CN108427388A - A kind of R2R manufacture systems fault coverage determines method and device - Google Patents
A kind of R2R manufacture systems fault coverage determines method and device Download PDFInfo
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- CN108427388A CN108427388A CN201810321193.9A CN201810321193A CN108427388A CN 108427388 A CN108427388 A CN 108427388A CN 201810321193 A CN201810321193 A CN 201810321193A CN 108427388 A CN108427388 A CN 108427388A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32339—Object oriented modeling, design, analysis, implementation, simulation language
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a kind of R2R manufacture systems fault coverages to determine method and device,By in flexible material (Roll to Roll,R2R) on the basis of the correlation analysis between each station of manufacture system,In conjunction with physical analysis and data-driven method,Establish the relational expression of description multistation process variations and product final mass,Build controlled error stream (the Stream of Variation under runaway condition of manufacture system,SoV) model,The quality control chart for monitoring autocorrelation evidence accordingly is established for product quality characteristic variable,Carry out R2R manufacture systems multiple faults detection be isolated,It solves when monitoring multistation system,Existing single factor test control figure,Multifactor control figure,Return the fault determination methods such as adjustment control chart,There is a problem of that rate of false alarm is high,Especially when there are when auto-correlation for process data,Can not be using abnormal failure of the shewhart control chart under independence assumption to monitor manufacturing process the technical issues of.
Description
Technical field
The present invention relates to field of information processing more particularly to a kind of R2R manufacture systems fault coverage to determine method and device.
Background technology
In recent years, it is ground using fexible film as the wearable sensors of substrate material, OLED, thin film solar cell technologies
Study carefully and make a breakthrough, industrialization, large-scale production demand have been brought into schedule.But flexible thin-film material belongs to anisotropy material
Material, deformation have diversity and do not know, and fold, interlayer slip, breakage and other issues easily occur.Roll-to-roll continuous manufacture
System is since with high degree of automation, production efficiency is high, and production process is affected by human factors the advantages that small, to as soft
Property material (Roll to Roll, R2R) fabricate optimal selection.
Flexible material R2R manufactures usually form the pressure that continuous process system carries out fexible film by several or dozens of roll shaft
The processing such as system, printing, when process quality issue occurs, conventional prediction technique is difficult to determine the failure for causing quality problems
Source.Existing quality control chart fault coverage determines method such as single factor test control figure, multifactor control figure, returns adjustment control
Figure, when monitor multistation system, control figure, cannot especially when process data is there are when auto-correlation with high rate of false alarm
The abnormal failure of manufacturing process is enough monitored using the shewhart control chart under independence assumption.
Invention content
The present invention provides a kind of R2R manufacture systems fault coverages to determine method and device, for solving in monitoring multiplexing
When the system of position, existing single factor test control figure, returns the fault determination methods such as adjustment control chart at multifactor control figure, there is mistake
The high problem of report rate can not use the shewhart control chart under independence assumption especially when process data is there are when auto-correlation
The technical issues of abnormal failure to monitor manufacturing process.
A kind of R2R manufacture systems fault coverage provided by the invention determines method, including：
Determine processing quality feature vector LiStatisticMean vector μf(μf≠ 0) and covariance matrix ∑f；
Wherein,Wherein, d'L,mFor dL,mDerivative；
Wherein, dL,mFor direction vector；
Wherein, m is failure offset direction line number, hL,mFor Li,mChange in Mean amount；
hL,m=Γ Φi-τμf,m+ΓΦi-(τ+1)μf,m+…+ΓΦ2μf,m+ΓΦμf,m+Γμf,m；
According to system failure source fiChange in Mean direction calculating LiChange direction, obtain updated qualitative character to
Measure Li,m；
Controlled SoV models are established, the controlled SoV models are：
Wherein,
Wherein, Φ indicates unit diagonal matrix, viIndicate independently of each other and meet the random number matrix of normal distribution, i tables
Show i-th of station, LiFor the quality characteristic vector that the N × 1 of i-th of station output products is tieed up, Γ indicates a production system institute
The constant coefficient matrix of decision, fiFor the failure source vector that the N × 1 of i-th of station is tieed up, εiThe measurement tieed up for the N × 1 of i-th of station
The noise vector that noise and model do not include；
SoV models out of control are established, the SoV models out of control are：
Wherein,
If the source of trouble of current station is linearly related with the source of trouble of previous station, fi=Φ fi-1+vi, wherein Φ tables
Show unit diagonal matrix, viIndicate independently of each other and meet the random number matrix of normal distribution；
When system is in slave mode, by the first preset formula group determine the first center line value, the first upper limit value and
First lower limiting value；
When system jam, the second center line value, the second upper limit value and second are determined by the second preset formula group
Lower limiting value；
When systems stay breaks down, by third preset formula group determine third centerline value, third upper limit value and
Third lower limiting value.
