CN114547920B - Reliability evaluation method for aircraft structural member assembly deviation simulation model - Google Patents

Reliability evaluation method for aircraft structural member assembly deviation simulation model Download PDF

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CN114547920B
CN114547920B CN202210454640.4A CN202210454640A CN114547920B CN 114547920 B CN114547920 B CN 114547920B CN 202210454640 A CN202210454640 A CN 202210454640A CN 114547920 B CN114547920 B CN 114547920B
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朱永国
石强
邓斌
胡元帆
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Nanchang Hangkong University
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Abstract

The invention discloses a method for evaluating the reliability of an aircraft structural member assembly deviation simulation model, which comprises the following steps: s100: constructing an aircraft structural member assembly simulation model, and collecting assembly deviation simulation data; s200: collecting actually measured data of the assembly deviation based on a physical prototype; s300: calculating the correlation degree of the simulation data and the actually measured data interval number; s400: based on an entropy weight method, giving weight to the credibility of each deviation component; s500: fusing the credibility of the simulation data of each deviation component based on a D-S evidence theory to obtain the credibility of the simulation data of the assembly unit; s600: the reliability of the simulation data of each assembly unit is upwards aggregated based on the aircraft structural member assembly process information, so that the comprehensive reliability evaluation of the aircraft structural member assembly deviation simulation model is realized.

Description

Reliability evaluation method for aircraft structural member assembly deviation simulation model
Technical Field
The invention relates to the technical field of airplane assembly characteristic deviation digital coordination, in particular to a method for evaluating the credibility of an airplane structural component assembly deviation simulation model.
Background
In recent years, with the continuous development of computers and simulation technologies, simulation models are widely applied in the fields of aerospace, military, social economy and the like. Meanwhile, as simulation systems become more complex, reliability evaluation of simulation models becomes a key problem in the field of simulation. Simulation models are essentially approximate abstractions of the real world, with the degree of confidence directly determining the success or failure of a simulation application. Simulation results generated by simulation models lacking sufficient confidence may mislead decision makers and even result in irretrievable situations.
The final quality of the airplane product is directly reflected by the assembly quality of the whole airplane, and the analysis of the assembly deviation is the basis of the control of the overall manufacturing quality of the airplane. Due to the fact that the production batch of the airplane is small, the assembling deviation detection data presents the characteristics of small samples, incomplete information and the like, a large amount of complete detection data of various deviation inputs, transmission and outputs in the assembling process cannot be observed. Therefore, the method for constructing the assembly deviation simulation model to obtain the simulation data expands the deviation detection data and has important significance for the assembly deviation analysis. However, in order to ensure the validity and reliability of the simulation data, only the reliability of the simulation model is determined to meet the requirements, so that a data base is laid for the analysis of the assembly deviation of the aircraft structural part.
The interval number association degree theory is based on data characteristics, and the degree of proximity between objects can be evaluated from multiple angles and multiple levels. Therefore, the degree of closeness of each deviation component simulation data and the measured data is calculated by using the section number correlation degree, and the evaluation of the reliability of each deviation component of the aircraft structural component assembly deviation simulation model is realized. However, the airplane structural component assembly has the characteristics of multiple deviation components, multi-level assembly and the like, and the reliability of the whole structural component simulation model is difficult to evaluate only by means of the interval number association degree theory.
Disclosure of Invention
The invention aims to solve the technical problems in the prior art and provides a method for evaluating the reliability of an assembly deviation simulation model of an aircraft structural part.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a reliability evaluation method of an aircraft structural component assembly deviation simulation model is used for evaluating the reliability of the aircraft structural component assembly simulation model and comprises the following steps:
s100: constructing an aircraft structural member assembly simulation model, and collecting assembly deviation simulation data;
s200: collecting actually measured data of the assembly deviation based on a physical prototype;
s300: calculating the interval number correlation degree between the simulation data and the measured data;
s400: based on an entropy weight method, giving weight to the credibility of each deviation component;
s500: fusing the credibility of the simulation data of each deviation component based on a D-S evidence theory to obtain the credibility of the simulation data of the assembly unit;
s600: and according to the aircraft structural member assembly process information, the reliability of the simulation data of each assembly unit is upwards aggregated, so that the comprehensive reliability evaluation of the aircraft structural member assembly deviation simulation model is realized.
