CN108764748A - Subway fault mode risk recognition methods based on information axiom and cloud model - Google Patents
Subway fault mode risk recognition methods based on information axiom and cloud model Download PDFInfo
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- CN108764748A CN108764748A CN201810569931.1A CN201810569931A CN108764748A CN 108764748 A CN108764748 A CN 108764748A CN 201810569931 A CN201810569931 A CN 201810569931A CN 108764748 A CN108764748 A CN 108764748A
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Abstract
The subway fault mode risk recognition methods based on information axiom and cloud model that the present invention relates to a kind of.This approach includes the following steps：(1) q experts assess the risk of each fault mode of subway；(2) the language assessment value that will be obtainedBe converted to cloud assessed value；(3) by the cloud assessed value of q expertsArithmetic average is carried out, group's cloud assessed value is obtained；(4) weight of each risks and assumptions is calculated；(5) cloud weighting group assessed value is calculated；(6) the information computing method for utilizing information axiom, calculates the risks and assumptions gross information content of each fault mode；(7) according to the risks and assumptions gross information content of above-mentioned acquired each fault mode
Description
Technical field
The present invention relates to field of traffic safety, more particularly to the subway fault mode wind based on information axiom and cloud model
Dangerous degree recognition methods.
Background technology
City railway vehicle is the carrier that passenger rides, and running environment closing, handling capacity of passengers are big, and vehicle trouble causes non-
Normal operation will cause larger inconvenience to the trip of passenger, once causing accident even generates larger personnel and property loss.
With city rail traffic route, vehicle increase and the complexity of vehicle itself, brought more to the service work of vehicle
Big challenge.For the acceptance of certain class fault mode, probability of happening, severity etc., especially for certain critical components, to the greatest extent
Fault mode risk, which may accurately be weighed, to be very important.
For the assessment of fault mode risk, many scholars are studied using multiple attributive decision making method, using level
Analytic approach (AHP), Network Analysis Method (ANP), grey correlation methods assess fault mode risk.Document 1 is to document 3
(document 1 is DAVIDSON G G, LABIB A W.Learning from failures:design improvements
using a multiple criteria decision-making process[J].Journal of Aerospace
Engineering,2003,217(4):207-216；Document 2 is Abdelgawad M, Fayek A R.Risk management
in the construction industry using combined fuzzy FMEA and fuzzy AHP[J]
.Journal of Construction Engineering&Management,2010,136(9):1028-1036；Document 3 is
CHEN Jihkuang,Lee Yucheng.Risk priority evaluated by ANP in failure mode and
Effects analysis [J] .Quality Tools&Techniques, 2007,11 (4) .1-6.) it is based on fuzzy number or grey
Uncertainty in number expression assessed value, but it is not objectively insufficient enough in the presence of assessment, and in AHP/ANP, due to assessing specially
Family's personal view preference and uncertainty, construction comparator matrix is relatively difficult, and there are the low deficiencies of Evaluation accuracy for grey correlation.
Invention content
Based on this, it is necessary to for the problem that assessed value is not objective enough and Evaluation accuracy is low in assessment, provide a kind ofly
Iron fault mode risk recognition methods, this method combines information axiom and cloud model, and objectivity and accuracy can be improved.
