CN104614989B - Improve the spacecraft attitude sensory perceptual system Optimal Configuration Method of fault diagnosability - Google Patents
Improve the spacecraft attitude sensory perceptual system Optimal Configuration Method of fault diagnosability Download PDFInfo
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Abstract
A kind of spacecraft attitude sensory perceptual system Optimal Configuration Method for improving fault diagnosability of the present invention, step are as follows：Initialization particle swarm parameter；Generate gyro installation matrix；Judge whether gyro has detectability and separability；According to gyro installation matrix calculus fault diagnosis distance；Judge whether the fault diagnosis distance is more than the optimum fault diagnosis distance of current particle record, if optimum fault diagnosis distance and its corresponding particle position that fault diagnosis distance and its corresponding particle position more new particle are utilized more than if；Judge whether the fault diagnosis distance is more than the optimum fault diagnosis distance of particle group records, if optimum fault diagnosis distance and its corresponding position that population is updated more than if；Judge whether the optimum fault diagnosis distance of population meets regulation requirement；Update particle swarm parameter；Consideration of the present invention to fault diagnosis is advanced to the whole design phase, provides advantage for Research on fault diagnosis method, has filled up the blank both at home and abroad in technical field.
Description
Technical field
The present invention relates to a kind of spacecraft attitude sensory perceptual system Optimal Configuration Method for improving fault diagnosability, belongs to and defends
Star overall design technique field.
Background technology
All kinds of sensors that spacecraft attitude sensory perceptual system is included provide position and attitude information for spacecraft, navigation with
Very important effect is played in gesture stability.All kinds of statistical datas show, sensor be also in spacecraft control easily
The part broken down, therefore spacecraft attitude sensory perceptual system fault diagnosis is the primary study direction of spacecraft fault diagnosis.
At present, the development process of sensor is：First configuration design, here basis are completed from the angle of performance and function
On, the sensor configuration for giving carries out the research of method for diagnosing faults, and the problem that this mode is present is due to sensor
Configuration it has been determined that therefore the available information content of fault diagnosis and quality are constant, the research of existing diagnostic method can only
According to the fault diagnosis for realizing sensor of these information maximization degree, this is a kind of thinking cured the symptoms, not the disease.Phase of the invention
Hope from the sensor design phase and begin to consider troubleshooting issue, ensure as far as possible to be supplied to the information content and matter of fault diagnosis
Amount optimum, is that a favorable environment is built in the research of method for diagnosing faults and application, and then improves sensor event to greatest extent
Barrier diagnosis capability.
As gyro is the core component of spacecraft attitude sensory perceptual system, and configure that number is more, configuration is complex,
Therefore Optimal Configuration Method (being also called diagnosticability method for designing) the more generation for improving diagnosticability is studied with gyro as object
Table, but method proposed by the present invention can be applied in the design of other sensor diagnosticabilities.
The content of the invention
The present invention technology solve problem be：For the deficiencies in the prior art, the deficiencies in the prior art are overcome, there is provided a kind of
The spacecraft attitude sensory perceptual system Optimal Configuration Method of fault diagnosability is improved, by considering the event of spacecraft attitude sensory perceptual system
Barrier diagnosis problem, fundamentally improves its trouble diagnosibility.