Preferably, the first preset formula group is specially：
Wherein, statisticDistribution：
Wherein m=1,2 ..., t, A are control limit coefficients,For statisticStandard deviation.
Preferably, the second preset formula group is specially：
Wherein：
Preferably, the third preset formula group is specially：
Wherein：
A kind of R2R manufacture systems fault coverage determining device provided by the invention, including：
First determining module, for determining processing quality feature vector LiStatisticMean vector μf(μf≠0)
With covariance matrix Σf；
Wherein,Wherein, d'L,mFor dL,mDerivative；
Wherein, dLmFor direction vector；
Wherein, m is failure offset direction line number, hL,mFor Li,mChange in Mean amount；
hL,m=Γ Φi-τμf,m+ΓΦi-(τ+1)μf,m+…+ΓΦ2μf,m+ΓΦμf,m+Γμf,m；
According to system failure source fiChange in Mean direction calculating LiChange direction, obtain updated qualitative character to
Measure Li,m；
First establishes module, and for establishing controlled SoV models, the controlled SoV models are：
Wherein,
Wherein, Φ indicates unit diagonal matrix, viIndicate independently of each other and meet the random number matrix of normal distribution, i tables
Show i-th of station, LiFor the quality characteristic vector that the N × 1 of i-th of station output products is tieed up, Γ indicates a production system institute
The constant coefficient matrix of decision, fiFor the failure source vector that the N × 1 of i-th of station is tieed up, εiThe measurement tieed up for the N × 1 of i-th of station
The noise vector that noise and model do not include；
Second establishes module, and for establishing SoV models out of control, the SoV models out of control are：
Wherein,
If the source of trouble of current station is linearly related with the source of trouble of previous station, fi=Φ fi-1+vi, wherein Φ tables
Show unit diagonal matrix, viIndicate independently of each other and meet the random number matrix of normal distribution；
Second determining module, for when system is in slave mode, the first center to be determined by the first preset formula group
Line value, the first upper limit value and the first lower limiting value；
Third determining module, for when system jam, by the second preset formula group determine the second center line value,
Second upper limit value and the second lower limiting value；
4th determining module, for when systems stay breaks down, third center to be determined by third preset formula group
Line value, third upper limit value and third lower limiting value.
Preferably, second determining module is specifically used for：
When system is in slave mode, by the first preset formula group determine the first center line value, the first upper limit value and
First lower limiting value；
The first preset formula group is specially：
Wherein, statisticDistribution：
Wherein m=1,2 ..., t, A are control limit coefficients,For statisticStandard deviation.
Preferably, the third determining module, is specifically used for：
When system jam, the second center line value, the second upper limit value and second are determined by the second preset formula group
Lower limiting value；
The second preset formula group is specially：
Wherein：
Preferably, the 4th determining module, is specifically used for：
When systems stay breaks down, by third preset formula group determine third centerline value, third upper limit value and
Third lower limiting value；
The third preset formula group is specially：
Wherein：
As can be seen from the above technical solutions, the present invention has the following advantages：
A kind of R2R manufacture systems fault coverage provided by the invention determines method, including：Determine processing quality feature vector
LiStatisticMean vector μf(μf≠ 0) and covariance matrix ∑f；According to system failure source fiChange in Mean direction
Calculate LiChange direction, obtain updated quality characteristic vector Li,m；Establish controlled SoV models；Establish SoV models out of control；
If the source of trouble of current station is linearly related with the source of trouble of previous station, fi=Φ fi-1+vi；It is in controlled shape in system
When state, the first center line value, the first upper limit value and the first lower limiting value are determined by the first preset formula group；Work as system jam
When, the second center line value, the second upper limit value and the second lower limiting value are determined by the second preset formula group；When event occurs for systems stay
When barrier, third centerline value, third upper limit value and third lower limiting value are determined by third preset formula group.