A kind of subway fault mode risk recognition methods based on information axiom and cloud model comprising following steps：
(1) q experts assess the risk of each fault mode of subway, wherein by kth position expert to subway
The language assessment value that j-th of risks and assumptions is assessed in i-th of fault mode is expressed as Q, m, n are positive integer；
(2) the language assessment value that will be obtainedBe converted to cloud assessed valueThe cloud assessed valueCloud numerical characteristic indicate
ForWhereinIt is expected,For entropy,For super entropy；
(3) by the cloud assessed value of q expertsArithmetic average is carried out, group cloud assessed value y' is obtainedij, wherein it should
Group cloud assessed value y'ijCloud numerical characteristic be expressed as (Ex'ij,En'ij,He'ij),
(4) weight of each risks and assumptions is calculated, specially：
Q first expert assesses the weight of each risks and assumptions, obtains the assessed value of risks and assumptions weight, and will
The assessed value of risks and assumptions weight is converted to the cloud assessed value of risks and assumptions weight, then finds out q experts to each risks and assumptions
The average value of the average value of the cloud assessed value of weight, the cloud assessed value of j-th of risks and assumptions weight of wherein q expert couple is expressed asCalculation formula it is as follows：
Wherein,For the cloud assessed value of j-th of risks and assumptions weight of kth position expert couple,
Then it is normalized, obtains the weight of each risks and assumptions, wherein the weight of j-th of risks and assumptions indicates
For wj, wjNormalized process be：S is scoring function；
(5) cloud weighting group assessed value is calculatedWherein,Cloud numerical characteristic be expressed as It is logical
It crossesIt is calculated；
(6) the risks and assumptions gross information content of each fault mode is calculatedWherein,
I in formulaijIndicate each risks and assumptions information content of each fault mode, IijFrom profit evaluation model index or cost type index
Aspect, the information computing method based on information axiom are calculated；
(7) according to the risks and assumptions gross information content of above-mentioned acquired each fault modeTo identify the wind of each fault mode
Dangerous degree.
In the above method, the language assessment value that expert obtains the fault mode evaluation of hazard grade of subway is converted into first
Cloud assessed value；Then group's cloud assessed value is averagely obtained to the cloud assessed value of q experts；And calculate each risks and assumptions
Weight；The information computing method for recycling information axiom, determines the information content of each fault mode of subwaySize；Last evidence
This information content to each fault modeIt is ranked up, according toSmaller, the bigger principle of fault mode risk finds out key
Fault mode, finally identify each fault mode risk.This method realized based on information axiom and cloud model, can it is objective and
Accurately determine the risk of each fault mode.
Specific implementation mode
The present invention provides a kind of subway fault mode risk recognition methods based on information axiom and cloud model.The side
Method includes the following steps：
(1) q experts assess the risk of each fault mode of subway, wherein by kth position expert to subway
The language assessment value that j-th of risks and assumptions is assessed in i-th of fault mode is expressed as Q, m, n are positive integer；
(2) the language assessment value that will be obtainedBe converted to cloud assessed valueThe cloud assessed valueCloud numerical characteristic indicate
ForWhereinIt is expected,For entropy,For super entropy；
(3) by the cloud assessed value of q expertsArithmetic average is carried out, group cloud assessed value y' is obtainedij, wherein it should
Group cloud assessed value y'ijCloud numerical characteristic be expressed as (Ex'ij,En'ij,He'ij),
(4) weight of each risks and assumptions is calculated, specially：
Q first expert assesses the weight of each risks and assumptions, obtains the assessed value of risks and assumptions weight, and will
The assessed value of risks and assumptions weight is converted to the cloud assessed value of risks and assumptions weight, then finds out q experts to each risks and assumptions
The average value of the average value of the cloud assessed value of weight, the cloud assessed value of j-th of risks and assumptions weight of wherein q expert couple is expressed asCalculation formula it is as follows：
Wherein,For the cloud assessed value of j-th of risks and assumptions weight of kth position expert couple,
Then it is normalized, obtains the weight of each risks and assumptions, wherein the weight of j-th of risks and assumptions indicates
For wj, wjNormalized process be：
Wherein S is scoring function, i.e.,A indicates that any one cloud assessed value, S (A) indicate the score of A
Value, (αl,βl) indicate A water dust, N indicate water dust number；
(5) cloud weighting group assessed value is calculatedWherein,Cloud numerical characteristic be expressed asIt is logical
Formula (4) is crossed to be calculated：
(6) the risks and assumptions gross information content of each fault mode is calculated by formula (5)Wherein,
I in formula (5)ijIndicate each risks and assumptions information content of each fault mode, IijFrom profit evaluation model index or cost type
In terms of index, the information computing method based on information axiom is calculated；
(7) according to the gross information content of above-mentioned acquired each fault modeTo identify the risk of each fault mode.