Preferably, the specific method for collecting the assembly deviation simulation data in step S100 is as follows:
s101: collecting assembly deviation simulation data of airplane structural member and constructing assembly deviation simulation data sequence
Figure 232617DEST_PATH_IMAGE001
：
Figure 87440DEST_PATH_IMAGE002
Wherein the content of the first and second substances,
Figure 54128DEST_PATH_IMAGE003
；lthe number of the assembly units;
Figure 449337DEST_PATH_IMAGE004
；mthe number of the deviation components;nthe number of deviation data;
Figure 201393DEST_PATH_IMAGE005
fitting deviation simulation data for the nth time of the kth deviation component of the ith fitting unit;
s102: converting the assembly deviation simulation data sequence obtained in the step S101 into the number of intervals
Figure 709341DEST_PATH_IMAGE006
：
Figure 914057DEST_PATH_IMAGE007
Wherein the content of the first and second substances,
Figure 112957DEST_PATH_IMAGE008
respectively, deviation simulation data sequence
Figure 968787DEST_PATH_IMAGE009
Minimum and maximum values of;
s103: construction of interval number form assembly deviation simulation data sequence
Figure 899834DEST_PATH_IMAGE010
：
Figure 654163DEST_PATH_IMAGE011
。
Wherein the content of the first and second substances,
Figure 79591DEST_PATH_IMAGE012
the number of simulation data intervals of the mth deviation component of the ith assembly unit;
preferably, the specific method for collecting the assembly deviation measured data in step S200 is as follows:
s201: building a physical prototype of the airplane structural part;
s202: collecting the assembly deviation actual measurement data of the airplane structural member and constructing an assembly deviation actual measurement data sequence
Figure 540659DEST_PATH_IMAGE013
：
Figure 970503DEST_PATH_IMAGE014
；
Wherein the content of the first and second substances,
Figure 399079DEST_PATH_IMAGE015
is as followsiAn assembly unitkA first of deviation componentsnActual measurement data of the secondary assembly deviation;
s203: converting the assembly deviation actual measurement data sequence obtained in the step S202 into interval number
Figure 674203DEST_PATH_IMAGE016
：
Figure 255357DEST_PATH_IMAGE017
；
Wherein the content of the first and second substances,
Figure 276009DEST_PATH_IMAGE018
respectively, deviation measured data sequence
Figure 942614DEST_PATH_IMAGE019
Minimum and maximum values of;
s204: construction of interval number form assembly deviation measured data sequence
Figure 21428DEST_PATH_IMAGE020
：
Figure 706356DEST_PATH_IMAGE021
Wherein the content of the first and second substances,
Figure 415686DEST_PATH_IMAGE022
is as followsiA mounting unitmThe number of actually measured data intervals of each deviation component;
preferably, the specific method for calculating the section number association degree in step S300 is as follows:
s301: taking the assembly deviation simulation data sequence obtained in the step S100 as a comparison sequence, taking the assembly deviation actual measurement data sequence obtained in the step S200 as a reference sequence, and calculating the association degree of the interval number of each deviation component:
Figure 54740DEST_PATH_IMAGE023
wherein the content of the first and second substances,
Figure 874929DEST_PATH_IMAGE024
is as followsiAn assembly unitkThe interval number correlation degree of the simulation data of each deviation component and the actual measurement data,
Figure 227413DEST_PATH_IMAGE025
number of intervals
Figure 356912DEST_PATH_IMAGE026
The distance of (a) to (b),
Figure 794846DEST_PATH_IMAGE027
；
s302: constructing an assembly deviation interval number correlation sequence
Figure 573053DEST_PATH_IMAGE028
：
Figure 45623DEST_PATH_IMAGE029
Wherein the content of the first and second substances,
Figure 96755DEST_PATH_IMAGE030
is as followsiAn assembly unitkThe degree of correlation of the interval number of each deviation component.
Preferably, in step S400, the method of giving a weight to each deviation component reliability includes:
s401: constructing a multi-attribute evaluation matrix according to the deviation actual measurement data collected in the step S200Q：
Figure 208937DEST_PATH_IMAGE031
Wherein the content of the first and second substances,
Figure 433245DEST_PATH_IMAGE032
is as followsiAn assembly unitmA first of deviation componentsnActual measurement data of the secondary assembly deviation;
s402: constructing a weight matrix of each deviation component dataP：
Figure 432425DEST_PATH_IMAGE033
Wherein the content of the first and second substances,
Figure 405191DEST_PATH_IMAGE034
is as followsiAn assembly unitmA deviation component ofnThe weight of the secondary assembly deviation measured data is occupied; the weight calculation formula is:
Figure 817718DEST_PATH_IMAGE035
wherein the content of the first and second substances,
Figure 783400DEST_PATH_IMAGE036
is as followsiAn assembly unitkA deviation component ofjThe weight of the measured data of the minor assembly deviation is occupied;
Figure 151933DEST_PATH_IMAGE037
；mthe number of the deviation components;
Figure 341606DEST_PATH_IMAGE038
；nthe number of deviation data;
s403: solving the entropy of the deviation component information:
Figure 179112DEST_PATH_IMAGE039
wherein the content of the first and second substances,
Figure 745223DEST_PATH_IMAGE040
is as followsiAn assembly unitkThe information entropy of each deviation component;
s404: solving the difference coefficient of the deviation components:
Figure 732377DEST_PATH_IMAGE041
；
wherein the content of the first and second substances,
Figure 30634DEST_PATH_IMAGE042
is as followsiA mounting unitkA coefficient of variance of each deviation component;
s405: solving the weight of the deviation component:
Figure 417753DEST_PATH_IMAGE043
wherein the content of the first and second substances,
Figure 974505DEST_PATH_IMAGE044
is as followsiAn assembly unitkA weight factor for each deviation component;
s406: weighting the interval number relevance according to the deviation component weight obtained in step S405 to obtain the deviation component interval number relevance given with the weight:
Figure 865101DEST_PATH_IMAGE045
wherein the content of the first and second substances,
Figure 599839DEST_PATH_IMAGE046
is as followsiA mounting unitkThe interval number relevance degree of the weight given by each deviation component;
preferably, the method for acquiring the reliability of the simulation data of the assembly unit in step S500 is as follows:
s501: defining an evidence theory identification framework as { credibility meets the requirement, credibility does not meet the requirement, and uncertainty } =
Figure 897090DEST_PATH_IMAGE047
；
S502: according to the relevance degree of the deviation component interval number endowed with the weight obtained in the step S406, a probability distribution matrix under the identification frame is constructedM：
Figure 70582DEST_PATH_IMAGE048
Wherein, the first and the second end of the pipe are connected with each other,
Figure 18947DEST_PATH_IMAGE049
is as followsmAssigning basic probability that the reliability of the deviation component simulation data meets the requirement;
Figure 986903DEST_PATH_IMAGE050
is as followsmAssigning basic probability that the reliability of the simulation data of the deviation components does not meet the requirement;
Figure 269985DEST_PATH_IMAGE051
is as followsmAssigning a basic probability of uncertainty of the simulation data of the deviation components;
s503: and taking the reliability of the simulation data of each deviation component as an evidence, and fusing according to an evidence theory to obtain the reliability of the simulation data of the assembly unit:
Figure 919273DEST_PATH_IMAGE052
wherein the content of the first and second substances,
Figure 784460DEST_PATH_IMAGE053
is as followsiReliability of simulation data of each assembly unit;
Figure 608804DEST_PATH_IMAGE054
is as followsiAn assembly unitmThe reliability of the simulation data of each deviation component is changed into direct sum operation;
s504: according to step S503, the method is obtainediDistributing probability of credibility of simulation data of each assembly unit:
Figure 192232DEST_PATH_IMAGE055
。
preferably, the reliability evaluation method in step S600 includes:
s601: according to the steps S503 and S504, an assembly unit credibility sequence is constructed
Figure 379631DEST_PATH_IMAGE056
：
Figure 551855DEST_PATH_IMAGE057
；
S602: based on the assembly process information, the credibility of the simulation data of each assembly unit is gathered upwards according to the assembly levelObtaining the comprehensive credibility of the simulation model of the assembly deviation of the airplane structural member
Figure 861613DEST_PATH_IMAGE058
：
Figure 870021DEST_PATH_IMAGE059
；
Wherein the content of the first and second substances,
Figure 923427DEST_PATH_IMAGE060
is as followslReliability of simulation data of each assembly unit;
s603: according to the step S602, obtaining the reliability probability distribution of the aircraft structural member assembly deviation simulation model:
Figure 451623DEST_PATH_IMAGE061
。
the invention has the beneficial effects that:
according to the method, an aircraft structural part assembly deviation simulation model reliability evaluation system is constructed by combining an interval number correlation degree theory, an entropy weight method, a D-S evidence theory and the like on the basis of the assembly deviation simulation data acquired by the simulation model and the deviation actual measurement data acquired by the physical prototype.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a simulation data confidence up-aggregation flow for the multi-level assembly of the present invention.
