CN108490908A - A kind of dynamic distributed monitoring method towards gigawatt extra-supercritical unit variable parameter operation - Google Patents
A kind of dynamic distributed monitoring method towards gigawatt extra-supercritical unit variable parameter operation Download PDFInfo
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Abstract
The invention discloses a kind of dynamic distributed monitoring methods towards gigawatt extra-supercritical unit variable parameter operation.The identification that the present invention this typical extensive non-stationary process for gigawatt extra-supercritical unit, the sparse cointegrating analysis of R. concomitans and dynamic feature extraction method change for fault detect and operating mode.Large scale process variable automatically can be divided into different set of variables by this method, while extraction process static state and multidate information are distinguished in each set of variables, and the two is modeled and monitored respectively.It realizes depth mining process information, and effectively realizes transformation and the actual process failure of differentiation process nominal situation.This method effective solution process monitoring difficulty problem of this extensive non-stationary dynamical system of gigawatt extra-supercritical unit, substantially increase the performance of process monitoring, field engineer is contributed to accurately to grasp operating states of the units, to ensure the safety of gigawatt extra-supercritical unit and improve productivity effect.
Description
Technical field
The invention belongs to extensive non-stationary process statistical monitoring fields, super towards gigawatt especially for one kind
The dynamic distributed monitoring method of criticality benchmark variable parameter operation.
Background technology
Power industry is the emphasis of Chinese national economy most important basic energy resource industry and Strategy for economic development.China lacks
The few gas of oil, rich coal resources, coal electricity is always China's main power source for a long time.Coal fired generation process is increasingly sophisticated
Change, enlargement development.Large-scale coal fired generation process environment is complicated, and scene has the features such as high temperature, high pressure, strong noise.1001000
Watt extra-supercritical unit be in the world it is state-of-the-art efficiently, large capacity coal powerplant, there is apparent efficiency advantage, be me
The representative unit and main flow direction of state's electric power industry development.China's gigawatt extra-supercritical unit ownership the first in the world,
And development space is huge.
Gigawatt extra-supercritical unit has apparent extensive, non-stationary and dynamic characteristic.In order to ensure million
The safety of kilowatt extra-supercritical unit simultaneously improves productivity effect, it is necessary to use effective process monitoring method.Process monitoring is just
It is monitoring process operation state, alarm is sent out in time when occurring extremely.With the development of technology, data are obtained in industry spot
Become increasingly to be easy, a large amount of procedural information has been contained in process data, then, the process monitoring method based on data has become
For the hot spot of research.
Forefathers have done corresponding research to the process monitoring based on data.Principal component analysis (PCA), offset minimum binary
(PLS) etc. Multielement statistical analysis methods have been widely used in data procedures monitoring field.However, these methods assumed that
Journey is stable, and there is no the dynamic characteristics for considering process.Actually since process is disturbed in gigawatt extra-supercritical unit
Dynamic, the reasons such as ageing equipment process can have apparent non-stationary property.Simultaneously because frequently peak regulation process is in dynamic change
Among.Therefore, gigawatt extra-supercritical unit has apparent extensive, non-stationary and dynamic characteristic, this is supervised to process
Survey brings prodigious challenge.
For gigawatt extra-supercritical unit, this typical extensive, dynamic non-stationary process proposes a kind of knot to the present invention
Close the process monitoring method of sparse cointegrating analysis and behavioral characteristics extraction.This method goes out non-stationary identification variables in the process
Come, establishes sparse co-integration model using these non-stationary variables and variable is divided into different set of variables using alternative manner.It carries
Go out the method for dynamic feature extraction for multidate information and static information to be distinguished from, and the two is modeled and monitored respectively.
Variable can be automatically divided into different set of variables by this method, while can effectively extract process dynamics and static letter
Breath, the effective change for distinguishing normal operating conditions and actual process failure, substantially increase the performance of process monitoring.To current
Until, there is not yet research related to the present invention is reported.
Invention content
It is an object of the invention to be directed to gigawatt extra-supercritical unit this typical extensive, dynamic non-stationary mistake
Journey provides a kind of fault detection method and diagnostic method towards large-scale Thermal generation unit non-stationary process.