The present invention technical solution be：
The spacecraft attitude sensory perceptual system Optimal Configuration Method of fault diagnosability is improved, including step is as follows：
(1) particle swarm parameter is initialized, the particle swarm parameter includes particle number, the Position And Velocity of particle, particle
With population optimum fault diagnosis distance；
(2) spacecraft gyro installation matrix H is generated according to particle position；
Wherein, X=[x1 x2 … x2n] position of particle is represented, n represents the number of spacecraft gyro, and H is 3 row square of n rows
Battle array, represents the installation site of a spacecraft gyro per a line；
(3) the gyro installation matrix obtained according to step (2) judges whether spacecraft gyro has detectability and can divide
From property, if while step (4) is entered if meeting, otherwise into step (7)；
(4) the gyro installation matrix calculus fault diagnosis distance obtained according to step (2)；
(5) judge calculated fault diagnosis distance in step (4) whether more than the optimum fault diagnosis of current particle
Distance, if being more than, updates the optimum failure of current particle using fault diagnosis distance now and its corresponding particle position
Diagnosis distance and its corresponding particle position, and step (6) is entered, otherwise into step (7)；
(6) judge calculated fault diagnosis distance in step (4) whether more than population optimum fault diagnosis away from
From if being more than, using fault diagnosis distance now and its optimum fault diagnosis of corresponding particle position renewal population
Distance and its corresponding particle position；
(7) if all particles of population all executed step (2)-(6), judge the optimum fault diagnosis of population away from
From whether regulation requirement is met, if meeting into step (9), if being unsatisfactory for into step (8)；If grain is still suffered from population
Son is not carried out step (2)-(6), then therefrom select particle execution step (2)-(6)；
(8) speed and the position of each particle, and repeat step (2)-(7) are updated；
(9) terminate.
Judge that gyrounit has the concrete mode of detectability and separability as follows in step (3)：
(3a) a line and string are increased as the first row of matrix H to the spacecraft gyro installation matrix H that step (2) is obtained
And first row, matrix H ' is obtained, wherein the first row is used to store variable label { V1,V2,V3, first row is used to store gyro mark
Number { C1,C2,…,Cn}；
(3b) to matrix H ' (2:n+1,2:4) DM decomposition is carried out, maximum match collection is obtained, and is concentrated every for maximum match
The related variable label in bar side and gyro label, are attached using oriented line, and line direction is pointed to by gyro output label
Variable label；Wherein H ' (2:n+1,2:4) represent the square being made up of to n+1 rows and the 2nd row to the 4th row the 2nd row of matrix H '
Battle array；
If M is the subset of side collection in figure G, if any both sides do not have same vertices in M, title M is of G
Match somebody with somebody；If there is no another matching N in figure G, make in N while number more than M in while number, then M is called maximum match collection.
(3c) assume that the gyro that the maximum match collection that step (3b) is obtained is included is numbered { Ci,Cj,Cl, wherein i ∈
{ 1 ..., n }, j ∈ { 1 ..., n }, l ∈ { 1 ..., n }, and i ≠ j ≠ l ({ Ci,Cj,ClFor gyro label { C1,C2,…,Cn}
In any three, not necessarily first three rows in matrix H '), { V is numbered to variable according to following rules1,V2,V3Addition
Mark：
If (3c1) gyro label CiOut-degree be 1, then the variable label V being attached theretomIncrease is designated { Ci, m ∈ 1,
2,3}；
If (3c2) gyro label CiOut-degree be 2, and one of variable label for being attached thereto is designated { Cl}
Or { Cj, then another variable label V being attached theretoqBe designated { Ci,ClOr { Ci,Cj, q ∈ { 1,2,3 }；
If (3c3) two gyros export label CiWith CjOut-degree is all 2, and with two variable label V of identicalpAnd Vg
It is connected, then the mark of the two variable labels is all { Ci,Cj, p ∈ { 1,2,3 }, g ∈ { 1,2,3 }, p ≠ g；
If three kinds of situations (3c4) described in (3c1), (3c2), (3c3) are unsatisfactory for, the mark of each variable label
For { Ci,Cj,Cl}；
(3d) mark is added to the gyro label for being not included in maximum match collection according to following rules, i.e., except { Ci,Cj,Cl}
Gyro numbering in addition；
For gyro label Ch, by the mark and C of the row all nonzero elements corresponding variable labelhCarry out union,
As gyro label ChMark, wherein h ∈ { 1 ..., n }, and h ≠ i ≠ j ≠ l；
(3e) the corresponding row of the middle deletion maximum match collection each edges of H ' for obtaining from step (3a) successively, to residual matrix weight
Multiple step (3b)-(3d), obtains the mark of all possible gyro label, can only delete maximum match collection a line correspondence every time
Row；
(3f) diagnosability analysis are carried out according to the mark of all gyro labels：
If (3f1) union of all gyro label marks can cover C1,C2,…,Cn, then all gyros are with detectable
Property；
(3f2) for each label of spacecraft gyro, if belonging to different gyro label logo collections, all tops
Spiral shell has separability.