In the present invention, on the basis of by correlation analysis between each station of flexible material R2R manufacture systems, in conjunction with
Physical analysis and data-driven method establish the relational expression of description multistation process variations and product final mass, structure
Controlled error stream (Stream of Variation, SoV) model under runaway condition of manufacture system, for product quality spy
Sign variable, which is established accordingly, to carry out the detection of multiple faults for monitoring the quality control chart of autocorrelation evidence and is isolated, and solves to exist
When monitoring multistation system, existing single factor test control figure, returns the failures determination sides such as adjustment control chart at multifactor control figure
Method has that rate of false alarm is high, especially when process data is there are when auto-correlation, can not use normal under independence assumption
The technical issues of advising abnormal failure of the control figure to monitor manufacturing process.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without having to pay creative labor, may be used also for those of ordinary skill in the art
To obtain other attached drawings according to these attached drawings.
Fig. 1 is that a kind of R2R manufacture systems fault coverage provided by the invention determines that the flow of one embodiment of method is shown
It is intended to；
Fig. 2 is the flow for another embodiment that a kind of R2R manufacture systems fault coverage provided by the invention determines method
Schematic diagram；
Fig. 3 is that a kind of structure of one embodiment of R2R manufacture systems fault coverage determining device provided by the invention is shown
It is intended to.
Specific implementation mode
An embodiment of the present invention provides a kind of R2R manufacture systems fault coverages to determine method and device, solves more in monitoring
When station system, existing single factor test control figure, returns the fault determination methods such as adjustment control chart at multifactor control figure, exists
The high problem of rate of false alarm can not use the conventional control under independence assumption especially when process data is there are when auto-correlation
The technical issues of abnormal failure of the figure to monitor manufacturing process.
In order to make the invention's purpose, features and advantages of the invention more obvious and easy to understand, below in conjunction with the present invention
Attached drawing in embodiment, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that disclosed below
Embodiment be only a part of the embodiment of the present invention, and not all embodiment.Based on the embodiments of the present invention, this field
All other embodiment that those of ordinary skill is obtained without making creative work, belongs to protection of the present invention
Range.
Referring to Fig. 1, an embodiment of the present invention provides the implementations that a kind of R2R manufacture systems fault coverage determines method
Example, including：
S101：Determine processing quality feature vector LiStatisticMean vector μf(μf≠ 0) and covariance matrix
Σf；
Wherein,Wherein, d'L,mFor dL,mDerivative；
Wherein, dL,mFor direction vector；
Wherein, m is failure offset direction line number, hL,mFor Li,mChange in Mean amount；
hL,m=Γ Φi-τμf,m+ΓΦi-(τ1)μf,m+…+ΓΦ2μf,m+ΓΦμf,m+Γμf,m；
According to system failure source fiChange in Mean direction calculating LiChange direction, obtain updated qualitative character to
Measure Lim；
It should be noted that μf(μf≠ 0) it is that multistation process is abnormal rear system failure source fiMean vector, ∑f
It is abnormal rear system failure source f for multistation processiCovariance matrix；
dL,mFor hL,mDirection vector, and under conditions of offset direction determines, statistic is designed asWhen control figure it is maximally efficient；
M indicates failure offset direction line number；LiFor the quality characteristic vector of i-th of station, Li,mFor the probability of its statistic
It is distributed mathematic(al) representation, on this basis, has derived the center line of control figure, the upper limit, the calculating formula of lower limiting value.And according to the time
Sequential sampling obtains sample quality characteristic value, control figure is drawn in the form of scatter plot, by control figure come monitor production process
Quality fluctuation situation；
S102：Controlled SoV models are established, the controlled SoV models are：
