system, not only species is various but also failure is former for the failure of equipment
Because different.Paper《Nuclear power plant's main coolant system distributed diagnostics technical research》It is proposed a kind of god of feature based extraction
Distributed type fault diagnosis method through network, but this method can not accurately describe when each sign parameter takes different value diagnostic result it
Between difference, thus can not preferably reflect influence of the change to diagnostic result of sign parameter, this be unfavorable for operator certainly
Plan supports that this problem is widely present again in actual main coolant system fault diagnosis, it would be highly desirable to is solved.
The content of the invention
The present invention be directed in nuclear power plant's main coolant system, not only species is various but also failure cause for the failure of equipment
It is different, cause fault diagnosis to be difficult to the problem of accurate, it is proposed that a kind of nuclear power plant's main coolant system method for diagnosing faults, can be compared with
Accurately to describe change of the fault diagnosis confidence level with sign parameter value, relation and work between effective faults and sign
Go out accurately judgement and decision-making.
The technical scheme is that：A kind of nuclear power plant's main coolant system method for diagnosing faults, specifically includes following step
Suddenly：
1) corresponding relation that model construction module is established between sign and failure：Set fault type and its corresponding sign
Million, defective space model is established, obtains the corresponding relation between sign and failure；
2) BPA is generated by basic probability assignment function BPA makers：First, using the minimum value of failure symptom parameter, most
Good value, maximum set up triangle ambiguity function, on this basis, the specific measured value of sign of input are compared, root
The basic probability assignment function of each fault type is generated according to BPA generating algorithms；
3) BPA for each failure symptom for being generated BPA makers according to sign measured value using evidence fusion module
Merged, obtain the BPA after a fusion, and this BPA is converted into probability distribution, in order to subsequently carry out decision-making；
4) decision-making diagnostor failure judgement type, certainty value is exported：The probability distribution that evidence fusion module is obtained inputs
Into decision-making diagnostor, maximum probability person is identified fault type, and provides the confidence level of the conclusion, is maximum
The probable value of fault mode.
BPA makers are made up of following device in the step 2)：
(1) historical data base input unit, maximum, minimum and the optimal normal value of sign parameter are inputted；
(2) sign parameter input device, each sign measured value of parameters, i.e. real time data are inputted；
(3) membership function generating means, suitable membership function is generated according to the model of module construction, and base is calculated
The distribution of this probability function；
BPA makers generation BPA methods are as follows：
Establish Triangular Fuzzy Number：Longitudinal axis μASign measured value is represented, three point X successively wherein on abscissaij1, Xij0, Xij2Point
The optimal normal value of minimum, the optimal and maximum normal value, wherein sign parameter of some failure symptom parameter Biao Shi not corresponded to
Xij0Corresponding longitudinal axis sign measured value is 1, is triangular apex, establishes this failure symptom Triangular Fuzzy Number function, build in this approach
Found all failure symptom Triangular Fuzzy Number functions；
Such as fault type framework of identification Θ={ F1, F2, F3, F4, N }, wherein F1, F2, F3, F4For 4 kinds of fault types, N is just
Often operation, if certain sign measured value of parameters is Xi, and the parameter is failure FiTypical sign, XiIt is under the jurisdiction of target FiDegree
It is expressed as μi, the degree for being under the jurisdiction of other three kinds of failures is μi', the degree for being under the jurisdiction of normal condition N is μN, wherein μNValue be Xi
Value substitute into Triangular Fuzzy Number Xij1, Xij0, Xij2What is obtained afterwards is subordinate to angle value yN, calculated respectively by following three formula and be respectively subordinate to angle value：
It is subordinate to angle value by 3 above again to be normalized, the BPA for establishing this measured value is as follows：
Wherein Fa、FbAnd FcRespectively remove FiOuter other three kinds of failures.