Detailed Description
Reference will now be made in detail to the present preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout.
Referring to fig. 1 to 2, a preferred embodiment of the present invention, an aircraft structural component assembly deviation simulation model reliability evaluation method, for evaluating aircraft structural component assembly simulation model reliability, includes the following steps:
s100: constructing an aircraft structural member assembly simulation model, and collecting assembly deviation simulation data;
s200: collecting actually measured data of the assembly deviation based on a physical prototype;
s300: calculating the interval number correlation degree between the simulation data and the measured data;
s400: based on an entropy weight method, giving weight to the credibility of each deviation component;
s500: fusing the credibility of the simulation data of each deviation component based on a D-S evidence theory to obtain the credibility of the simulation data of the assembly unit;
s600: and according to the aircraft structural member assembly process information, the reliability of the simulation data of each assembly unit is upwards aggregated, so that the comprehensive reliability evaluation of the aircraft structural member assembly deviation simulation model is realized.
As a preferred embodiment of the present invention, it may also have the following additional technical features:
in this embodiment, the specific method for collecting the assembly deviation simulation data in step S100 is as follows:
s101: collecting assembly deviation simulation data of airplane structural member and constructing assembly deviation simulation data sequence
Figure 932283DEST_PATH_IMAGE062
：
Figure 162407DEST_PATH_IMAGE063
Wherein the content of the first and second substances,
Figure 206455DEST_PATH_IMAGE064
；lthe number of the assembly units;
Figure 900742DEST_PATH_IMAGE065
；mthe number of the deviation components;nthe number of deviation data;
Figure 489986DEST_PATH_IMAGE066
is as followsiA mounting unitkA first of deviation componentsnSub-assembly deviation simulation data;
s102: converting the assembly deviation simulation data sequence obtained in the step S101 into interval number
Figure 535302DEST_PATH_IMAGE067
：
Figure 615998DEST_PATH_IMAGE068
Wherein the content of the first and second substances,
Figure 102474DEST_PATH_IMAGE069
respectively, deviation simulation data sequence
Figure 190515DEST_PATH_IMAGE070
Minimum and maximum values of;
s103: constructing interval number form assembly deviation simulation data sequence
Figure 644499DEST_PATH_IMAGE071
：
Figure 843400DEST_PATH_IMAGE072
Wherein the content of the first and second substances,
Figure 184382DEST_PATH_IMAGE073
is as followsiAn assembly unitmA deviation ofThe number of simulation data intervals of the component;
in this embodiment, the specific method for collecting the actually measured data of the assembly deviation in step S200 is as follows:
s201: building a physical prototype of the airplane structural member;
s202: collecting the assembly deviation actual measurement data of the airplane structural member and constructing an assembly deviation actual measurement data sequence
Figure 131741DEST_PATH_IMAGE074
：
Figure 886070DEST_PATH_IMAGE075
Wherein, the first and the second end of the pipe are connected with each other,
Figure 560765DEST_PATH_IMAGE076
is as followsiAn assembly unitkA first of deviation componentsnActual measurement data of the secondary assembly deviation;
s203: converting the assembly deviation actual measurement data sequence obtained in the step S202 into interval number
Figure 84150DEST_PATH_IMAGE077
：
Figure 700945DEST_PATH_IMAGE078
Wherein the content of the first and second substances,
Figure 614675DEST_PATH_IMAGE079
respectively measured data sequence for deviation
Figure 155377DEST_PATH_IMAGE080
Minimum and maximum values of;
s204: construction of interval number form assembly deviation measured data sequence
Figure 218755DEST_PATH_IMAGE081
：
Figure 350659DEST_PATH_IMAGE082
Wherein, the first and the second end of the pipe are connected with each other,
Figure 79581DEST_PATH_IMAGE083
is as followsiAn assembly unitmThe number of actually measured data intervals of each deviation component;
in this embodiment, the specific method for calculating the association degree of the interval number in step S300 includes:
s301: taking the assembly deviation simulation data sequence obtained in the step S100 as a comparison sequence, taking the assembly deviation actual measurement data sequence obtained in the step S200 as a reference sequence, and calculating the association degree of the interval number of each deviation component:
Figure 96078DEST_PATH_IMAGE084
wherein the content of the first and second substances,
Figure 781006DEST_PATH_IMAGE085
is as followsiAn assembly unitkThe section number correlation degree of the simulation data of each deviation component and the actual measurement data,
Figure 287074DEST_PATH_IMAGE086
number of intervals
Figure 440975DEST_PATH_IMAGE087
The distance of (a) to (b),
Figure 323480DEST_PATH_IMAGE088
；
s302: constructing an assembly deviation interval number correlation sequence
Figure 364380DEST_PATH_IMAGE089
：
Figure 244611DEST_PATH_IMAGE090
Wherein, the first and the second end of the pipe are connected with each other,
Figure 682545DEST_PATH_IMAGE091
is as followsiAn assembly unitkThe interval number correlation degree of each deviation component.