Wherein,
Wherein, Φ indicates unit diagonal matrix, viIndicate independently of each other and meet the random number matrix of normal distribution, i tables
Show i-th of station, LiFor the quality characteristic vector that the N × 1 of i-th of station output products is tieed up, Γ indicates a production system institute
The constant coefficient matrix of decision, fiFor the failure source vector that the N × 1 of i-th of station is tieed up, εiThe measurement tieed up for the N × 1 of i-th of station
The noise vector that noise and model do not include；
S103：SoV models out of control are established, the SoV models out of control are：
Wherein,
If the source of trouble of current station is linearly related with the source of trouble of previous station, fi=Φ fi-1+vi, wherein Φ tables
Show unit diagonal matrix, viIndicate independently of each other and meet the random number matrix of normal distribution；
It should be noted that fiInitial distribution obey fi~N (0, ∑f), Φ is diagonal matrix, indicates that the source of trouble is related
Size degree, in general Φ=φ I, (between failure independent).Wherein -1<φ<1, I is unit matrix, viIt is to obey just
The noise of state distribution.By linear state space model Li=Γ fi+εiTransformation, wherein i represents i-th of station, LiIt is i-th
The quality characteristic vector that the N × 1 of a station output products is tieed up, the constant coefficient matrix that Γ is determined by a production system：Fi is
The failure source vector that the N of i-th of station × 1 is tieed up, εiIt makes an uproar for what the N × 1 of i-th of station measurement noises tieed up and model did not included
Sound vector；
S104：When system is in slave mode, the first center line value, first upper limit are determined by the first preset formula group
Value and the first lower limiting value；
S105：When system jam, by the second preset formula group determine the second center line value, the second upper limit value and
Second lower limiting value；
S106：When systems stay breaks down, third centerline value, the third upper limit are determined by third preset formula group
Value and third lower limiting value；
A kind of R2R manufacture systems fault coverage provided in an embodiment of the present invention determines method, including：Determine processing quality spy
Levy vector LiStatisticMean vector μf(μf≠ 0) and covariance matrix ∑f；According to system failure source fiMean value become
Change direction calculating LiChange direction, obtain updated quality characteristic vector Li,m；Establish controlled SoV models；It establishes out of control
SoV models；If the source of trouble of current station is linearly related with the source of trouble of previous station, fi=Φ fi-1+vi；At system
When slave mode, the first center line value, the first upper limit value and the first lower limiting value are determined by the first preset formula group；Work as system
When breaking down, the second center line value, the second upper limit value and the second lower limiting value are determined by the second preset formula group；When system is held
It is continuous that third centerline value, third upper limit value and third lower limiting value are determined by third preset formula group when breaking down, by
On the basis of correlation analysis between each station of flexible material R2R manufacture systems, in conjunction with physical analysis and data-driven method,
Establish the relational expression of description multistation process variations and product final mass, structure is under manufacture system is controlled and runaway condition
Error stream (Stream of Variation, SoV) model, established accordingly for product quality characteristic variable for monitoring
The quality control chart of autocorrelation evidence, carries out the detection of multiple faults and is isolated, and solves when monitoring multistation system, existing list
Factor controlling figure, returns the fault determination methods such as adjustment control chart at multifactor control figure, there is a problem of that rate of false alarm is high, especially
It is that can not the different of manufacturing process be monitored using the shewhart control chart under independence assumption when process data is there are when auto-correlation
The technical issues of normal failure.
It is a kind of description for one embodiment progress for determining method to R2R manufacture systems fault coverage above, below will
Another embodiment of method, which is described in detail, to be determined to a kind of R2R manufacture systems fault coverage.