The purpose of the present invention is what is be achieved through the following technical solutions：One kind is towards large-scale Thermal generation unit non-stationary mistake
The fault detection method of journey, includes the following steps：
(1) normal processes data are obtained：If in a gigawatt extra-supercritical unit including J process variable, every time
Sampling can obtain the vector of a 1 × J, and the two-dimensional matrix X under a normal processes is obtained after sampling M timesn(M×J)；
(2) non-stationary variable is identified：Two-Dimensional Moment is identified using Augmented Dickey-Fuller (ADF) method of inspection
Battle array XnNon-stationary variable in (M × J) obtains non-stationary variable data matrix Xns(M × N), N indicate non-stationary variable number；
(3) the non-stationary variable data matrix X obtained in (2) is utilizedns(M × N)=[x1,x2,…,xN], xt=(x1,
x2,…,xN)TEstablish sparse co-integration model, wherein xtFor data matrix XnsThe transposition of a row vector in (M × N) indicates
The sampled value of t moment, t=1,2 ..., M.It establishes sparse co-integration model and specifically includes following sub-step：
(3.1) to xtEstablish Vector Autoression Models
xt=Π1xt-1+…+Πpxt-p+c+μt (1)
Wherein, Π1~ΠpIt is the coefficient matrix of (N × N), μtFor (N × 1) matrix, white Gaussian noise, μ are indicatedt~N
(0, Ξ), c are (N × 1) matrix, indicate constant, p is model order；
(3.2) x is subtracted at formula (1) both endst-1Obtain error correction model
Wherein,INFor the unit matrix of (N × N)；
(3.3) Γ in step (3.2) is decomposed into matrix Γ=Α Β of two sequency spectrumsT, formula (2) becomes
Wherein, Α (N × R), Β (N × R)；
(3.4) the whole vector matrix Β of association in formula (3) is estimated by Maximum Likelihood Estimation
Wherein, L (*) indicates maximum likelihood function, the mark of tr (*) representing matrix.X=(Δ xp+1,...,ΔxM)T, Y=
(ΔYp+1,...,ΔYM)T,L=p+1 ..., M, Ω=(Ω1,...,Ωp-1)T, Z=
(xt-1,...,xM-1)T, Θ=Ξ-1；
(3.5) characteristic equation solution procedure can be converted into the Maximum-likelihood estimation of formula (4)
Wherein,
Wherein parameter matrix ΘiAnd Φi, i=1,2 ..., p-1 can be acquired by least-squares algorithm；
(3.6) penalty is added to the object function of formula (4) and obtains the sparse whole vector of association
Wherein, P1, P2, P3For parameter Β, the penalty of Ω, Θ, using a normal form；Wherein Β is to assist whole vector
Matrix, Ω, Θ are parameters to be estimated；Adjusting parameter λ1And λ2It is determined using crosscheck, adjusting parameter λ3Using Bayes
Information criterion determines.By the way that the whole vector of sparse association can be obtained to the solution of formula (6).
(4) variable can be divided using the sparse association whole vector obtained in formula (6), specifically includes following sub-step
Suddenly：
(4.1) the sparse whole vector matrix Β of association is obtained according to formula (6)s(N × K)=[βs,1,βs,2,...,βs,K], wherein
K indicates the number of the sparse whole vector of association；
(4.2) steady residual sequence is obtained according to the sparse whole vector of associationWherein k=1,2 ..., K, t=1,
2 ..., M.The consistent level for weighing residual sequence, and record residual difference sequence γ are examined using ADFk,tADF test statistics tk
(4.3) the test statistics t to obtainingkCarry out ascending sort, the corresponding sparse association of minimum inspection statistics magnitude
Whole vector is retained.The corresponding variable of nonzero element in the sparse whole vector of association is assigned in subgroup, and is denoted as Xb。
(4.4) by XbIn variable from initial data concentrate remove, raw data set is denoted as X at this timeL。
(4.5) iteration step (4.1)-(4.4) are all assigned in different subgroups until all variables, original at this time
Variable is divided into Z different set of variables.
(5) local static, dynamic monitoring statistic are established in each set of variables：
(5.1) local static monitoring and statistics amount is established
Wherein,
γs,b,tFor the steady residual sequence in each set of variables, Bf,bFor the whole moment of a vector of association calculated in each set of variables
Battle array, Λs,b=(XbBf,b)T(XbBf,b)/(M-1) (b=1 ..., Z) be covariance matrix, M be sampled point number.Z is variable point
The number of group indicates that original variable is divided into Z set of variables, xb,tFor XbThe transposition of middle row vector indicates the sampling in t moment
Point.
