CN108985642B - Subway vehicle fault mode risk degree identification method - Google Patents

Subway vehicle fault mode risk degree identification method Download PDF

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CN108985642B
CN108985642B CN201810840559.3A CN201810840559A CN108985642B CN 108985642 B CN108985642 B CN 108985642B CN 201810840559 A CN201810840559 A CN 201810840559A CN 108985642 B CN108985642 B CN 108985642B
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CN108985642A (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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    • 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
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    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • 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
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Abstract

The invention relates to a subway vehicle fault mode risk degree identification method. The method comprises the following steps: (1) evaluating risk factors of each fault mode of the subway by q experts; (2) the language evaluation value obtained
Figure DDA0001745478900000011
Conversion into cloud evaluation values
Figure DDA0001745478900000012
(3) The cloud evaluation value of q experts
Figure DDA0001745478900000013
Carrying out arithmetic mean to obtain a group cloud evaluation value
Figure DDA0001745478900000014
(4) By passing
Figure DDA0001745478900000015
Calculating a risk factor fjWeight w ofj(ii) a (5) By passing
Figure DDA0001745478900000016
Calculating failure mode FMiWith respect to failure mode FMoDegree of dominance δ (FM)i,FMo) (ii) a (6) Calculating the overall dominance of each failure mode
Figure DDA0001745478900000017
(7) According to the obtained overall dominance degree of each fault mode

Description

Subway vehicle fault mode risk degree identification method
Technical Field
The invention relates to the field of traffic safety, in particular to a subway vehicle fault mode risk degree identification method.
Background
The subway is an important component of urban public transport, has the characteristics of large transportation capacity, punctuality and high speed, and provides transport services for resident travel, shopping, commuting and the like. The vehicle is an important carrier for subway transportation, and the vehicle fault often causes train late points and influences the normal operation order of the subway. In order to guarantee subway operation order and safety, it is important to evaluate the failure mode risk degree of subway vehicle components, which is helpful for detection and maintenance personnel to reasonably make maintenance measures, and pay attention to the failure mode with higher risk degree and related components, and is also helpful for determining the main objects of improvement or design of the vehicle. Therefore, the subway vehicle fault mode risk degree evaluation is necessary, and has important significance for improving the reliability of subway operation.
Currently, the risk of the Failure Mode of the metro vehicle is generally evaluated by a Failure Mode and impact Analysis method (FMEA for short). Currently, there are two disadvantages to FMEA method research: 1) the decision maker is assumed to be completely rational and is based on expected utility theory, however, researches show that the decision maker cannot be completely rational in the real decision making process, and the made decision has certain deviation from the rational expectation; 2) when the language information is used as the evaluation value, only the ambiguity of the language information is considered, and the randomness of the language information is ignored. These all reduce the accuracy of the evaluation results.
Disclosure of Invention
Therefore, it is necessary to provide a subway vehicle failure mode risk degree identification method, which combines an interactive multi-criteria decision (TODIM) and a cloud model to improve the accuracy of the evaluation result.
A subway vehicle fault mode risk degree identification method is characterized by comprising the following steps:
(1) evaluating risk factors of fault modes of the subway by q experts, wherein the fault mode FM of the subway vehicle is evaluated by the kth expertiRisk factor fjThe language evaluation value obtained by evaluation is expressed as
Figure BDA0001745478890000021
k is 1,2, …, q; i is 1,2, …, m; j is 1,2, …, n; q, m and n are positive integers;
(2) the language evaluation value obtained
Figure BDA0001745478890000022
Conversion into corresponding cloud evaluation values
Figure BDA0001745478890000023
The cloud evaluation value
Figure BDA0001745478890000024
Is represented as
Figure BDA0001745478890000025
Wherein
Figure BDA0001745478890000026
In the interest of expectation,