The concrete mode that fault diagnosis distance is calculated in step (4) is as follows：
(4a) the installation matrix H according to gyro, obtains matrix V=In×n-H(HTH)-1HT；
Wherein, the concrete form of matrix V isIn×nRepresent the unit matrix of n rows n row；
(4b) according to the matrix V for obtaining, fault diagnosis distance is calculated according to following equation：
Wherein, VeeThe e row e column elements of representing matrix V, VseV is removed in the e row of representing matrix VeeOuter absolute value is most
Big element, i.e. Vse=max (| | V1e||,||V2e||,…,||V(e-1)e||,||V(e+1)e||,…,||Vne| |), e, s ∈
{ 1 ..., n }, and s ≠ e.
The concrete mode that particle swarm parameter is updated in step (8) is as follows：
The Position And Velocity of particle d is updated using following formula：
vd(k+1)=ω vd(k)+c1r1(pd(k)-xd(k))+c2r2(pg(k)-xd(k))
xd(k+1)=xd(k)+vd(k+1)
Wherein, vdRepresent the speed of particle d, xdRepresent the position of particle d, pdRepresent particle d optimum fault diagnosises apart from right
The particle position answered, pgRepresent population optimum fault diagnosis apart from corresponding particle position, ω, c1、r1、r2Represent random number.
The present invention is had the advantage that compared with prior art：
(1) present invention with fault diagnosis accuracy as optimization aim to meet fault detectability and separability is for about
Beam condition, provides the Optimal Configuration Method of attitude sensing system, and the consideration to fault diagnosis is advanced to the design phase, is failure
Diagnostic method research provides advantage, has filled up the blank both at home and abroad in the technical field.
(2) result that the inventive method is obtained using DM decomposition techniques, more features variable and measurement visual pattern
Between relation, it is easy to carry out diagnosability analysis, the present invention is easily achieved, and versatility is stronger.
(3) present invention is optimized solution using particle cluster algorithm, accelerates search procedure by intelligent search technique, carries
The high efficiency of fault diagnosis and satellite operation, greatlys save manpower and hardware cost.
Description of the drawings
Fig. 1 is flow chart of the present invention；
Fig. 2 is maximum match collection correlated variabless label of the present invention and gyro label annexation.
Specific embodiment
Just combine accompanying drawing below to be described further the present invention.
As shown in figure 1, the spacecraft attitude sensory perceptual system Optimal Configuration Method of fault diagnosability is improved, including step is such as
Under：
(1) particle swarm parameter is initialized, the particle swarm parameter includes particle number, the Position And Velocity of particle, particle
With population optimum fault diagnosis distance；
(2) spacecraft gyro installation matrix H is generated according to particle position；
Wherein, X=[x1 x2 … x2n] position of particle is represented, n represents the number of spacecraft gyro, and H is 3 row square of n rows
Battle array, represents the installation site of a spacecraft gyro per a line；
(3) the gyro installation matrix obtained according to step (2) judges whether spacecraft gyro has detectability and can divide
From property, if while step (4) is entered if meeting, otherwise into step (7)；
Diagnosticability refers to the degree that the failure in system accurately and effectively can be recognized, wherein accurately referring in failure
Unambiguously can detect every time and separation failure during generation, and effectively refer to fault reconstruction resource requirement can be carried out it is excellent
Change, fault diagnosability includes detectability and separability.Failure has detectability, after failure that and if only if generation
In the section time, even if there is disturbance, be possible with system input and the detection of failure realized with output information, in this patent mainly
It is whether the gyro output affected by failure judgement can build analytical redundancy relation to be analyzed.The isolabilily refers to
For giving the different faults in set, system has the ability of different manifestations, mainly different by judging in this patent
Whether the gyro output of fault impact can build different analytical redundancy relations to be analyzed.