(5.2) local dynamic station monitoring and statistics amount is established
Wherein,
B indicates that b-th of set of variables, p indicate time lag, Δ xp+1,bIndicate the Difference Terms, that is, Δ x at p+1 momentp+1,b=
xp+1,b-xp,b, xp,bFor XbThe transposition of middle row vector indicates the sampled point at the p moment, ΘbAnd Φb(b=1 ..., Z) it is to wait estimating
Parameter is counted, can be acquired by least square.Be,bAnd Bf,bFor EbAnd FbCalculate the weight matrix obtained after canonical correlation analysis.It is dynamic
State monitoring and statistics amount is
Wherein, te,bFrom matrix Te,b, it is Te,bMiddle column vector.
(6) global monitoring and statistics amount is established
(6.1) global static monitoring techniques statistic is established：
Wherein, Z indicates the number of set of variables； PS(xb)=P (xb|Ns)P(Ns)
+P(xb|Fs)P(Fs)；P(Ns) it is confidence interval, i.e. P (Ns)=α, P (Fs)=1- α； For sample xb'sMonitoring and statistics amount,For monitoring and statistics amountControl
System limit；
(6.2) global dynamic monitoring statistic is established：
Wherein, Z indicates the number of set of variables； Pe(xb)=P (xb|Ne)P(Ne)
+P(xb|Fe)P(Fe)；P(Ne) it is confidence interval, i.e. P (Ne)=α, P (Fe)=1- α； For sample xb'sMonitoring and statistics amount,For monitoring and statistics amount's
Control limit；
(7) online process monitoring
(7.1) to new collected non-stationary variable sample xnew,t(N × 1) carries out variable grouping, which is divided into Z
Set of variables, that is, xnew,b,t, b=1,2 ..., Z.
(7.2) method recorded according to step 5 calculates local static monitoring and statistics amount
xnewi,tWith xnew,t(N × 1) what relationship, xnewi,tWhat is indicated
(7.3) local dynamic station monitoring and statistics amount is calculated
(7.4) global static, dynamic monitoring statistic is calculated
IfWithIt is limited beyond control, it is meant that static, dynamic long-run equilibrium relationship is broken, process hair
Raw failure；IfWhile alarm,Normal range (NR) is returned to after being limited beyond control, was meaned at this time
The operating point of journey changes；IfWithIt is all limited without departing from control, means that process is operated in new work at this time
Static state, dynamic long-run equilibrium relationship under condition, but between variable are not broken or the operating mode is included in modeling data.
If local static, dynamic monitoring statistic are influenced by failure, BICsOr BICeAlso exceed control limit, anticipate at this time
Taste the local state that the failure not only influences process, while also affecting global state, if BICsOr BICeDo not exceed
Control limit, indicates the local state of the failure influence process.
Beneficial effects of the present invention are：Extensive non-stationary process can be divided into different set of variables and reduced by the present invention
The complexity of process, while by monitoring static and dynamic change in different set of variables, the identification of operating mode variation may be implemented
With the detection to failure, provides local monitoring information while providing global monitoring information.
Description of the drawings
Fig. 1 is the flow chart of the dynamic distributed monitoring the present invention is based on sparse cointegrating analysis；
Fig. 2 is area monitoring's result of this method；
Fig. 3 is the global monitoring result of this method；
Fig. 4 is the monitoring result of traditional cointegrating analysis.
Fig. 5 is the monitoring result of traditional cointegrating analysis.
Specific implementation mode
Below in conjunction with the accompanying drawings and specific example, invention is further described in detail.
Gigawatt extra-supercritical unit is typical non-stationary process, and a portion variable has apparent non-stationary special
Property, such as condenser circulating water pressure, high-pressure heater initial steam pressure, oxygen-eliminating device incoming condensing water amount.The present invention is to praise Hua Ran
For the unit of power plant 8, the power of the unit is 10000MW, is gigawatt extra-supercritical unit, including 159
Process variable, these variables are related to pressure, temperature, water level, flow velocity etc..