Figure BDA0001745478890000027
in order to be the entropy of the signal,
Figure BDA0001745478890000028
is the super entropy;
(3) the cloud evaluation value of q experts
Figure BDA0001745478890000029
Carrying out arithmetic mean to obtain a group cloud evaluation value
Figure BDA00017454788900000210
Wherein the group cloud evaluation value
Figure BDA00017454788900000211
Is represented as (Ex'ij,En′ij,He′ij) Wherein Ex'ijTo expect, En'ijIs entropy, He'ijIs the super entropy;
Figure BDA00017454788900000212
(4) calculating the risk factor f by equation (1)jWeight w ofjThe method specifically comprises the following steps:
Figure BDA00017454788900000213
in the formula (1), Gj＝1-Hj，
Figure BDA00017454788900000214
Wherein G isjIs a risk factor fjDegree of deviation of (H)jFor fault mode FMiRisk factor fjInformation entropy of (1), information entropy HjThe smaller the value, the smaller the deviation degree GjThe larger;
Figure BDA00017454788900000215
is composed of
Figure BDA00017454788900000216
Obtaining a score value according to a score function S;
(5) calculating the failure mode FM by equation (2)iWith respect to failure mode FMoDegree of dominance δ (FM)i,FMo)，
Figure BDA00017454788900000217
In formula (2), o is 1, 2.., m,
Figure BDA0001745478890000031
Figure BDA0001745478890000032
indicating a failure mode FMiRisk factor fjWith respect to failure mode FMoRisk factor fjThe degree of superiority of (a) is,
wherein, wjr＝wj/wr，wjrIs a risk factor fjRelative to the risk factor frRelative weight of, wjRepresents a risk factor fjThe weight of (a) is determined,
Figure BDA0001745478890000033
θ is the attenuation coefficient of the loss, and θ > 0;
(6) calculating the total dominance degree of each fault mode through formula (3)
Figure BDA0001745478890000034
Wherein the content of the first and second substances,
Figure BDA0001745478890000035
(7) according to the obtained overall dominance degree of each fault mode
Figure BDA0001745478890000036
The risk of each failure mode is identified.
Firstly, converting a language evaluation value of a failure mode risk factor of a subway, which is given by a q expert, into a cloud evaluation value; then, averaging the cloud evaluation values of the q experts to obtain a group cloud evaluation value; and calculating the weight of each risk factor; then, the TODIM method is utilized to calculate the overall dominance degree of the risk factors of each fault mode
Figure BDA0001745478890000037
The size of (d); finally, the pair is based onOverall dominance of risk factors for each failure mode
Figure BDA0001745478890000038
Carry out the sorting according to
Figure BDA0001745478890000039
And finding out the key fault mode according to the principle that the larger the fault mode risk degree is, and finally identifying each fault mode risk degree. The method is realized based on the TODIM and the cloud model, and the risk degree of each fault mode can be objectively and accurately determined.
Detailed Description
The invention provides a subway vehicle fault mode risk degree identification method. The method comprises the following steps:
(1) evaluating risk factors of fault modes of the subway by q experts, wherein the fault mode FM of the subway is evaluated by the kth expertiRisk factor fjThe language evaluation value obtained by evaluation is expressed as
Figure BDA0001745478890000041
k is 1,2, …, q; i is 1,2, …, m; j is 1,2, …, n; q, m and n are positive integers;
(2) the language evaluation value obtained
Figure BDA0001745478890000042
Conversion into cloud evaluation values
Figure BDA0001745478890000043
The cloud evaluation value
Figure BDA0001745478890000044
Is represented as
Figure BDA0001745478890000045
Wherein
Figure BDA0001745478890000046
In the interest of expectation,
Figure BDA0001745478890000047
in order to be the entropy of the signal,
Figure BDA0001745478890000048
is the super entropy;
(3) the cloud evaluation value of q experts
Figure BDA0001745478890000049
Carrying out arithmetic mean to obtain a group cloud evaluation value
Figure BDA00017454788900000410
Wherein the group cloud evaluation value
Figure BDA00017454788900000411
Is represented as (Ex'ij,En′ij,He′ij) Wherein Ex'ijTo expect, En'ijIs entropy, He'ijIs the super entropy;
Figure BDA00017454788900000412
(4) calculating the risk factor f by equation (1)jWeight w ofjThe method specifically comprises the following steps:
Figure BDA00017454788900000413
in the formula (1), Gj＝1-Hj，
Figure BDA00017454788900000414
Wherein G isjIs a risk factor fjDegree of deviation of (H)jFor fault mode FMiRisk factor fjInformation entropy of (1), information entropy HjThe smaller the value, the smaller the deviation degree GjThe larger.
Figure BDA00017454788900000415
Is composed of
Figure BDA00017454788900000416
The score value obtained according to the score function S, i.e.