In step (6), each risks and assumptions information content I of each fault modeij, from profit evaluation model index or cost type index
Aspect is calculated.In terms of profit evaluation model index, IijIt is obtained by following formula (6)：
Wherein, RespectivelyRoot
The score value accordingly obtained according to scoring function S.
In terms of cost type index, IijIt is obtained by following formula (7)：
Wherein, RespectivelyRoot
The score value accordingly obtained according to scoring function S.
Illustrate below by way of a specific embodiment 1：
Embodiment 1
A kind of failure that door device failure is more universal occurs during Subway Train Operation in Existed Subway, the present embodiment is directed to Y
Its Vehicular door fault mode risk of rail transportation operation business valuation studies.
Step (1) invites 3 domain expert { T1,T2,T3It is respectively from railcar guarantee department managers, production
One line class monitor and group leader, scientific research institutions expert, to 4 fault modes of railcar：Car door shows red point (FM1), car door display system
Mistake (FM2), cab door failure (FM3), barrier monitoring starts (FM4) risk assessed.In assessment vehicle trouble mould
Generation degree f is considered when formula risk1, severity f2, difficult inspection degree f3Three risks and assumptions.Expert TkTo vehicle trouble model F Mi's
Risks and assumptions fjThe language assessment value assessed is expressed asIt is assumed that expert TkTo vehicle trouble pattern risks and assumptions into
Row assessment when use language Comment gathers S=extremely low (EL), it is very low (VL), it is relatively low (L), it is medium (M), it is higher (H), it is very high
(VH), high (EH) }.The language assessment value that expert evaluates, is shown in Table 1.
Step (2), using the mapping table of the language assessment value and cloud assessed value of table 2, the language assessment value that will be obtainedBe converted to corresponding cloud assessed valueObtained cloud assessed valueIt is shown in Table 3.
1 expert linguistic assessed value of table
The mapping table of table 2 language assessment value and cloud assessed value
The cloud assessed value of 3 expert of table
Step (3), it is assumed that the weight of every expert is equal, utilizes formulaTo cloud assessed valueArithmetic average is carried out, group cloud assessed value y' is obtainedij, group cloud assessed value y'ijCloud numerical characteristic be expressed as (Ex'ij,
En'ij,He'ij), it is shown in Table 4.
4 group's cloud assessed value of table
Step (4), calculates the weight of each risks and assumptions；
Q first expert assesses the weight of each risks and assumptions, and the assessed value for obtaining risks and assumptions weight (is shown in Table
5) assessed value and cloud assessed value mapping table for, then utilizing 6 risks and assumptions weight of table, by the assessed value of risks and assumptions weight
Be converted to the cloud assessed value (being shown in Table 7) of risks and assumptions weight.The average value of the cloud assessed value of the risks and assumptions weight is found out again,
The average value of the cloud assessed value of j-th of risks and assumptions weight of middle q expert couple is expressed asCalculation formula it is as follows：
Wherein,For the cloud assessed value of j-th of risks and assumptions weight of kth position expert couple.
The language assessment value of 5 risks and assumptions weight of table
The assessed value of 6 risks and assumptions weight of table and cloud assessed value correspondence
The cloud assessed value of 7 risks and assumptions weight of table
Secondly, formulaIt is normalized.Specifically,
It indicatesWater dust, N indicate water dust number.N=10000 in the present embodiment, water dust are generated by positive Normal Cloud algorithm.And it obtains
To the weight w of each risks and assumptionsj, obtain w1=0.268, w2=0.429, w3=0.303.
Step (5) considers the weight w of risks and assumptionsj, by formulaCalculate cloud weighting group assessed value
The results are shown in Table 8.
8 cloud of table weights group's assessed value
Step (6) calculates the risks and assumptions gross information content of each fault mode using the information computing method of information axiom
According to formula (6)：Calculate each risks and assumptions information content I of each fault modeijSee
Table 9.Specifically,It indicatesWater dust, N indicate water dust number；
It indicatesWater dust, N indicate water dust number；It indicatesWater dust, N indicate water dust
Number.N=10000 in the present embodiment, water dust are generated by positive Normal Cloud algorithm.