Judge that gyro has the concrete mode of detectability and separability as follows：
(3a) a line and string are increased as the first row of matrix H to the spacecraft gyro installation matrix H that step (2) is obtained
And first row, matrix H ' is obtained, wherein the first row is used to store variable label { V1,V2,V3, first row is used to store gyro mark
Number { C1,C2,…,Cn}；
(3b) to matrix H ' (2:n+1,2:4) DM decomposition is carried out, maximum match collection is obtained, and is concentrated every for maximum match
The related variable label in bar side and gyro label, are attached using oriented line, and line direction is pointed to by gyro output label
Variable label；Wherein H ' (2:n+1,2:4) represent the square being made up of to n+1 rows and the 2nd row to the 4th row the 2nd row of matrix H '
Battle array；
If M is the subset of side collection in figure G, if any both sides do not have same vertices in M, title M is of G
Match somebody with somebody；If there is no another matching N in figure G, make in N while number more than M in while number, then M is called maximum match collection.
(3c) assume that the gyro that the maximum match collection that step (3b) is obtained is included is numbered { Ci,Cj,Cl, wherein i ∈
{ 1 ..., n }, j ∈ { 1 ..., n }, l ∈ { 1 ..., n }, and i ≠ j ≠ l ({ Ci,Cj,ClFor gyro label { C1,C2,…,Cn}
In any three, not necessarily first three rows in matrix H '), { V is numbered to variable according to following rules1,V2,V3Addition
Mark：
If (3c1) gyro label CiOut-degree be 1, then the variable label V being attached theretomIncrease is designated { Ci, m ∈ 1,
2,3}；
If (3c2) gyro label CiOut-degree be 2, and one of variable label for being attached thereto is designated { Cl}
Or { Cj, then another variable label V being attached theretoqBe designated { Ci,ClOr { Ci,Cj, q ∈ { 1,2,3 }；
If (3c3) two gyros export label CiWith CjOut-degree is all 2, and with two variable label V of identicalpAnd Vg
It is connected, then the mark of the two variable labels is all { Ci,Cj, p ∈ { 1,2,3 }, g ∈ { 1,2,3 }, p ≠ g；
If three kinds of situations (3c4) described in (3c1), (3c2), (3c3) are unsatisfactory for, the mark of each variable label
For { Ci,Cj,Cl}；
(3d) mark is added to the gyro label for being not included in maximum match collection according to following rules, i.e., except { Ci,Cj,Cl}
Gyro numbering in addition；
For gyro label Ch, by the mark and C of the row all nonzero elements corresponding variable labelhCarry out union,
As gyro label ChMark, wherein h ∈ { 1 ..., n }, and h ≠ i ≠ j ≠ l；
(3e) the corresponding row of the middle deletion maximum match collection each edges of H ' for obtaining from step (3a) successively, to residual matrix weight
Multiple step (3b)-(3d), obtains the mark of all possible gyro label, can only delete maximum match collection a line correspondence every time
Row；
(3f) diagnosability analysis are carried out according to the mark of all gyro labels：
If (3f1) union of all gyro label marks can cover C1,C2,…,Cn, then all gyros are with detectable
Property；
(3f2) for each label of spacecraft gyro, if belonging to different gyro label logo collections, all tops
Spiral shell has separability.
Name a concrete example to be described in detail step (3).
WithAs a example by, step (3) is described in detail.
Increase a line to matrix H and string obtains matrixCarrying out DM decomposition can
Know that maximum match collection is { { C1,V1},{C2,V2},{C5,V3}}.For the variable label that maximum match concentration each edge is related to
With gyro label, it is attached using oriented line, variable label is pointed to by gyro output label in line direction, as shown in Figure 2.
Due to gyro label C in Fig. 25Out-degree be 1, therefore variable V3Be designated { C5, and for gyro label C1,
C2, due to belonging to the situation described in step (3c4), therefore variable V1With V2Mark be all { C1,C2,C5}。
Gyro label to being not included in maximum match collection adds mark below.Due to gyro label C3Only with variable V1Phase
Close, therefore gyro label C3Be designated { C1,C2,C3,C5, gyro label C is obtained in the same manner4Be designated { C1,C2,C4,C5}。
By C1It is be expert to delete the matrix for obtainingRepeat step (3b)-(3d) is obtained
Gyro label is designated：{C2,C3,C4,C5}.