Figure BDA00017454788900000417
Wherein A represents any one of the cloud evaluation values, S (A) represents a score value of A, (alpha)l,βl) The cloud droplets of A are represented, and N represents the number of the cloud droplets;
(5) calculating the failure mode FM by equation (2)iWith respect to failure mode FMoDegree of dominance δ (FM)i,FMo)，
Figure BDA00017454788900000418
In formula (2), o is 1, 2.., m,
Figure BDA0001745478890000051
Figure BDA0001745478890000052
indicating a failure mode FMiRisk factor fjWith respect to failure mode FMoRisk factor fjThe degree of superiority of (a) is,
wherein, wjr＝wj/wr，wjrIs a risk factor fjRelative to the risk factor frRelative weight of, wjRepresents a risk factor fjThe weight of (a) is determined,
Figure BDA0001745478890000053
θ is the attenuation coefficient of the loss, and θ > 0;
(6) calculating the total dominance degree of each fault mode through formula (3)
Figure BDA0001745478890000054
Wherein the content of the first and second substances,
Figure BDA0001745478890000055
(7) according to the obtained overall dominance degree of each fault mode
Figure BDA0001745478890000056
And identifying the risk degree of each fault mode.
In step (7), the overall dominance according to the failure mode
Figure BDA0001745478890000057
The principle that the larger the fault mode risk degree is, the overall dominance degree of each fault mode
Figure BDA0001745478890000058
And sequencing to determine the risk degree of each fault mode.
The following is illustrated by example 1:
example 1
In the subway operation process, the occurrence of door system faults of the subway vehicles is common. According to the statistics of subway operation companies in the city Y: the average door failure of all service lines in 2016 accounts for nearly 40% of vehicle failures. The normal operation order of subway train is influenced in the door trouble, leads to the train late point, influences follow-up train operation, and in addition, door system reliability also directly concerns passenger's personal safety. In the embodiment, for the door failure mode of the subway vehicle of the rail transit operation company in Y city, the parts with higher door failure rate are selected to be ' door controller EDCU ' and ' travel switch S1"," nut subassembly ", and" door carrying frame ", there are mainly 10 failure modes in these 4 kinds of parts, and the door failure mode is seen in table 1.
TABLE 1 vehicle door failure modes
Figure BDA0001745478890000061
Step (1) invite 3 domain experts { T }1,T2,T3The assessment method is characterized in that 10 fault modes of the subway vehicle are respectively obtained from managers of subway vehicle security departments, teams and groups in the production line and experts in scientific research institutions, three risk factors of incidence degree, severity degree and difficulty in detection are considered when the hazard degree of the fault mode of the subway vehicle is assessed, and an expert TkFor fault mode FMiRisk factor fjIs expressed as a language evaluation value of
Figure BDA0001745478890000062
Each expert TkThe language comment set Z adopted when evaluating the vehicle failure mode risk factor is { Extremely Low (EL), Very Low (VL), low (ML), medium (M), high (MH), Very High (VH), Extremely High (EH) }. The language assessment values given by the experts are shown in table 2.
Step (2) of using the correspondence table between the language evaluation value and the cloud evaluation value in table 3 to obtain a language evaluation value
Figure BDA0001745478890000063
Conversion into corresponding cloud evaluation values
Figure BDA0001745478890000064
Derived cloud evaluation value
Figure BDA0001745478890000065
See table 4.
TABLE 2 expert language assessment values
Figure BDA0001745478890000066
Figure BDA0001745478890000071
Table 3 correspondence table between language evaluation value and cloud evaluation value
Figure BDA0001745478890000072
Table 4 cloud evaluation values of experts
Figure BDA0001745478890000073
Figure BDA0001745478890000081
And (3) assuming that the weight of each expert is equal, and using a formula
Figure BDA0001745478890000082
Evaluation value for cloud
Figure BDA0001745478890000083
Carrying out arithmetic mean to obtain a group cloud evaluation value
Figure BDA0001745478890000084
The group cloud evaluation value
Figure BDA0001745478890000085
Is represented as (Ex'ij,En'ij,He'ij) See table 5.