In this embodiment, the method for giving a weight to the reliability of each deviation component in step S400 includes:
s401: constructing a multi-attribute evaluation matrix according to the deviation actual measurement data collected in the step S200Q：
Figure 290113DEST_PATH_IMAGE092
Wherein the content of the first and second substances,
Figure 762683DEST_PATH_IMAGE093
is as followsiAn assembly unitmA first of deviation componentsnActual measurement data of the secondary assembly deviation;
s402: constructing a weight matrix of each deviation component dataP：
Figure 548236DEST_PATH_IMAGE094
Wherein the content of the first and second substances,
Figure 994890DEST_PATH_IMAGE095
is a firstiAn assembly unitmA deviation component ofnThe weight of the secondary assembly deviation measured data is occupied; the weight calculation formula is:
Figure 219198DEST_PATH_IMAGE096
wherein the content of the first and second substances,
Figure 139750DEST_PATH_IMAGE097
is as followsiAn assembly unitkA deviation component ofjThe weight of the measured data of the minor assembly deviation is occupied;
Figure 955259DEST_PATH_IMAGE098
；mthe number of deviation components;
Figure 508731DEST_PATH_IMAGE099
；nthe number of deviation data;
s403: solving the entropy of the deviation component information:
Figure 802309DEST_PATH_IMAGE100
wherein the content of the first and second substances,
Figure 141149DEST_PATH_IMAGE101
is as followsiAn assembly unitkThe information entropy of each deviation component;
s404: solving the difference coefficient of the deviation components:
Figure 799663DEST_PATH_IMAGE102
wherein the content of the first and second substances,
Figure 230645DEST_PATH_IMAGE103
is as followsiAn assembly unitkA coefficient of variance of each deviation component;
s405: solving the weight of the deviation component:
Figure 452548DEST_PATH_IMAGE104
wherein the content of the first and second substances,
Figure 754216DEST_PATH_IMAGE105
is as followsiAn assembly unitkA weight factor for each deviation component;
s406: according to the deviation component weight obtained in step S405, a weight is given to the section number association degree, and the deviation component section number association degree given with the weight is obtained:
Figure 786894DEST_PATH_IMAGE106
wherein the content of the first and second substances,
Figure 705171DEST_PATH_IMAGE107
is as followsiAn assembly unitkThe interval number relevance degree of the weight given by each deviation component;
in this embodiment, the method for obtaining the reliability of the simulation data of the assembly unit in step S500 includes:
s501: defining an evidence theory identification framework as { credibility meets the requirement, credibility does not meet the requirement, and uncertainty } =
Figure 760459DEST_PATH_IMAGE108
；
S502: according to the relevance degree of the deviation component interval number endowed with the weight obtained in the step S406, a probability distribution matrix under the identification frame is constructedM：
Figure 323158DEST_PATH_IMAGE109
Wherein the content of the first and second substances,
Figure 385792DEST_PATH_IMAGE110
is as followsmAssigning basic probability that the reliability of the deviation component simulation data meets the requirement;
Figure 650420DEST_PATH_IMAGE111
is as followsmAssigning basic probability that the reliability of the simulation data of the deviation components does not meet the requirement;
Figure 355071DEST_PATH_IMAGE112
is as followsmAssigning a basic probability of uncertainty of the simulation data of the deviation components;
s503: and taking the reliability of the simulation data of each deviation component as an evidence, and fusing according to an evidence theory to obtain the reliability of the simulation data of the assembly unit:
Figure 569015DEST_PATH_IMAGE113
wherein the content of the first and second substances,
Figure 694228DEST_PATH_IMAGE114
is as followsiReliability of simulation data of each assembly unit;
Figure 321518DEST_PATH_IMAGE115
is as followsiAn assembly unitmThe reliability of the simulation data of each deviation component is changed into direct sum operation;
s504: according to step S503, get the secondiAnd (3) distributing reliability probability of simulation data of each assembly unit:
Figure 908489DEST_PATH_IMAGE116
。
in this embodiment, the reliability evaluation method in step S600 includes:
s601: according to the steps S503 and S504, an assembly unit credibility sequence is constructed
Figure 570414DEST_PATH_IMAGE117
：
Figure 161801DEST_PATH_IMAGE118
；
S602: based on the assembly process information, the credibility of the simulation data of each assembly unit is upwards aggregated according to the assembly level to obtain the comprehensive credibility of the assembly deviation simulation model of the airplane structural member
Figure 151754DEST_PATH_IMAGE119
：
Figure 932628DEST_PATH_IMAGE120
；
Wherein the content of the first and second substances,
Figure 806650DEST_PATH_IMAGE121
is as followslReliability of simulation data of each assembly unit;
s603: according to the step S602, obtaining the reliability probability distribution of the aircraft structural member assembly deviation simulation model:
Figure 647567DEST_PATH_IMAGE122
。
the specific embodiment is as follows:
is combined withlAircraft structural component of individual assembly units, each assembly unit being designated as
Figure 124816DEST_PATH_IMAGE123
(ii) a The deviation components for each assembly unit are recorded as
Figure 630752DEST_PATH_IMAGE124
The method for evaluating the reliability of the aircraft structural part assembly deviation simulation model based on the interval number correlation degree and the evidence theory is provided, and is used for evaluating the reliability of the aircraft structural part assembly deviation simulation model, and the method comprises the following steps:
s100: the method for constructing the aircraft structural part assembly simulation model and acquiring the assembly deviation simulation data comprises the following specific steps:
s101: constructing an aircraft structural member assembly deviation simulation model; collecting assembly deviation simulation data of the airplane structural part, and constructing an assembly deviation simulation data sequence:
Figure 1691DEST_PATH_IMAGE125
wherein the content of the first and second substances,
Figure 623296DEST_PATH_IMAGE126
，lthe number of the assembly units;mthe number of deviation components;nthe number of the assembly deviation simulation data;
Figure 446896DEST_PATH_IMAGE127
is a firstiAn assembly unitmA simulation data sequence of the deviation components;
Figure 461250DEST_PATH_IMAGE128
is as followsiAn assembly unitmA first of deviation componentsnSub-assembly deviation simulation data;