By C2It is be expert to delete the matrix for obtainingRepeat step (3b)-(3d) is obtained
Gyro label is designated：{C1,C3,C4,C5}。
By C5It is be expert to delete the matrix for obtainingRepeat step (3b)-(3d) is obtained
Gyro label is designated：{C1,C2,C3,C4}。
All gyro labels that above-mentioned steps are obtained are designated：{C1,C2,C3,C5}{C2,C3,C4,C5}{C1,C3,C4,C5}
{C1,C2,C4,C5}{C1,C2,C3,C4, as the tagged union of institute contains all gyro labels, therefore spacecraft gyro tool
There is detectability.
For gyro label C1The 2nd be not belonging in above-mentioned five set, gyro label C2It is not belonging to above-mentioned five set
In the 3rd, gyro label C3The 4th be not belonging in above-mentioned five set, gyro label C4It is not belonging in above-mentioned five set
The 1st and gyro label C5The 5th be not belonging in above-mentioned five set, therefore each gyro label belongs to different tops
Spiral shell label logo collection, so spacecraft gyro has separability.
It should be noted that each gyro label mark represents an analytical redundancy relation, analytical redundancy relation refer to from
The only restriction relation comprising observational variable obtained in system model, for the concordance of check observation value, each gyro label
The corresponding gyro output of all labels included in logo collection can build an analytical redundancy relation.
(4) the gyro installation matrix calculus fault diagnosis distance obtained according to step (2)；
The concrete mode for calculating fault diagnosis distance is as follows：
(4a) the installation matrix H according to gyro, obtains matrix V=In×n-H(HTH)-1HT；
Wherein, the concrete form of matrix V isIn×nRepresent the unit matrix of n rows n row；
(4b) according to the matrix V for obtaining, fault diagnosis distance is calculated according to following equation：
Wherein, VeeThe e row e column elements of representing matrix V, VseV is removed in the e row of representing matrix VeeOuter absolute value is most
Big element, i.e. Vse=max (| | V1e||,||V2e||,…,||V(e-1)e||,||V(e+1)e||,…,||Vne| |), e, s ∈
{ 1 ..., n }, and s ≠ e.
(5) judge calculated fault diagnosis distance in step (4) whether more than the optimum fault diagnosis of current particle
Distance, if being more than, updates the optimum failure of current particle using fault diagnosis distance now and its corresponding particle position
Diagnosis distance and its corresponding particle position, and step (6) is entered, otherwise into step (7)；
(6) judge calculated fault diagnosis distance in step (4) whether more than population optimum fault diagnosis away from
From if being more than, using fault diagnosis distance now and its optimum fault diagnosis of corresponding particle position renewal population
Distance and its corresponding particle position；
(7) if all particles of population all executed step (2)-(6), judge the optimum fault diagnosis of population away from
From whether regulation requirement is met, if meeting into step (9), if being unsatisfactory for into step (8)；If grain is still suffered from population
Son is not carried out step (2)-(6), then therefrom select particle execution step (2)-(6)；
(8) speed and the position of each particle, and repeat step (2)-(7) are updated；
The concrete mode for updating particle swarm parameter is as follows：
The Position And Velocity of particle d is updated using following formula：
vd(k+1)=ω vd(k)+c1r1(pd(k)-xd(k))+c2r2(pg(k)-xd(k))
xd(k+1)=xd(k)+vd(k+1)
Wherein, vdRepresent the speed of particle d, xdRepresent the position of particle d, pdRepresent particle d optimum fault diagnosises apart from right
The particle position answered, pgRepresent population optimum fault diagnosis apart from corresponding particle position, ω, c1、r1、r2Represent random number.
(9) terminate.
Unspecified part of the present invention belongs to general knowledge as well known to those skilled in the art.