As shown in Figure 1, the present invention is a kind of towards the dynamic distributed of gigawatt extra-supercritical unit variable parameter operation
Monitoring method includes the following steps：
Step 1：Acquisition process data：If including J process variable in a gigawatt extra-supercritical unit, adopt every time
Sample can obtain the vector of a 1 × J, and the data obtained after sampling K times can be described as a two-dimensional matrix X (K × J).It obtains
Take normal data Xn(M × J), wherein subscript n indicate normal data.In this example, the sampling period is 1 minute, for normal number
According to 740 samples of acquisition, 159 process variables.So normal data sample is Xn(740×159)。
Step 2：Identify non-stationary variable：It is normal using the identification of Augmented Dickey-Fuller (ADF) method of inspection
Data XnNon-stationary variable in (740 × 159), is examined by ADF, wherein 51 variables are non-stationary variable, is obtained non-flat
Steady variable data Xns(740 × 51), wherein subscript n s indicate Non-stationary Data.Below for convenience of expression, by sampled point number 740
It is indicated with M, non-stationary variables number 51 is indicated with N.
Step 3：Utilize the non-stationary variable data matrix X obtained in (2)ns(M × N)=[x1,x2,…,xN], xt=
(x1,x2,…,xN)TEstablish sparse co-integration model, wherein xtFor data matrix XnsThe transposition of a row vector in (M × N), table
Show the sampled value in t moment, t=1,2 ..., M.It establishes sparse co-integration model and specifically includes following sub-step：
(3.1) to xtEstablish Vector Autoression Models
xt=Π1xt-1+…+Πpxt-p+c+μt (1)
Wherein, Π1~ΠpIt is the coefficient matrix of (N × N), μtFor (N × 1) matrix, white Gaussian noise, μ are indicatedt~N
(0, Ξ), c are (N × 1) matrix, indicate constant, p is model order；
(3.2) x is subtracted at formula (1) both endst-1Obtain error correction model
Wherein,INFor the unit square of (N × N)
Battle array；
(3.3) Γ in step (3.2) is decomposed into matrix Γ=Α Β of two sequency spectrumsT, formula (2) becomes
Wherein, Α (N × R), Β (N × R)；
(3.4) the whole vector matrix Β of association in formula (3) is estimated by Maximum Likelihood Estimation
Wherein, L (*) indicates maximum likelihood function, the mark of tr (*) representing matrix.X=(Δ xp+1,...,ΔxM)T, Y=
(ΔYp+1,...,ΔYM)T,L=p+1 ..., M, Ω=(Ω1,...,Ωp-1)T, Z=
(xt-1,...,xM-1)T, Θ=Ξ-1；
(3.5) characteristic equation solution procedure can be converted into the Maximum-likelihood estimation of formula (4)
Wherein,
Wherein parameter matrix ΘiAnd Φi, i=1,2 ..., p-1 can be acquired by least-squares algorithm；
(3.6) penalty is added to the object function of formula (4) and obtains the sparse whole vector of association
Wherein, P1, P2, P3For parameter Β, the penalty of Ω, Θ, using a normal form；Wherein Β is to assist whole vector
Matrix, Ω, Θ are parameters to be estimated；Adjusting parameter λ1And λ2It is determined using crosscheck, adjusting parameter λ3Using Bayes
Information criterion determines.By the way that the whole vector of sparse association can be obtained to the solution of formula (6).
(4) variable can be divided using the sparse association whole vector obtained in formula (6), specifically includes following sub-step
Suddenly：
(4.1) the sparse whole vector matrix Β of association is obtained according to formula (6)s(N × K)=[βs,1,βs,2,...,βs,K], wherein
K indicates the number of the sparse whole vector of association；
(4.2) steady residual sequence is obtained according to the sparse whole vector of associationWherein k=1,2 ..., K, t=1,
2 ..., M.The consistent level for weighing residual sequence, and record residual difference sequence γ are examined using ADFk,tADF test statistics tk
(4.3) the test statistics t to obtainingkCarry out ascending sort, the corresponding sparse association of minimum inspection statistics magnitude
Whole vector is retained.The corresponding variable of nonzero element in the sparse whole vector of association is assigned in subgroup, and is denoted as Xb。
(4.4) by XbIn variable from initial data concentrate remove, raw data set is denoted as X at this timeL。
(4.5) iteration step (4.1)-(4.4), all variables are all assigned in 5 different subgroups.