s102: converting the assembly deviation simulation data sequence obtained in the step S101 into the number of intervals
Figure 686695DEST_PATH_IMAGE129
：
Figure 541519DEST_PATH_IMAGE130
Wherein the content of the first and second substances,
Figure 977048DEST_PATH_IMAGE131
respectively, deviation simulation data sequence
Figure 903416DEST_PATH_IMAGE132
Minimum and maximum values of;
s103: construction of interval number form assembly deviation simulation data sequence
Figure 858734DEST_PATH_IMAGE133
：
Figure 897840DEST_PATH_IMAGE134
Wherein the content of the first and second substances,
Figure 696032DEST_PATH_IMAGE135
is as followsiAn assembly unitmThe number of simulation data intervals of each deviation component;
s200: collecting actually measured data of the assembly deviation based on a physical prototype; the specific method comprises the following steps:
s201: building a physical prototype of the airplane structural part;
s202: collecting the assembly deviation actual measurement data of the airplane structural member and constructing an assembly deviation actual measurement data sequence
Figure 770298DEST_PATH_IMAGE136
：
Figure 970336DEST_PATH_IMAGE137
Wherein the content of the first and second substances,
Figure 681809DEST_PATH_IMAGE138
，lthe number of the assembly units;mthe number of deviation components;
Figure 311504DEST_PATH_IMAGE139
is as followsiAn assembly unitmA sequence of measured data for each deviation component;
Figure 845254DEST_PATH_IMAGE140
is as followsiAn assembly unitmA first of deviation componentsnActual measurement data of the secondary assembly deviation;
s203: converting the assembly deviation actual measurement data sequence obtained in the step S202 into interval number
Figure 525896DEST_PATH_IMAGE141
：
Figure 893423DEST_PATH_IMAGE142
Wherein the content of the first and second substances,
Figure 400628DEST_PATH_IMAGE143
respectively as deviation simulation data sequences
Figure 597123DEST_PATH_IMAGE144
Minimum and maximum values of;
s204: construction of interval number form assembly deviation measured data sequence
Figure 506173DEST_PATH_IMAGE145
：
Figure 310181DEST_PATH_IMAGE146
Wherein the content of the first and second substances,
Figure 927851DEST_PATH_IMAGE147
is as followsiA mounting unitmThe number of actually measured data intervals of each deviation component;
s300: the specific method for calculating the interval number correlation degree between the simulation data and the measured data comprises the following steps:
s301: taking the assembly deviation simulation data sequence obtained in the step S100 as a comparison sequence, taking the assembly deviation actual measurement data sequence obtained in the step S200 as a reference sequence, and calculating the association degree of the interval number of each deviation component:
Figure 537824DEST_PATH_IMAGE148
wherein the content of the first and second substances,
Figure 442326DEST_PATH_IMAGE149
is as followsiAn assembly unitkThe interval number correlation degree of the simulation data of each deviation component and the actual measurement data,
Figure 479552DEST_PATH_IMAGE150
number of intervals
Figure 148300DEST_PATH_IMAGE151
Number of to interval
Figure 437330DEST_PATH_IMAGE152
The Euclidean distance of (a) is,
Figure 55393DEST_PATH_IMAGE153
；
s302: constructing an assembly deviation interval number correlation sequence
Figure 889619DEST_PATH_IMAGE154
：
Figure 530816DEST_PATH_IMAGE155
Wherein the content of the first and second substances,
Figure 748171DEST_PATH_IMAGE156
is as followsiAn assembly unitkThe degree of correlation of the interval number of each deviation component.
S400: based on an entropy weight method, giving weight to the credibility of each deviation component; the specific method comprises the following steps:
s401: based on a physical prototype, collecting actual measurement data of each deviation component, and constructing a multi-attribute evaluation matrixQ：
Figure 345374DEST_PATH_IMAGE157
Wherein the content of the first and second substances,
Figure 989982DEST_PATH_IMAGE158
is as followsiAn assembly unitmA deviation component ofnActual measurement data of the secondary assembly deviation;
s402: constructing a weight matrix of each deviation component dataP：
Figure 321737DEST_PATH_IMAGE159
Wherein the content of the first and second substances,
Figure 497110DEST_PATH_IMAGE160
is as followsiA mounting unitmA deviation component ofnThe weight of the secondary assembly deviation measured data is occupied; the weight calculation formula is:
Figure 355345DEST_PATH_IMAGE161
wherein the content of the first and second substances,
Figure 780641DEST_PATH_IMAGE162
is as followsiAn assembly unitkA deviation component ofjThe weight of the measured data of the minor assembly deviation is occupied;
Figure 458747DEST_PATH_IMAGE163
；mthe number of deviation components;
Figure 939276DEST_PATH_IMAGE164
；nthe number of deviation data;
s403: based on a weight matrixPSolving deviation component information entropy
Figure 527383DEST_PATH_IMAGE165
：
Figure 513794DEST_PATH_IMAGE166
Wherein the content of the first and second substances,
Figure 305295DEST_PATH_IMAGE167
is as followsiAn assembly unitkThe information entropy of each deviation component;
s404: solving difference coefficient of deviation component based on information entropy of each deviation component
Figure 402564DEST_PATH_IMAGE168
：
Figure 641915DEST_PATH_IMAGE169
Wherein the content of the first and second substances,
Figure 923861DEST_PATH_IMAGE170
is as followsiAn assembly unitkA coefficient of variance of each deviation component; the larger the information entropy, the smaller the variation degree of the deviation component itself, and the smaller the amount of information contained therein, the smaller the difference coefficient,the less the effect on the subject's assessment is;
s405: calculating the weight of each deviation component on the basis of the information entropy and the difference coefficient
Figure 576559DEST_PATH_IMAGE171
：
Figure 87306DEST_PATH_IMAGE172
Wherein the content of the first and second substances,
Figure 774639DEST_PATH_IMAGE173
is a firstiAn assembly unitkWeights of the individual deviation components;
s406: weighting the interval number relevance degree sequence obtained in the step S302 according to each deviation component weight obtained in the step S405, and constructing the interval number relevance degree sequence containing the deviation component weightS i ：
Figure 726021DEST_PATH_IMAGE174
Wherein the content of the first and second substances,
Figure 538119DEST_PATH_IMAGE175
，
Figure 977191DEST_PATH_IMAGE176
and simulating the data interval number correlation degree for the deviation component containing the weight.