Referring to Fig. 2, an embodiment of the present invention provides the implementations that a kind of R2R manufacture systems fault coverage determines method
Example, including：
S201：Determine processing quality feature vector LiStatisticMean vector μf(μf≠ 0) and covariance matrix
∑f；
Wherein,Wherein, d'L,mFor dL,mDerivative；
Wherein, dL,mFor direction vector；
Wherein, m is failure offset direction line number, hL,mFor Li,mChange in Mean amount；
hL,m=Γ Φi-τμf,m+ΓΦi-(τ+1)μf,m+…+ΓΦ2μf,m+ΓΦμf,m+Γμf,m；
According to system failure source fiChange in Mean direction calculating LiChange direction, obtain updated qualitative character to
Measure Li,m；
S202：Controlled SoV models are established, the controlled SoV models are：
Wherein,
Wherein, Φ indicates unit diagonal matrix, viIndicate independently of each other and meet the random number matrix of normal distribution, i tables
Show i-th of station, LiFor the quality characteristic vector that the N × 1 of i-th of station output products is tieed up, Γ indicates a production system institute
The constant coefficient matrix of decision, fiFor the failure source vector that the N × 1 of i-th of station is tieed up, εiThe measurement tieed up for the N × 1 of i-th of station
The noise vector that noise and model do not include；
S203：SoV models out of control are established, the SoV models out of control are：
Wherein,
If the source of trouble of current station is linearly related with the source of trouble of previous station, fi=Φ fi-1+vi, wherein Φ tables
Show unit diagonal matrix, viIndicate independently of each other and meet the random number matrix of normal distribution；
S204：When system is in slave mode, the first center line value, first upper limit are determined by the first preset formula group
Value and the first lower limiting value；
The first preset formula group is specially：
Wherein, statisticDistribution：
Wherein m=1,2 ..., t, A are control limit coefficients,For statisticStandard deviation；
S205：When system jam, by the second preset formula group determine the second center line value, the second upper limit value and
Second lower limiting value；
The second preset formula group is specially：
Wherein：
S206：When systems stay breaks down, third centerline value, the third upper limit are determined by third preset formula group
Value and third lower limiting value；
The third preset formula group is specially：
Wherein：
A kind of R2R manufacture systems fault coverage provided in an embodiment of the present invention determines method, including：Determine processing quality spy
Levy vector LiStatisticMean vector μf(μf≠ 0) and covariance matrix ∑f；According to system failure source fiMean value become
Change direction calculating LiChange direction, obtain updated quality characteristic vector Li,m；Establish controlled SoV models；It establishes out of control
SoV models；If the source of trouble of current station is linearly related with the source of trouble of previous station, fi=Φ fi-1+vi；At system
When slave mode, the first center line value, the first upper limit value and the first lower limiting value are determined by the first preset formula group；Work as system
When breaking down, the second center line value, the second upper limit value and the second lower limiting value are determined by the second preset formula group；When system is held
It is continuous that third centerline value, third upper limit value and third lower limiting value are determined by third preset formula group when breaking down, by
On the basis of correlation analysis between each station of flexible material R2R manufacture systems, in conjunction with physical analysis and data-driven method,
Establish the relational expression of description multistation process variations and product final mass, structure is under manufacture system is controlled and runaway condition
Error stream (Stream of Variation, SoV) model, established accordingly for product quality characteristic variable for monitoring
The quality control chart of autocorrelation evidence, carries out the detection of multiple faults and is isolated, and solves when monitoring multistation system, existing list
Factor controlling figure, returns the fault determination methods such as adjustment control chart at multifactor control figure, there is a problem of that rate of false alarm is high, especially
It is that can not the different of manufacturing process be monitored using the shewhart control chart under independence assumption when process data is there are when auto-correlation
The technical issues of normal failure.
It is a kind of description for another embodiment progress that method is determined to R2R manufacture systems fault coverage above, below
A kind of one embodiment of R2R manufacture systems fault coverage determining device will be described in detail.