TABLE 5 group cloud assessment values
Figure BDA0001745478890000086
Figure BDA0001745478890000091
Step (4), calculating a risk factor f by using the formula (1)jWeight w ofj，w1＝0.324，w2＝0.337，w2＝0.339。
Step (5), calculating the fault mode FM through the formula (2)iWith respect to failure mode FMoDegree of dominance δ (FM)i,FMo)。
Let δ become [ δ (FM) ]i,FMo)]m×m
Figure BDA0001745478890000092
And (6) calculating the total dominance degree of each fault mode through the formula (3)
Figure BDA0001745478890000093
Overall dominance of each failure mode obtained
Figure BDA0001745478890000094
The following were used:
Figure BDA0001745478890000095
Figure BDA0001745478890000096
and (7) determining the risk degree of each fault mode. According to the principle that the greater the overall dominance degree is, the greater the risk degree of the failure mode is, therefore, the risk degrees of the respective failure modes are ranked as follows: FM2＞FM3＞FM6＞FM9＞FM5＞FM4＞FM7＞FM10＞FM8＞FM1. As can be seen, failure mode FM2The greatest risk.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (2)

1. A subway vehicle fault mode risk degree identification method is characterized by comprising the following steps:
(1) evaluating risk factors of fault modes of the subway by q experts, wherein the fault mode FM of the subway vehicle is evaluated by the k-th expertiRisk factor fjThe language evaluation value obtained by evaluation is expressed as
Figure FDA0003079568610000011
Figure FDA0003079568610000012
q, m and n are positive integers;
(2) the language evaluation value obtained
Figure FDA0003079568610000013
Conversion into corresponding cloud evaluation values
Figure FDA0003079568610000014
The cloud evaluation value
Figure FDA0003079568610000015
Is represented as
Figure FDA0003079568610000016
Wherein
Figure FDA0003079568610000017
Evaluating a value for cloud
Figure FDA0003079568610000018
The desire for a cloud digital feature of (a),
Figure FDA0003079568610000019
evaluating a value for cloud
Figure FDA00030795686100000110
The entropy of the cloud digital signature of (a),
Figure FDA00030795686100000111
evaluating a value for cloud
Figure FDA00030795686100000112
The cloud digital features of (1) hyper-entropy;
(3) the cloud evaluation value of q experts
Figure FDA00030795686100000113
Carrying out arithmetic mean to obtain a group cloud evaluation value
Figure FDA00030795686100000114
Wherein the group cloud evaluation value
Figure FDA00030795686100000115
Is represented as (Ex'ij,En′ij,He′ij) Wherein Ex'ijEvaluating a value for a group cloud
Figure FDA00030795686100000116
Expected of cloud digital feature of, En'ijEvaluating a value for a group cloud
Figure FDA00030795686100000117
Entropy of cloud digital features of, He'ijEvaluating a value for a group cloud
Figure FDA00030795686100000118
The cloud digital features of (1) hyper-entropy;
Figure FDA00030795686100000119
(4) calculating the risk factor f by equation (1)jWeight w ofjThe method specifically comprises the following steps:
Figure FDA00030795686100000120
in the formula (1), Gj＝1-Hj，
Figure FDA00030795686100000121
Wherein G isjIs a risk factor fjDegree of deviation of (H)jFor fault mode FMiRisk factor fjInformation entropy of (1), information entropy HjThe smaller the value, the degree of deviation GjThe larger;
Figure FDA00030795686100000122
is composed of
Figure FDA00030795686100000123
Obtaining a score value according to a score function S;
(5) calculating the failure mode FM by equation (2)iWith respect to failure mode FMoDegree of dominance δ (FM)i,FMo)，
Figure FDA00030795686100000124
In formula (2), o is 1, 2.., m,
Figure FDA0003079568610000021
Figure FDA0003079568610000022
indicating a failure mode FMiRisk factor fjWith respect to failure mode FMoRisk factor fjThe degree of superiority of (a) is,
wherein, wjr＝wj/wr，wjrIs a risk factor fjRelative to the risk factor frRelative weight of, wjRepresents a risk factor fjThe weight of (a) is determined,
Figure FDA0003079568610000023
θ is the attenuation coefficient of the loss, and θ > 0;
(6) calculating the total dominance degree of each fault mode through formula (3)
Figure FDA0003079568610000024
Wherein the content of the first and second substances,
Figure FDA0003079568610000025
(7) according to the obtained overall dominance degree of each fault mode
Figure FDA0003079568610000026
The risk of each failure mode is identified.
2. The subway vehicle fault mode risk identification method as claimed in claim 1, wherein in step (7), the overall dominance degree according to fault mode
Figure FDA0003079568610000027
The principle that the larger the fault mode risk degree is, the overall dominance degree of each fault mode
Figure FDA0003079568610000028
And sequencing to determine the risk degree of each fault mode.
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