CN108985642A - Railcar fault mode risk recognition methods - Google Patents

Railcar fault mode risk recognition methods Download PDF

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CN108985642A
CN108985642A CN201810840559.3A CN201810840559A CN108985642A CN 108985642 A CN108985642 A CN 108985642A CN 201810840559 A CN201810840559 A CN 201810840559A CN 108985642 A CN108985642 A CN 108985642A
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fault mode
assumptions
risks
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risk
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CN108985642B (en
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李磊
韦强
施俊庆
王瑞萍
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Zhejiang Normal University CJNU
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Abstract

The present invention relates to a kind of railcar fault mode risk recognition methods.Method includes the following steps: (1) q experts assess the risks and assumptions 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) pass throughCalculation risk factor fjWeight wj；(5) pass throughCalculate fault mode FMiRelative to fault mode FMoDominance δ (FMi,FMo)；(6) the overall advantage degree of each fault mode is calculated(7) according to the overall advantage degree of above-mentioned acquired each fault mode

Description

Railcar fault mode risk recognition methods
Technical field
The present invention relates to field of traffic safety, more particularly to railcar fault mode risk recognition methods.
Background technique
Subway has the characteristics that large conveying quantity, punctual, fireballing, to occupy as urban public transport important component People's travelling, shopping, commuting etc. provide transportation service.Vehicle is the important carrier of subway transport, and vehicle trouble often will cause column Vehicle is late, influences subway normal operation order.To ensure metro operation order and safety, the failure mould of railcar component is assessed Formula risk is important, this facilitates Measuring error personnel's rational maintenance measure, and to the higher failure mould of risk Formula and related component are paid close attention to, while also contributing to determining the main object that vehicle itself is improved or designed.Therefore, Railcar fault mode evaluation of hazard grade is necessary, and is of great significance to the reliability for improving subway circulation.
Currently, generally by failure model and effect analysis method, (full name in English is Failure Mode and Effect Analysis, abbreviation FMEA) railcar fault mode risk assessed.Currently, FMEA technique study, there are two A deficiency: 1) assuming that policymaker is rational, establish on the basis of expected utility theory, however, studies have shown that, Policymaker is unlikely to be rational in the decision process of reality, and there are certain deviations for the decision and rational expectations made； 2) in when using language message as assessed value, only consider the ambiguity of language message, ignore the randomness of language message.These are dropped The low accuracy of assessment result.
Summary of the invention
Based on this, it is necessary in view of the above problems, providing a kind of railcar fault mode risk recognition methods, the party Method combines interactive multiple criteria decision making (MCDM) (full name in English: an acronym in Portuguese of interactive and Multicriteria decision making, referred to as: TODIM) and cloud model, and the accuracy of assessment result can be improved.
A kind of railcar fault mode risk recognition methods, which comprises the following steps:
(1) q experts assess the risks and assumptions of each fault mode of subway, wherein by kth position expert to subway Vehicle trouble model F MiRisks and assumptions fjThe language assessment value assessed is expressed asK=1,2 ..., q；I=1, 2,…,m；J=1,2 ..., n；Q, m, n are positive integer；
(2) the language assessment value that will be obtainedBe converted to corresponding cloud assessed valueThe cloud assessed valueCloud number it is special Sign is expressed asWhereinIt is expected,For entropy,For super entropy；
(3) by the cloud assessed value of q expertsArithmetic average is carried out, group's cloud assessed value is obtainedWherein, the group Body cloud assessed valueCloud numerical characteristic be expressed as (Ex 'ij,En′ij,He′ij), wherein Ex 'ijFor expectation, En 'ijFor entropy, He 'ij For super entropy；
(4) pass through formula (1) calculation risk factor fjWeight wj, specifically:
In formula (1), Gj=1-Hj,
Wherein, GjFor risks and assumptions fjThe degree of deviation, HjFor fault mode FMiRisks and assumptions fjComentropy, comentropy HjIt is worth smaller, then degree of deviation GjIt is bigger；ForThe score value obtained according to scoring function S；
(5) fault mode FM is calculated by formula (2)iRelative to fault mode FMoDominance δ (FMi,FMo),
In formula (2), o=1,2 ..., m,
Indicate fault mode FMiRisks and assumptions fjRelative to fault mode FMoRisks and assumptions fj's Dominance,
Wherein, wjr=wj/wr, wjrFor risks and assumptions fjRelative to risks and assumptions frRelative weighting, wjIndicate risks and assumptions fjWeight,
θ is the attenuation coefficient of loss, and θ > 0；
(6) the overall advantage degree of each fault mode is calculated by formula (3)Wherein,
(7) according to the overall advantage degree of above-mentioned acquired each fault modeTo identify the risk of each fault mode Degree.
In the above method, the language assessment value of the fault mode risks and assumptions of subway given by q expert is converted first At 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；TODIM method is recycled, the overall advantage degree of the risks and assumptions of each fault mode is calculatedSize；Finally Accordingly to the overall advantage degree of the risks and assumptions of each fault modeIt is ranked up, according toIt is bigger, fault mode The bigger principle of risk finds out crucial fault mode, finally identifies each fault mode risk.This method is based on TODIM And cloud model and realize, can risk that is objective and accurately determining each fault mode.