Reference Fig. 3, a kind of one embodiment of R2R manufacture systems fault coverage determining device provided by the invention, including：
First determining module 301, for determining processing quality feature vector LiStatisticMean vector and association side
Poor matrix；
Wherein,
Wherein, dL,mFor direction vector；
Wherein, hL,mFor Li,mChange in Mean amount；
hL,m=Γ Φi-τμf,m+ΓΦi-(τ+1)μf,m+…+ΓΦ2μf,m+ΓΦμf,m+Γμf,m；
Wherein, Li,mIndicate that R2R manufacture systems break down, according to system failure source fiChange in Mean direction calculating Li's
Change direction obtains updated quality characteristic vector；
First establishes module 302, and for establishing controlled SoV models, the controlled SoV models are：
Wherein,
Wherein, Φ indicates unit diagonal matrix, viIndicate independently of each other and meet the random number matrix of normal distribution, i tables
Show i-th of station, LiFor the quality characteristic vector that the N × 1 of i-th of station output products is tieed up, Γ indicates a production system institute
The constant coefficient matrix of decision, fiFor the failure source vector that the N × 1 of i-th of station is tieed up, εiThe measurement tieed up for the N × 1 of i-th of station
The noise vector that noise and model do not include；
Second establishes module 303, and for establishing SoV models out of control, the SoV models out of control are：
Wherein,
If the source of trouble of current station is linearly related with the source of trouble of previous station, i.e. fi=Φ fi-1+vi, wherein Φ tables
Show unit diagonal matrix, viIndicate independently of each other and meet the random number matrix of normal distribution；
Second determining module 304, for when system is in slave mode, being determined in first by the first preset formula group
Heart line value, the first upper limit value and the first lower limiting value；
Third determining module 305, for when system jam, the second center line to be determined by the second preset formula group
Value, the second upper limit value and the second lower limiting value；
4th determining module 306, for when systems stay breaks down, being determined in third by third preset formula group
Heart line value, third upper limit value and third lower limiting value；
Optionally, second determining module 304 is specifically used for：
When system is in slave mode, by the first preset formula group determine the first center line value, the first upper limit value and
First lower limiting value；
The first preset formula group is specially：
Wherein, statisticDistribution：
Wherein m=1,2 ..., t, A are control limit coefficients,For statisticStandard deviation；
Optionally, the third determining module 305, is specifically used for：
When system jam, the second center line value, the second upper limit value and second are determined by the second preset formula group
Lower limiting value；
The second preset formula group is specially：
Wherein：
Optionally, the 4th determining module 306, is specifically used for：
When systems stay breaks down, by third preset formula group determine third centerline value, third upper limit value and
Third lower limiting value；
The third preset formula group is specially：
Wherein：
Specific implementation mode in the present embodiment illustrates which is not described herein again in the above-described embodiments.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of system and module, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In several embodiments provided herein, it should be understood that disclosed module and method can pass through it
Its mode is realized.For example, module embodiments described above are only schematical, for example, the division of the module, only
Only a kind of division of logic function, formula that in actual implementation, there may be another division manner, such as multiple module or components can be tied
Another system is closed or is desirably integrated into, or some features can be ignored or not executed.Another point, it is shown or discussed
Mutual coupling, direct-coupling or communication connection can be the INDIRECT COUPLING or logical by some interfaces, device or module
Letter connection can be electrical, machinery or other forms.
The module illustrated as separating component may or may not be physically separated, aobvious as module
The component shown may or may not be physical module, you can be located at a place, or may be distributed over multiple
On network module.Some or all of module therein can be selected according to the actual needs to realize the mesh of this embodiment scheme
's.
In addition, each function module in each embodiment of the present invention can be integrated in a processing module, it can also
That modules physically exist alone, can also two or more modules be integrated in a module.Above-mentioned integrated mould
The form that hardware had both may be used in block is realized, can also be realized in the form of software function module.
The above, the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although with reference to before
Stating embodiment, invention is explained in detail, it will be understood by those of ordinary skill in the art that：It still can be to preceding
The technical solution recorded in each embodiment is stated to modify or equivalent replacement of some of the technical features；And these
Modification or replacement, the spirit and scope for various embodiments of the present invention technical solution that it does not separate the essence of the corresponding technical solution.
Claims (8)
1. a kind of R2R manufacture systems fault coverage determines that method, feature are, including：
Determine processing quality feature vector LiStatisticMean vector μf(μf≠ 0) and covariance matrix ∑f；
Wherein,Wherein, d'L,mFor dL,mDerivative；
Wherein, dL,mFor direction vector；
Wherein, m is failure offset direction line number, hL,mFor Li,mChange in Mean amount；
hL,m=Γ Φi-τμf,m+ΓΦi-(τ+1)μf,m+…+ΓΦ2μf,m+ΓΦμf,m+Γμf,m；
According to system failure source fiChange in Mean direction calculating LiChange direction, obtain updated quality characteristic vector
Li,m；
Controlled SoV models are established, the controlled SoV models are：
Wherein,
Wherein, Φ indicates unit diagonal matrix, viThe random number matrix of normal distribution is indicated independently of each other and meets, i indicates i-th
A station, LiFor the quality characteristic vector that the N × 1 of i-th of station output products is tieed up, Γ indicates what a production system was determined
Constant coefficient matrix, fiFor the failure source vector that the N × 1 of i-th of station is tieed up, εiFor i-th of station N × 1 tie up measurement noise and
The noise vector that model does not include；
SoV models out of control are established, the SoV models out of control are：
Wherein,
If the source of trouble of current station is linearly related with the source of trouble of previous station, fi=Φ fi-1+vi, wherein Φ expression lists
Position diagonal matrix, viIndicate independently of each other and meet the random number matrix of normal distribution；
When system is in slave mode, the first center line value, the first upper limit value and first are determined by the first preset formula group
Lower limiting value；
When system jam, the second center line value, the second upper limit value and the second lower limit are determined by the second preset formula group
Value；
When systems stay breaks down, third centerline value, third upper limit value and third are determined by third preset formula group
Lower limiting value.