Each risks and assumptions information content I of 9 each fault mode of tableij
Formula (5) is recycled, the risks and assumptions gross information content of each fault mode is finally obtained
Step (7), determines the risk of each fault mode.ReferenceIt is smaller, fault mode wind
The bigger principle of dangerous degree, therefore, the risk sequence of each fault mode are as follows：FM3> FM4> FM1> FM2.It is found that failure
Model F M3Risk it is maximum.
Each technical characteristic of embodiment described above can be combined arbitrarily, to keep description succinct, not to above-mentioned reality
It applies all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, it is all considered to be the range of this specification record.
Several embodiments of the invention above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the protection of the present invention
Range.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (4)
1. a kind of subway fault mode risk recognition methods based on information axiom and cloud model, which is characterized in that including with
Lower step：
(1) q experts assess the risk of each fault mode of subway, wherein by kth position expert to i-th of failure of subway
The language assessment value that j-th of risks and assumptions is assessed in pattern is expressed as
Q, m, n are positive integer；
(2) the language assessment value that will be obtainedBe converted to cloud assessed valueThe cloud assessed valueCloud numerical characteristic be expressed asWhereinIt is expected,For entropy,For super entropy；
(3) by the cloud assessed value of q expertsArithmetic average is carried out, group cloud assessed value y is obtainedi'j, wherein the group
Cloud assessed value yi'jCloud numerical characteristic be expressed as (Exi'j,Eni'j,Hei'j),
(4) weight of each risks and assumptions is calculated, specially：
Q first expert assesses the weight of each risks and assumptions, obtains the assessed value of risks and assumptions weight, and by risk
The assessed value of Factor Weight is converted to the cloud assessed value of risks and assumptions weight, then finds out q experts to each risks and assumptions weight
Cloud assessed value average value, the average value of the cloud assessed value of j-th of risks and assumptions weight of wherein q expert couple is expressed asCalculation formula it is as follows：
Wherein,For the cloud assessed value of j-th of risks and assumptions weight of kth position expert couple,
Then it is normalized, obtains the weight of each risks and assumptions, wherein the weight of j-th of risks and assumptions is expressed as wj,
wjNormalized process be：S is scoring function；
(5) cloud weighting group assessed value is calculatedWherein,Cloud numerical characteristic be expressed as Pass throughIt is calculated；
(6) the risks and assumptions gross information content of each fault mode is calculatedWherein,
I in formulaijIndicate each risks and assumptions information content of each fault mode, IijIn terms of profit evaluation model index or cost type index,
Information computing method based on information axiom is calculated；
(7) according to the risks and assumptions gross information content of above-mentioned acquired each fault modeTo identify the risk of each fault mode.
2. the subway fault mode risk recognition methods based on information axiom and cloud model as described in claim 1, feature
It is, each risks and assumptions information content I of each fault modeijIt is calculated in terms of profit evaluation model index, it is specific as follows：
Wherein, RespectivelyAccording to score
The score value that function S is accordingly obtained.
3. the subway fault mode risk recognition methods based on information axiom and cloud model as described in claim 1, feature
It is, each risks and assumptions information content I of each fault modeijIt is calculated in terms of cost type index：
Wherein, RespectivelyAccording to score
The score value that function S is accordingly obtained.
4. the subway fault mode risk recognition methods based on information axiom and cloud model as described in claim 1, feature
It is, according to risks and assumptions gross information content in step (7)It is smaller, the bigger principle of fault mode risk, to each failure
The risks and assumptions gross information content of patternSequence, to determine the risk of each fault mode.
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| CN103810533A (en) * | 2013-08-15 | 2014-05-21 | 国家电网公司 | Cloud-model-based power distribution network fault risk identification method |
| CN105302089A (en) * | 2014-07-17 | 2016-02-03 | 广州市地下铁道总公司 | Urban rail transit power supply operation safety production management system and management method |
| CN107480915A (en) * | 2017-09-15 | 2017-12-15 | 中国地质大学（武汉） | A kind of cloud model URBAN EARTHQUAKE endangers methods of risk assessment, equipment and storage device |
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