The beneficial effects of the present invention are：Nuclear power plant's main coolant system method for diagnosing faults of the present invention, by failure symptom
As the evidence of identification target, fusion results failure judgement type is obtained with the method for combining evidences, it is of the invention from the angle of evidence
Spend and the uncertain analytical table that carries out of fault diagnosis is reached, constantly reduce hypothesis collection by the accumulation of evidence, and " will not know "
" uncertain " distinguishes, so that diagnostic result is more objective, accurate.BPA is generated using Triangular Fuzzy Number, passes through quantitative scoring
Calculation obtains concrete numerical value and is easy to analyze, and calculates relatively simple；Secondly there is stronger flexibility, contributes to analysis personnel to be determined
Plan.
Brief description of the drawings
Fig. 1 is nuclear power plant's main coolant system method for diagnosing faults flow chart of the present invention；
Fig. 2 is BPA maker structure charts；
Fig. 3 is that the triangle used in the present invention obscures functional arrangement.
Embodiment
A kind of nuclear power plant's main coolant system method for diagnosing faults flow chart as shown in Figure 1, comprises the following steps：
1st, model construction module is vertical corresponding relation between sign and failure：Set fault type and its corresponding sign
Million, defective space model is established, obtains the corresponding relation between sign and failure；
2nd, BPA is generated by BPA (basic probability assignment function) maker：First, using the minimum value of failure symptom parameter,
Optimum value, maximum set up triangle ambiguity function, and on this basis, the specific measured value of sign of input is compared,
The basic probability assignment function of each fault type is generated according to BPA generating algorithms；
3rd, the BPA for each failure symptom for being generated BPA makers according to sign measured value using evidence fusion module
Merged, obtain the BPA after a fusion, and this BPA is converted into probability distribution, in order to subsequently carry out decision-making；
4th, decision-making diagnostor failure judgement type, certainty value is exported.The probability distribution that evidence fusion module is obtained inputs
Into decision-making diagnostor, maximum probability person is identified fault type, and provides the confidence level of the conclusion, is maximum
The probable value of fault mode.
Model construction module, BPA makers, evidence fusion module and decision-making diagnostor are sequentially connected in order, module structure
The data that the model built is calculated by BPA makers are used as the input of evidence fusion module, and are melted by evidence fusion module
Close out result and be sent to decision-making diagnostor, result of determination is finally exported by decision-making diagnostor.
Wherein BPA makers following device as shown in Fig. 2 be made up of：
1) historical data base input unit, maximum, minimum and the optimal normal value of sign parameter are inputted；
2) sign parameter input device, each sign measured value of parameters, i.e. real time data are inputted；
3) membership function generating means, suitable membership function is generated according to the model of module construction, and base is calculated
The distribution of this probability function.
When certain sign parameter value is nearer apart from its optimal normal value, the possibility that corresponding failure occurs is lower,
The possibility of system normal operation is bigger；Conversely, the possibility for then corresponding to failure generation is bigger, the possibility of system normal operation
Property is lower.Based on such thought, BPA is generated as follows：
Establish Triangular Fuzzy Number (Xij1, Xij0, Xij2) as shown in figure 3, longitudinal axis μARepresent sign measured value, wherein abscissa
On three point X successivelyij1, Xij0, Xij2Minimum, the optimal and maximum normal value of some corresponding failure symptom parameter is represented respectively, its
The optimal normal value X of middle sign parameterij0Corresponding longitudinal axis sign measured value is 1, is triangular apex, establishes this failure symptom three
Angle fuzzy number function, all failure symptom Triangular Fuzzy Number functions are established in this approach.Known present system fault type is distinguished
Know framework Θ={ F1, F2, F3, F4, N }, wherein F1, F2, F3, F4For 4 kinds of fault types, N is normal operation.If certain sign parameter
Measured value is Xi, and the parameter is failure FiTypical sign, XiIt is under the jurisdiction of target FiDegree be expressed as μi, it is under the jurisdiction of other three
The degree of kind failure is μi', the degree for being under the jurisdiction of normal condition N is μN.Wherein μNValue be XiValue substitute into Triangular Fuzzy Number
(Xij1, Xij0, Xij2) after obtain be subordinate to angle value yN(see accompanying drawing 3).Calculated respectively by following three formula and be respectively subordinate to angle value：
It is subordinate to angle value by 3 above again to be normalized, the BPA for establishing this measured value is as follows：
Wherein Fa、FbAnd FcRespectively remove FiOuter other three kinds of failures.