2. R2R manufacture systems fault coverage according to claim 1 determines method, which is characterized in that described first is default
Formula group is specially：
Wherein, statisticDistribution：
Wherein m=1,2 ..., t, A are control limit coefficients,For statisticStandard deviation.
3. R2R manufacture systems fault coverage according to claim 2 determines method, which is characterized in that described second is default
Formula group is specially：
Wherein：
4. R2R manufacture systems fault coverage according to claim 3 determines method, which is characterized in that the third is default
Formula group is specially：
Wherein：
5. a kind of R2R manufacture systems fault coverage determining device, feature are, including：
First determining module, for determining processing quality feature vector LiStatisticMean vector μf(μf≠ 0) it and assists
Variance matrix ∑f；
Wherein,Wherein, d'L,mFor dL,mDerivative；
Wherein, dL,mFor direction vector；
Wherein, m is failure offset direction line number, hL,mFor Li,mChange in Mean amount；
hL,m=Γ Φi-τμf,m+ΓΦi-(τ+1)μf,m+…+ΓΦ2μf,m+ΓΦμf,m+Γμf,m；
According to system failure source fiChange in Mean direction calculating LiChange direction, obtain updated quality characteristic vector
Li,m；
First establishes module, and for establishing controlled SoV models, the controlled SoV models are：
Wherein,
Wherein, Φ indicates unit diagonal matrix, viThe random number matrix of normal distribution is indicated independently of each other and meets, i indicates i-th
A station, LiFor the quality characteristic vector that the N × 1 of i-th of station output products is tieed up, Γ indicates what a production system was determined
Constant coefficient matrix, fiFor the failure source vector that the N × 1 of i-th of station is tieed up, εiFor i-th of station N × 1 tie up measurement noise and
The noise vector that model does not include；
Second establishes module, and for establishing SoV models out of control, the SoV models out of control are：
Wherein,
If the source of trouble of current station is linearly related with the source of trouble of previous station, fi=Φ fi-1+vi, wherein Φ expression lists
Position diagonal matrix, viIndicate independently of each other and meet the random number matrix of normal distribution；
Second determining module, for when system is in slave mode, by the first preset formula group determine the first center line value,
First upper limit value and the first lower limiting value；
Third determining module, for when system jam, the second center line value, second to be determined by the second preset formula group
Upper limit value and the second lower limiting value；
4th determining module, for when systems stay breaks down, by third preset formula group determine third centerline value,
Third upper limit value and third lower limiting value.
6. R2R manufacture systems fault coverage determining device according to claim 5, which is characterized in that described second determines
Module is specifically used for：
When system is in slave mode, the first center line value, the first upper limit value and first are determined by the first preset formula group
Lower limiting value；
The first preset formula group is specially：
Wherein, statisticDistribution：
Wherein m=1,2 ..., t, A are control limit coefficients,For statisticStandard deviation.
7. R2R manufacture systems fault coverage determining device according to claim 6, which is characterized in that the third determines
Module is specifically used for：
When system jam, the second center line value, the second upper limit value and the second lower limit are determined by the second preset formula group
Value；
The second preset formula group is specially：
Wherein：
8. R2R manufacture systems fault coverage determining device according to claim 7, which is characterized in that the described 4th determines
Module is specifically used for：
When systems stay breaks down, third centerline value, third upper limit value and third are determined by third preset formula group
Lower limiting value；
The third preset formula group is specially：
Wherein：
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