Claims (3)
1. the spacecraft attitude sensory perceptual system Optimal Configuration Method of fault diagnosability is improved, it is characterised in that step is as follows：
(1) particle swarm parameter is initialized, the particle swarm parameter includes particle number, the Position And Velocity of particle, particle and grain
Subgroup optimum fault diagnosis distance；
(2) spacecraft gyro installation matrix H is generated according to particle position；
Wherein, X=[x1 x2 … x2n] position of particle is represented, n represents the number of spacecraft gyro, and H is 3 column matrix of n rows, often
A line represents the installation site of a spacecraft gyro；
(3) the gyro installation matrix obtained according to step (2) judges whether spacecraft gyro has detectability and separability,
If at the same meet if enter step (4), otherwise into step (7)；
(4) the gyro installation matrix calculus fault diagnosis distance obtained according to step (2)：
(4a) the installation matrix H according to gyro, obtains matrix V=In×n-H(HTH)-1HT；Wherein, the concrete form of matrix V isIn×nRepresent the unit matrix of n rows n row；
(4b) according to the matrix V for obtaining, fault diagnosis distance is calculated according to following equation：
Wherein, VeeThe e row e column elements of representing matrix V, VseV is removed in the e row of representing matrix VeeOuter maximum absolute value
Element, i.e. Vse=max (| | V1e||,||V2e||,…,||V(e-1)e||,||V(e+1)e||,…,||Vne| |), e, s ∈ 1 ...,
N }, and s ≠ e；
(5) calculated fault diagnosis distance in step (4) is judged whether more than the optimum fault diagnosis distance of current particle,
If being more than, using now fault diagnosis distance and its corresponding particle position update current particle optimum fault diagnosis away from
From and its corresponding particle position, and enter step (6), otherwise into step (7)；
(6) calculated fault diagnosis distance in step (4) is judged whether more than the optimum fault diagnosis distance of population, if
Be more than, then updated using fault diagnosis distance now and its corresponding particle position the optimum fault diagnosis of population apart from and
Its corresponding particle position；
(7) if all particles of population all executed step (2)-(6), if particle due to being unsatisfactory for the condition of step (3) and
Be not carried out step (4)-(6), be also considered as particle executed step (2)-(6), then judge the optimum fault diagnosis of population away from
From whether regulation requirement is met, if meeting into step (9), step (8) is entered if being unsatisfactory for；If still suffered from population
Particle is not carried out step (2)-(6), then therefrom select particle execution step (2)-(6)；
(8) speed and the position of each particle, and repeat step (2)-(7) are updated；
(9) terminate.
2. the spacecraft attitude sensory perceptual system Optimal Configuration Method of fault diagnosability is improved as claimed in claim 1, and which is special
Levy and be：Judge that gyrounit has the concrete mode of detectability and separability as follows in the step (3)：
(3a) a line and string are increased to the spacecraft gyro installation matrix H that step (2) is obtained as the first row of matrix H and
String, obtains matrix H ', and wherein the first row is used to store variable label { V1,V2,V3, first row is used to store gyro label
{C1,C2,…,Cn}；
(3b) to matrix H ' (2:n+1,2:4) DM decomposition is carried out, and is obtained maximum match collection, and each edge is concentrated for maximum match
Related variable label and gyro label, are attached using oriented line, and variable is pointed to by gyro output label in line direction
Label；Wherein H ' (2:n+1,2:4) represent the matrix being made up of to n+1 rows and the 2nd row to the 4th row the 2nd row of matrix H '；
(3c) assume that the gyro that the maximum match collection that step (3b) is obtained is included is numbered { Ci,Cj,Cl, wherein i ∈ 1 ...,