S500: fusing the credibility of the simulation data of each deviation component based on a D-S evidence theory to obtain the credibility of the simulation data of the assembly unit; the specific method comprises the following steps:
s501: defining an evidence theory identification framework as { the reliability meets the requirement, the reliability does not meet the requirement, and the uncertainty } =
Figure 378085DEST_PATH_IMAGE177
；
S502: according to the weight obtained in step S406And (4) establishing a probability distribution matrix under an identification framework according to the relevance of the deviation component interval numberM：
Figure 877200DEST_PATH_IMAGE178
Wherein the content of the first and second substances,
Figure 379856DEST_PATH_IMAGE179
is as followsmAssigning basic probability that the reliability of the deviation component simulation data meets the requirement;
Figure 45455DEST_PATH_IMAGE180
is as followsmAssigning basic probability that the reliability of the simulation data of the deviation components does not meet the requirement;
Figure 707381DEST_PATH_IMAGE181
is as followsmAssigning a basic probability of uncertainty of the simulation data of the deviation components;
s503: and taking the reliability of the simulation data of each deviation component as an evidence, and fusing according to an evidence theory to obtain the reliability of the simulation data of the assembly unit:
Figure 252763DEST_PATH_IMAGE182
wherein the content of the first and second substances,
Figure 836191DEST_PATH_IMAGE183
is as followsiReliability of simulation data of each assembly unit;
Figure 7278DEST_PATH_IMAGE184
is as followsiAn assembly unitmThe reliability of the simulation data of each deviation component is changed into direct sum operation;
further, the specific method in step S503 is as follows:
definition ofμProbability distribution of individual deviation component simulation data credibility as
Figure 523710DEST_PATH_IMAGE185
Of 1 atvProbability distribution of individual deviation component simulation data credibility as
Figure 36731DEST_PATH_IMAGE186
. Wherein the content of the first and second substances,
Figure 996203DEST_PATH_IMAGE187
is as followsμThe probability that the individual bias component simulation data confidence meets the requirement,
Figure 580768DEST_PATH_IMAGE188
is as followsμThe probability that the individual bias component simulation data confidence level does not meet the requirement,
Figure 561494DEST_PATH_IMAGE189
is as followsμThe individual bias components simulate the probability of uncertainty in the data confidence.
Figure 573312DEST_PATH_IMAGE190
Is as followsνThe probability that the individual bias component simulation data confidence meets the requirement,
Figure 583862DEST_PATH_IMAGE191
is a firstνThe probability that the individual bias component simulation data confidence level does not meet the requirement,
Figure 581905DEST_PATH_IMAGE192
is a firstνThe individual bias components simulate the probability of uncertainty in the data confidence.
The fusion process of the credibility of the simulation data of the two deviation components according to the evidence theory is as follows:
calculating evidence conflict factors
Figure 72930DEST_PATH_IMAGE193
：
Figure 616169DEST_PATH_IMAGE194
② calculating normalization factor
Figure 927064DEST_PATH_IMAGE195
：
Figure 525536DEST_PATH_IMAGE196
Calculating the probability distribution of the credibility of the deviation component simulation data after evidence fusion:
Figure 995700DEST_PATH_IMAGE197
；
Figure 83742DEST_PATH_IMAGE198
；
Figure 757300DEST_PATH_IMAGE199
。
wherein the content of the first and second substances,
Figure 930703DEST_PATH_IMAGE200
the probability that the credibility meets the requirement after the evidence fusion is carried out on the two deviation components,
Figure 865161DEST_PATH_IMAGE201
the probability that the credibility does not meet the requirement after the evidence fusion is carried out on the two deviation components,
Figure 858524DEST_PATH_IMAGE202
and carrying out evidence fusion on the two deviation components to obtain the probability of uncertainty of the credibility.
S504: according to step S503, obtaining a reliability probability distribution of the simulation data of the assembly unit:
Figure 550537DEST_PATH_IMAGE203
；
s600: and according to the aircraft structural member assembly process information, the reliability of the simulation data of each assembly unit is upwards aggregated, so that the comprehensive reliability evaluation of the aircraft structural member assembly deviation simulation model is realized.
Specifically, as shown in fig. 2, an upward aggregation process of simulation data reliability of multilevel assembly is shown, and according to an assembly relationship of each assembly unit in the assembly process information, the simulation reliability information of each assembly unit is upwards fused by using an evidence synthesis theory, so as to finally obtain the assembly simulation comprehensive reliability of the product. And judging whether the reliability of the aircraft structural part simulation model meets the requirement or not according to the comprehensive reliability.