Specific embodiment
The present invention provides a kind of railcar fault mode risk recognition methods.It the described method comprises the following steps:
(1) q experts assess the risks and assumptions of each fault mode of subway, wherein by kth position expert to subway Fault mode FMiRisks and assumptions fjThe language assessment value assessed is expressed asK=1,2 ..., q；I=1, 2,…,m；J=1,2 ..., n；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's cloud assessed value is obtainedWherein, the group Body cloud assessed valueCloud numerical characteristic be expressed as (Ex 'ij,En′ij,He′ij), wherein Ex 'ijFor expectation, En 'ijFor entropy, He 'ij For super entropy；
(4) pass through formula (1) calculation risk factor fjWeight wj, specifically:
In formula (1), Gj=1-Hj,
Wherein, GjFor risks and assumptions fjThe degree of deviation, HjFor fault mode FMiRisks and assumptions fjComentropy, comentropy HjIt is worth smaller, then degree of deviation GjIt is bigger.
ForAccording to the score value that scoring function S is obtained, i.e.,Wherein, A indicates any one Cloud assessed value, S (A) indicate the score value of A, (αl,βl) indicate A water dust, N indicate water dust number；
(5) fault mode FM is calculated by formula (2)iRelative to fault mode FMoDominance δ (FMi,FMo),
In formula (2), o=1,2 ..., m,
Indicate fault mode FMiRisks and assumptions fjRelative to fault mode FMoRisks and assumptions fj's Dominance,
Wherein, wjr=wj/wr, wjrFor risks and assumptions fjRelative to risks and assumptions frRelative weighting, wjIndicate risks and assumptions fjWeight,
θ is the attenuation coefficient of loss, and θ > 0；
(6) the overall advantage degree of each fault mode is calculated by formula (3)Wherein,
(7) according to the overall advantage degree of above-mentioned acquired each fault modeIdentify the wind of each fault mode Dangerous degree.
In step (7), according to the overall advantage degree of fault modeIt is bigger, the bigger original of fault mode risk Then, to the overall advantage degree of each fault modeSequence, to determine the risk of each fault mode.
1 illustrates by the following examples:
Embodiment 1
During metro operation, it is more universal that door device failure occurs for railcar.It unites according to metro operation company of the city Y Meter: nearly the 40% of vehicle trouble is accounted in the average car door failure of all operating lines in 2016.Car door failure influences subway column Vehicle normally runs order, leads to Train delay, influences subsequent train operation, in addition, door device reliability also direct relation To the personal safety of passenger.The present embodiment is directed to rail transportation operation company of the city Y railcar car door fault mode, chooses car door The higher component of failure rate has " gating device EDCU ", " travel switch S1", " nut subcomponent ", " taking door frame ", the 4 base part master There are 10 kinds of fault modes, car door fault mode is shown in Table 1.
1 car door fault mode of table
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 assess 10 fault modes of railcar, in assessment vehicle trouble mode harm Generation degree, severity, difficult inspection three risks and assumptions of degree, expert T are considered when spendingkTo fault mode FMiRisks and assumptions fjLanguage Assessed value is expressed asEvery expert TkThe language Comment gathers Z=used when assessing vehicle trouble mode risks and assumptions Extremely low (EL), and it is very low (VL), it is lower (ML), it is medium (M), it is higher (MH), it is very high (VH), high (EH).The language that expert provides It says assessed value, is shown in Table 2.
Step (2), using the language assessment value of table 3 and the mapping table of cloud assessed value, the language assessment value that will be obtainedBe converted to corresponding cloud assessed valueObtained cloud assessed valueIt is shown in Table 4.
2 expert linguistic assessed value of table
The mapping table of table 3 language assessment value and cloud assessed value
The cloud assessed value of 4 expert of table
Step (3), it is assumed that the weight of every expert be it is equal, utilize formulaTo cloud assessed valueArithmetic average is carried out, group's cloud assessed value is obtainedGroup's cloud assessed valueCloud numerical characteristic be expressed as (Ex'ij,En'ij,He'ij), it is shown in Table 5.
5 group's cloud assessed value of table
Step (4) utilizes formula (1) calculation risk factor fjWeight wj, w1=0.324, w2=0.337, w2= 0.339。
Step (5) calculates fault mode FM by formula (2)iRelative to fault mode FMoDominance δ (FMi,FMo)。
Enable δ=[δ (FMi,FMo)]m×m
Step (6) calculates the overall advantage degree of each fault mode by formula (3)
The overall advantage degree of obtained each fault modeIt is as follows:
Step (7), determines the risk of each fault mode.Bigger according to overall advantage degree, fault mode risk is bigger Principle, therefore, the sequence of the risk of each fault mode is as follows: FM2> FM3> FM6> FM9> FM5> FM4> FM7> FM10 > FM8> FM1.It is found that fault mode FM2Risk it is maximum.
Each technical characteristic of embodiment described above can be combined arbitrarily, for simplicity of description, 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, all should be considered as described in this specification.
The embodiments described above only express several embodiments of the present invention, and 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 protection of the invention Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.