The BPA of generation is input into evidence fusion module again to be merged by evidence, and is output to decision-making
Diagnostor is converted into probability distribution by new probability formula of gambling and carries out decision-making judgement.
Below in conjunction with accompanying drawing, the present invention will be further described：
Initially set up defective space model, F={ F1, F2, F3, F4, N } and (F1One left loop steam generator tube rupture
Accident, F2One right loop steam generator tube rupture accident, F3Left loop main steam line rupture accident in one containment,
F4One left loop primary pipe rupture accident and N- normal operations), each sign parameter is having one just during system normal operation
Normal span, failure symptom parameter as shown in table 1 and normal range (NR) table.Feature extraction is carried out to each sign parameter again,
Abnormal condition is represented with characteristic value 1,0 represents normal condition, and each measured value of parameters is changed into the characteristic parameter of { 0,1 }, can
Therefrom to find out the corresponding relation between several failures and corresponding typical sign, malfunction monitoring parameter as shown in table 2 carries with feature
Take the corresponding relation of sign and failure shown in list and table 3.
Table 1
Table 2
Table 3
Obviously when certain sign parameter value is nearer apart from its optimal normal value, the possibility that corresponding failure occurs is got over
Low, the possibility of system normal operation is bigger；Conversely, the possibility for then corresponding to failure generation is bigger, system normal operation
Possibility is lower.The BPA generation models for establishing this paper on this basis are as follows:
Establish Triangular Fuzzy Number (Xij1, Xij0, Xij2) as shown in Figure 3, wherein Xij1, Xij0, Xij2Sign ginseng is represented respectively
Several minimum, optimal and maximum normal values.Known present system framework of identification Θ={ F1, F2, F3, F4, N }, if certain sign is joined
Several measured values is Xi, and the parameter is failure FiTypical sign, XiIt is under the jurisdiction of target FiDegree be expressed as μi, it is under the jurisdiction of another
The degree of outer three kinds of failures is μi', the degree for being under the jurisdiction of normal condition N is μN.Wherein μNValue be XiValue substitute into triangle obscure
Number (Xij1, Xij0, Xij2) after obtain be subordinate to angle value yN(see accompanying drawing 3).Calculated respectively by following three formula and be respectively subordinate to angle value：
It is subordinate to angle value by 3 above again to be normalized, the BPA for establishing this measured value is as follows：
Wherein Fa、FbAnd FcRespectively remove FiOuter other three kinds of failures.
Above BPA is merged with Dempster combined methods again, obtains fusion results.Dempster combinatorial formulas are such as
Under：
Wherein
Wherein, Ai、Bj、ClIt is burnt first in evidence theory framework of identification, m1(Ai) it is burnt first A in first BPAiIt is basic
Probability assigns assignment, m2(Bj) it is burnt first B in second BPAjBasic probability assignment assignment, m3(Cl) it is burnt in the 3rd BPA
First ClBasic probability assignment assignment.
Fault type is identified judgement finally by decision-making diagnostor.Assuming that m is the BPA functions on Θ, then its is right
The gambling probability conversion formula BetP answeredm:Θ → [0,1] is defined as follows：
Wherein, | A | it is set A gesture (i.e. the number of element in A).
After obtaining probability distribution, it is the failure identified to find out wherein maximum probability person, and exports certainty value.
Wherein w is fault mode when basic probability assignment (BPA) is converted into probability distribution, i.e. w belongs to F1, F2, F3, F4
And N.A is BPA Jiao's member, can be any one subset in framework of identification power set, i.e. A is set { F1, F2, F3, F4, N }
Random subset.M { A } is burnt first A basic probability assignment assignment.mIt is empty setBasic probability assignment assignment.