S601: according to the steps S503 and S504, a credibility sequence of the simulation data of each assembly list is constructed
Figure 553128DEST_PATH_IMAGE204
：
Figure 263464DEST_PATH_IMAGE205
Wherein the content of the first and second substances,
Figure 427729DEST_PATH_IMAGE206
is as followslReliability of simulation data of each assembly unit;
s602: based on the assembly process information, the credibility of the simulation data of each assembly unit is upwards aggregated according to the assembly level to obtain the comprehensive credibility of the aircraft structural member assembly deviation simulation model:
Figure 607038DEST_PATH_IMAGE207
wherein the content of the first and second substances,
Figure 836156DEST_PATH_IMAGE208
is a firstlReliability of simulation data of each assembly unit;
s603: according to the step S602, obtaining the reliability probability distribution of the aircraft structural member assembly deviation simulation model:
Figure 214048DEST_PATH_IMAGE209
。
the method and the device can be used for the digital analysis stages of the aircraft structural member assembly deviation transmission process, the assembly deviation prediction process, the assembly deviation inversion and the like, the evaluation of the reliability of the assembly deviation simulation model is realized, and the simulation model with the reliability meeting the requirement is identified. Such as
Figure 752476DEST_PATH_IMAGE210
The simulation model is accurate and the provided simulation data is reliable and effective. Compared with the traditional reliability evaluation method only depending on test software, the method provided by the embodiment of the invention takes simulation output data and deviation actual measurement data as data bases, and is based on the objective evaluation model simulation reliability such as interval number correlation degree theory, D-S evidence theory and the like, wherein an entropy weight method is fused, so that the difference of each deviation component on the evaluation importance degree is considered, and the accuracy of simulation model evaluation is improved.
The above additional technical features can be freely combined and used in addition by those skilled in the art without conflict.
The above description is only a preferred embodiment of the present invention, and the technical solutions that achieve the objects of the present invention by basically the same means are all within the protection scope of the present invention.

Claims (5)

1. A reliability evaluation method for an aircraft structural member assembly deviation simulation model is characterized by comprising the following steps: the method comprises the following steps:
s100: constructing an aircraft structural member assembly simulation model, and collecting assembly deviation simulation data;
s200: collecting actually measured data of the assembly deviation based on a physical prototype;
s300: calculating the interval number correlation degree between the simulation data and the measured data;
s400: based on an entropy weight method, weights are given to the credibility of each deviation component;
s500: fusing the reliability of the simulation data of each deviation component based on the D-S evidence theory to obtain the reliability of the simulation data of the assembly unit; the method for acquiring the reliability of the simulation data of the assembly unit in the step S500 comprises the following steps:
s501: defining an evidence theory identification framework as { credibility meets the requirement, credibility does not meet the requirement, and uncertainty } =
Figure 256084DEST_PATH_IMAGE001
S502: according to the credibility of each deviation component endowed with the weight obtained in the step S400, a probability distribution matrix under the identification frame is constructedM：
Figure 770242DEST_PATH_IMAGE002
Wherein the content of the first and second substances,
Figure 352402DEST_PATH_IMAGE003
is as followsmAssigning basic probability that the reliability of the deviation component simulation data meets the requirement;
Figure 635616DEST_PATH_IMAGE004
is as followsmAssigning basic probability that the reliability of the simulation data of the deviation components does not meet the requirement;
Figure 395761DEST_PATH_IMAGE005
is as followsmAssigning a basic probability of uncertainty of the simulation data of the deviation components;
s503: and taking the reliability of the simulation data of each deviation component as an evidence, and fusing according to an evidence theory to obtain the reliability of the simulation data of the assembly unit:
Figure 649413DEST_PATH_IMAGE006
wherein the content of the first and second substances,
Figure 520417DEST_PATH_IMAGE007
is a firstiReliability of simulation data of each assembly unit;
Figure 658137DEST_PATH_IMAGE008
is a firstiAn assembly unitmThe reliability of the simulation data of each deviation component is changed into direct sum operation;
s504: according to step S503, get the secondiAnd (3) distributing reliability probability of simulation data of each assembly unit:
Figure 838452DEST_PATH_IMAGE009
；
s600: according to the aircraft structural member assembly process information, the reliability of the simulation data of each assembly unit is upwards aggregated, and the comprehensive reliability evaluation of the aircraft structural member assembly deviation simulation model is realized; the simulation model reliability evaluation method in step S600 includes:
s601: according to the steps S503 and S504, an assembly unit credibility sequence is constructed
Figure 327202DEST_PATH_IMAGE010
：
Figure 251164DEST_PATH_IMAGE011
Wherein the content of the first and second substances,
Figure 446653DEST_PATH_IMAGE012
is as followslReliability of simulation data of each assembly unit;
s602: based on the assembly process information, the credibility of the simulation data of each assembly unit is upwards aggregated according to the assembly level to obtain the comprehensive credibility of the assembly deviation simulation model of the airplane structural member
Figure 63448DEST_PATH_IMAGE013
：
Figure 508336DEST_PATH_IMAGE014
Wherein the content of the first and second substances,
Figure 783460DEST_PATH_IMAGE015
is as followslReliability of simulation data of each assembly unit;
s603: according to the step S602, obtaining the reliability probability distribution of the aircraft structural member assembly deviation simulation model:
Figure 616811DEST_PATH_IMAGE016
。
2. the method for evaluating the reliability of the aircraft structural part assembly deviation simulation model according to claim 1, is characterized in that: the specific method for collecting the assembly deviation simulation data in the step S100 is as follows:
s101: collecting assembly deviation simulation data of airplane structural member and constructing assembly deviation simulation data sequence
Figure 889660DEST_PATH_IMAGE017
：
Figure 87424DEST_PATH_IMAGE018
Wherein the content of the first and second substances,
Figure 87610DEST_PATH_IMAGE019
；lthe number of the assembly units;
Figure 320008DEST_PATH_IMAGE020
；mthe number of deviation components;nthe number of deviation data;
Figure 747447DEST_PATH_IMAGE021
is as followsiAn assembly unitkA first of deviation componentsnSub-assembly deviation simulation data;
s102: converting the assembly deviation simulation data sequence obtained in the step S101 into the number of intervals
Figure 698085DEST_PATH_IMAGE022
：
Figure 518274DEST_PATH_IMAGE023
Wherein the content of the first and second substances,
Figure 792129DEST_PATH_IMAGE024
respectively, deviation simulation data sequence
Figure 469098DEST_PATH_IMAGE025
Minimum and maximum values of;
s103: construction of interval number form assembly deviation simulation data sequence
Figure 831334DEST_PATH_IMAGE026
：
Figure 986372DEST_PATH_IMAGE027
Wherein the content of the first and second substances,
Figure 131045DEST_PATH_IMAGE028
is as followsiAn assembly unitmNumber of simulated data intervals for each deviation component.