Claims (2)

1. a kind of railcar fault mode risk recognition methods, which comprises the following steps:
(1) q experts assess the risks and assumptions of each fault mode of subway, wherein by kth position expert to railcar Fault mode FMiRisks and assumptions fjThe language assessment value assessed is expressed asK=1,2 ..., q；I=1, 2,…,m；J=1,2 ..., n；Q, m, n are positive integer；
(2) the language assessment value that will be obtainedBe converted to corresponding 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's cloud assessed value is obtainedWherein, group's cloud Assessed valueCloud numerical characteristic be expressed as (Ex 'ij,En′ij,He′ij), wherein Ex 'ijFor expectation, En 'ijFor entropy, He 'ijIt is super Entropy；
(4) pass through formula (1) calculation risk factor fjWeight wj, specifically:
In formula (1), Gj=1-Hj,
Wherein, GjFor risks and assumptions fjThe degree of deviation, HjFor fault mode FMiRisks and assumptions fjComentropy, comentropy HjValue It is smaller, degree of deviation GjIt is bigger；ForThe score value obtained according to scoring function S；
(5) fault mode FM is calculated by formula (2)iRelative to fault mode FMoDominance δ (FMi,FMo),
In formula (2), o=1,2 ..., m,
Indicate fault mode FMiRisks and assumptions fjRelative to fault mode FMoRisks and assumptions fjAdvantage Degree,
Wherein, wjr=wj/wr, wjrFor risks and assumptions fjRelative to risks and assumptions frRelative weighting, wjIndicate risks and assumptions fj's Weight,
θ is the attenuation coefficient of loss, and θ > 0；
(6) the overall advantage degree of each fault mode is calculated by formula (3)Wherein,
(7) according to the overall advantage degree of above-mentioned acquired each fault modeTo identify the risk of each fault mode.
2. railcar fault mode risk recognition methods as described in claim 1, which is characterized in that basis in step (7) The overall advantage degree of fault modeIt is bigger, the bigger principle of fault mode risk, to the totality of each fault mode DominanceSequence, to determine the risk of each fault mode.
CN201810840559.3A 2018-07-27 2018-07-27 Subway vehicle fault mode risk degree identification method Active CN108985642B (en)

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Publication number Priority date Publication date Assignee Title
CN113065260A (en) * 2021-04-19 2021-07-02 绍兴文理学院 Electromagnet potential failure mode and effect analysis method based on TODIM method
CN113065260B (en) * 2021-04-19 2023-11-14 绍兴文理学院 Method for analyzing potential failure mode and effect of electromagnet based on TODIM method

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