Found by verifying, method of the invention can not only obtain correct diagnostic result, and can preferably reflect
Difference when each sign parameter takes different value between diagnostic result, it is easier to find out the change of diagnostic result confidence level, Ke Yiwei
The fault diagnosis research in actual nuclear power system provides certain reference and reference function from now on.
Claims (2)
1. a kind of nuclear power plant's main coolant system method for diagnosing faults, it is characterised in that specifically comprise the following steps：
1) corresponding relation that model construction module is established between sign and failure：Fault type and its corresponding sign are set, is built
Vertical defective space model, obtains the corresponding relation between sign and failure；
2) BPA is generated by basic probability assignment function BPA makers：First, the minimum value of failure symptom parameter, optimal is utilized
Value, maximum set up triangle ambiguity function, and on this basis, the specific measured value of sign of input is compared, according to
BPA generating algorithms generate the basic probability assignment function of each fault type；
3) BPA for each failure symptom for being generated BPA makers according to sign measured value using evidence fusion module is carried out
Fusion, the BPA after a fusion is obtained, and this BPA is converted into probability distribution, in order to subsequently carry out decision-making；
4) decision-making diagnostor failure judgement type, certainty value is exported：The probability distribution that evidence fusion module obtains is input to certainly
In plan diagnostor, maximum probability person is identified fault type, and provides the confidence level of the conclusion, as maximum failure
The probable value of pattern.
2. nuclear power plant's main coolant system method for diagnosing faults according to claim 1, it is characterised in that in the step 2)
BPA makers are made up of following device：
(1) historical data base input unit, maximum, minimum and the optimal normal value of sign parameter are inputted；
(2) sign parameter input device, each sign measured value of parameters, i.e. real time data are inputted；
(3) membership function generating means, suitable membership function is generated according to the model of module construction, and be calculated substantially general
The distribution of rate function；
BPA makers generation BPA methods are as follows：
Establish Triangular Fuzzy Number：Longitudinal axis μASign measured value is represented, three point X successively wherein on abscissaij1, Xij0, Xij2Table respectively
Show the optimal normal value X of minimum, the optimal and maximum normal value for corresponding to some failure symptom parameter, wherein sign parameterij0It is right
It is 1 to answer longitudinal axis sign measured value, is triangular apex, establishes this failure symptom Triangular Fuzzy Number function, establish institute in this approach
Faulty sign Triangular Fuzzy Number function；
Such as fault type framework of identification Θ={ F1, F2, F3, F4, N }, wherein F1, F2, F3, F4For 4 kinds of fault types, N is normal fortune
OK, if certain sign measured value of parameters is Xi, and the parameter is failure FiTypical sign, XiIt is under the jurisdiction of target FiDegree represent
For μi, the degree for being under the jurisdiction of other three kinds of failures is μi', the degree for being under the jurisdiction of normal condition N is μN, wherein μNValue be XiValue
Substitute into Triangular Fuzzy Number Xij1, Xij0, Xij2What is obtained afterwards is subordinate to angle value yN, calculated respectively by following three formula and be respectively subordinate to angle value：
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Wherein Fa、FbAnd FcRespectively remove FiOuter other three kinds of failures.
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| CN110718312A (en) * | 2019-09-18 | 2020-01-21 | 上海电力大学 | System and method for stopping safety injection in heat transfer pipe rupture accident |
| CN111312420A (en) * | 2020-03-02 | 2020-06-19 | 上海交通大学 | Fault diagnosis method and device |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108830382A (en) * | 2018-05-31 | 2018-11-16 | 上海电力学院 | A kind of DCS method for evaluating reliability based on dynamic evidence network |
| CN110718312A (en) * | 2019-09-18 | 2020-01-21 | 上海电力大学 | System and method for stopping safety injection in heat transfer pipe rupture accident |
| CN110718312B (en) * | 2019-09-18 | 2023-08-04 | 上海电力大学 | System and method for terminating safety injection under heat transfer pipe fracture accident |
| CN111312420A (en) * | 2020-03-02 | 2020-06-19 | 上海交通大学 | Fault diagnosis method and device |
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