3. The method for evaluating the reliability of the aircraft structural part assembly deviation simulation model according to claim 2, is characterized in that: the specific method for collecting the actually measured data of the assembly deviation in the step S200 is as follows:
s201: building a physical prototype of the airplane structural member;
s202: collecting the assembly deviation actual measurement data of the airplane structural member and constructing an assembly deviation actual measurement data sequence
Figure 165866DEST_PATH_IMAGE029
：
Figure 825518DEST_PATH_IMAGE030
Wherein the content of the first and second substances,
Figure 518667DEST_PATH_IMAGE031
is a firstiAn assembly unitkA first of deviation componentsnActual measurement data of the secondary assembly deviation;
s203: converting the assembly deviation actual measurement data sequence obtained in the step S202 into interval number
Figure 32694DEST_PATH_IMAGE032
：
Figure 989149DEST_PATH_IMAGE033
Wherein the content of the first and second substances,
Figure 385364DEST_PATH_IMAGE034
，
Figure 819887DEST_PATH_IMAGE035
respectively, deviation measured data sequence
Figure 470312DEST_PATH_IMAGE036
Minimum and maximum values of;
s204: construction of interval number form assembly deviation measured data sequence
Figure 127163DEST_PATH_IMAGE037
：
Figure 761406DEST_PATH_IMAGE038
Wherein the content of the first and second substances,
Figure 248888DEST_PATH_IMAGE039
is as followsiAn assembly unitmThe number of actually measured data intervals of each deviation component.
4. The method for evaluating the reliability of the simulation model of the assembly deviation of the aircraft structural part according to claim 3, wherein the method comprises the following steps: the specific method for calculating the section number association degree in step S300 is as follows:
s301: taking the assembly deviation simulation data sequence obtained in the step S100 as a comparison sequence, taking the assembly deviation actual measurement data sequence obtained in the step S200 as a reference sequence, and calculating the association degree of the interval number of each deviation component:
Figure 19398DEST_PATH_IMAGE040
wherein the content of the first and second substances,
Figure 317656DEST_PATH_IMAGE041
is as followsiAn assembly unitkThe interval number correlation degree of the simulation data of each deviation component and the actual measurement data,
Figure 688463DEST_PATH_IMAGE042
number of intervals
Figure 933631DEST_PATH_IMAGE043
To
Figure 807915DEST_PATH_IMAGE044
The distance of (a) to (b),
Figure 339390DEST_PATH_IMAGE045
；
s302: constructing an assembly deviation interval number correlation sequence
Figure 885909DEST_PATH_IMAGE046
：
Figure 983703DEST_PATH_IMAGE047
Wherein the content of the first and second substances,
Figure 728805DEST_PATH_IMAGE048
is as followsiAn assembly unitkThe degree of correlation of the interval number of each deviation component.
5. The method for evaluating the reliability of the aircraft structural part assembly deviation simulation model according to claim 1, is characterized in that: the method for giving the weight to the reliability of each deviation component in the step S400 is as follows:
s401: constructing a multi-attribute evaluation matrix according to the deviation actual measurement data collected in the step S200Q：
Figure 883711DEST_PATH_IMAGE049
Wherein the content of the first and second substances,
Figure 448685DEST_PATH_IMAGE050
is as followsiAn assembly unitmA first of deviation componentsnActual measurement data of the secondary assembly deviation;
s402: constructing a weight matrix of each deviation component dataP：
Figure 566814DEST_PATH_IMAGE051
Wherein the content of the first and second substances,
Figure 681269DEST_PATH_IMAGE052
is as followsiAn assembly unitmA deviation component ofnThe weight of the secondary assembly deviation measured data is occupied; the weight calculation formula is:
Figure 492230DEST_PATH_IMAGE053
wherein the content of the first and second substances,
Figure 810079DEST_PATH_IMAGE054
is as followsiAn assembly unitkA deviation component ofjThe weight of the measured data of the minor assembly deviation is occupied;
Figure 981166DEST_PATH_IMAGE055
；mthe number of deviation components;
Figure 700861DEST_PATH_IMAGE056
；nthe number of deviation data;
s403: solving the entropy of the deviation component information:
Figure 200500DEST_PATH_IMAGE057
wherein, the first and the second end of the pipe are connected with each other,
Figure 740065DEST_PATH_IMAGE058
is as followsiAn assembly unitkThe information entropy of each deviation component;
s404: solving the difference coefficient of the deviation components:
Figure 465576DEST_PATH_IMAGE059
wherein, the first and the second end of the pipe are connected with each other,
Figure 289044DEST_PATH_IMAGE060
is a firstiAn assembly unitkA difference coefficient of each deviation component;
s405: solving the weight of the deviation component:
Figure 441808DEST_PATH_IMAGE061
wherein the content of the first and second substances,
Figure 203091DEST_PATH_IMAGE062
is as followsiAn assembly unitkWeights of the individual deviation components;
s406: according to the deviation component weight obtained in step S405, a weight is given to the section number association degree, and the deviation component section number association degree given with the weight is obtained:
Figure 247139DEST_PATH_IMAGE063
wherein the content of the first and second substances,
Figure 410267DEST_PATH_IMAGE064
is as followsiAn assembly unitkThe individual deviation components are assigned to the degree of association of the number of intervals of the weight.
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