N }, j ∈ { 1 ..., n }, l ∈ { 1 ..., n }, and i ≠ j ≠ l, { Ci,Cj,ClFor gyro label { C1,C2,…,CnIn appoint
Meaning three, the not necessarily first three rows in matrix H ', number { V to variable according to following rules1,V2,V3Addition mark：
If (3c1) gyro label CiOut-degree be 1, then the variable label V being attached theretomIncrease is designated { Ci, m ∈ 1,2,
3}；
If (3c2) gyro label CiOut-degree be 2, and one of variable label for being attached thereto is designated { ClOr
{Cj, then another variable label V being attached theretoqBe designated { Ci,ClOr { Ci,Cj, q ∈ { 1,2,3 }；
If (3c3) two gyros export label CiWith CjOut-degree is all 2, and with two variable label V of identicalpAnd VgIt is connected
Connect, then the mark of the two variable labels is all { Ci,Cj, p ∈ { 1,2,3 }, g ∈ { 1,2,3 }, p ≠ g；
If three kinds of situations (3c4) described in (3c1), (3c2), (3c3) are unsatisfactory for, the mark of each variable label is
{Ci,Cj,Cl}；
(3d) mark is added to the gyro label for being not included in maximum match collection according to following rules, i.e., except { Ci,Cj,ClBeyond
Gyro numbering；
For gyro label Ch, by the mark and C of the row all nonzero elements corresponding variable labelhUnion is carried out, as
Gyro label ChMark, wherein h ∈ { 1 ..., n }, and h ≠ i ≠ j ≠ l；
(3e) the corresponding row of the middle deletion maximum match collection each edges of H ' for obtaining from step (3a) successively, repeats to walk to residual matrix
Suddenly (3b)-(3d), obtains the mark of all possible gyro label, can only delete maximum match collection a line every time corresponding
OK；
(3f) diagnosability analysis are carried out according to the mark of all gyro labels：
If (3f1) union of all gyro label marks can cover C1,C2,…,Cn, then all gyros there is detectability；
(3f2) for each label of spacecraft gyro, if belonging to different gyro label logo collections, all gyro tools
There is separability.
3. the spacecraft attitude sensory perceptual system Optimal Configuration Method of fault diagnosability is improved as claimed in claim 1, and which is special
Levy and be：The concrete mode that particle swarm parameter is updated in the step (8) is as follows：
The Position And Velocity of particle d is updated using following formula：
vd(k+1)=ω vd(k)+c1r1(pd(k)-xd(k))+c2r2(pg(k)-xd(k))
xd(k+1)=xd(k)+vd(k+1)
Wherein, vdRepresent the speed of particle d, xdRepresent the position of particle d, pdRepresent particle d optimum fault diagnosises apart from corresponding
Particle position, pgRepresent population optimum fault diagnosis apart from corresponding particle position, ω, c1、r1、r2Represent random number.
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Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103234553A (en) * | 2013-03-29 | 2013-08-07 | 北京控制工程研究所 | Fault diagnosis method for gyro measurement system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8024610B2 (en) * | 2007-05-24 | 2011-09-20 | Palo Alto Research Center Incorporated | Diagnosing intermittent faults |
-
2014
- 2014-12-26 CN CN201410827911.1A patent/CN104614989B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103234553A (en) * | 2013-03-29 | 2013-08-07 | 北京控制工程研究所 | Fault diagnosis method for gyro measurement system |
Non-Patent Citations (8)
| Title |
|---|
| 基于QPSCO算法的传感器优化配置;蒋鼎国等;《南京理工大学学报（自然科学版）》;20090831;第33卷(第4期);第459-463页 * |
| 基于可诊断性约束的测点优化配置研究;刘文静等;《空间控制技术与应用》;20110430;第37卷(第2期);第1-5,25页 * |
| 基于多信号流图和改进BHS-树的陀螺可诊断性研究;王振西等;《空间控制技术与应用》;20120831;第38卷(第4期);第1-5,56页 * |
| 基于相关性模型的液浮陀螺可诊断性分析方法;刘成瑞等;《空间控制技术与应用》;20130228;第39卷(第1期);第10-14,22页 * |
| 基于相关矩阵的动力传动系统故障诊断多类传感器优化布置方法研究;闫鹏程等;《计算机测量与控制》;20120731;第20卷(第7期);第1753-1756页 * |
| 定量与定性相结合的动量轮故障可诊断性评价;刘文静等;《中国空间科学技术》;20110831;第54-63页 * |
| 故障可诊断性评价与设计研究进展;刘文静等;《航天控制》;20111231;第29卷(第6期);第72-78,87页 * |
| 故障诊断中传感器配置优化的复杂性分析;刘晓芹等;《计算机工程与设计》;20110331;第32卷(第3期);第1062-1065页 * |
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