(5) local static, dynamic monitoring statistic are established in each set of variables：
(5.1) local static monitoring and statistics amount is established
Wherein,
γs,b,tFor the steady residual sequence in each set of variables, Bf,bFor the whole moment of a vector of association calculated in each set of variables
Battle array, Λs,b=(XbBf,b)T(XbBf,b)/(M-1) (b=1 ..., 5) be covariance matrix, M be sampled point number. xb,tFor Xb
The transposition of middle row vector indicates the sampled point in t moment.
(5.2) local dynamic station monitoring and statistics amount is established
Wherein,
Indicate that b-th of set of variables, p indicate time lag, Δ xp+1,bIndicate the Difference Terms, that is, Δ x at p+1 momentp+1,b=xp+1,b-xp,b,
xp,bFor XbThe transposition of middle row vector indicates the sampled point at the p moment, ΘbAnd Φb(b=1 ..., Z) is parameter to be estimated, can
It is acquired by least square.Be,bAnd Bf,bFor EbAnd FbCalculate the weight matrix obtained after canonical correlation analysis.Dynamic monitoring counts
Amount is
Wherein, te,bFrom matrix Te,b, it is Te,bMiddle column vector.
(6) global monitoring and statistics amount is established
(6.1) global static monitoring techniques statistic is established：
Wherein,PS(xb)=P (xb|Ns)P(Ns)+P(xb|Fs)P(Fs)；P(Ns) be
Confidence interval, i.e. P (Ns)=α, P (Fs)=1- α； For sample xb'sMonitoring and statistics amount,For monitoring and statistics amountControl limit；
(6.2) global dynamic monitoring statistic is established：
Wherein,Pe(xb)=P (xb|Ne)P(Ne)+P(xb|Fe)P(Fe)；P(Ne) be
Confidence interval, i.e. P (Ne)=α, P (Fe)=1- α； For sample xb'sMonitoring and statistics amount,For monitoring and statistics amountControl limit；
Step 7：Online process monitoring
(7.1) to new collected non-stationary variable sample xnew,t(N × 1) carries out variable grouping, which is divided into Z
Set of variables, that is, xnew,b,t, b=1,2 ..., 5.
(7.2) method recorded according to step 5 calculates local static monitoring and statistics amount
(7.3) local dynamic station monitoring and statistics amount is calculated
(7.4) global static, dynamic monitoring statistic is calculated
IfWithMean that static, dynamic long-run equilibrium relationship is broken beyond control limit, at this time process
It breaks down；IfWhile alarm,Normal range (NR) is returned to after being limited beyond control, is meaned at this time
The operating point of process changes；IfWithIt is all limited without departing from control, means that process may be operated at this time
Static state, dynamic long-run equilibrium relationship under new operating mode, but between variable are not broken or the operating mode is included in modeling data
In.
If local static, dynamic monitoring statistic are influenced by failure, BICsOr BICeAlso exceed control limit, anticipate at this time
Taste the local state that the failure not only influences process, while also affecting global state, if BICsOr BICeDo not exceed
Control limit indicates the local state of the failure influence process.
Table 1 is set of variables dividing condition, and non-stationary variable is divided into 5 different set of variables.It can be seen from Fig. 2
The load of gigawatt extra-supercritical unit often changes, it means that the operating mode of unit does not stop to change.Fig. 3 is the prison of this method
Survey result, it can be seen that the static monitoring techniques statistic of set of variables 1-4 is beyond control limit after operating point changes, it means that
Static long-run equilibrium relationship is broken.However dynamic monitoring statistic connects only after operating point changes beyond control limit
Get off to return to normal range (NR).The static state and dynamic monitoring statistic of set of variables 5 are all limited without departing from control, this indicates the change of work
Change does not influence the variable in set of variables 5.Fig. 4 is global monitoring result, it can be seen that global monitoring result and area monitoring
As a result consistent.Fig. 5 illustrates the monitoring result of traditional cointegrating analysis, it can be seen that monitoring and statistics amount after operating point changes
It is constantly in alarm condition, this can impact field engineer, so that engineer is mistakenly considered process and be in abnormality.Pass through
This method can help field engineer effectively to distinguish the influence of the change and failure of process operating point to process, ensure that hundred
The Effec-tive Function of ten thousand kilowatts of extra-supercritical units.
1. set of variables division result of table
| Set of variables | Variable |
| 1 | 4,7,15,16,20,22,26,27,29,30,33,34,37,39,43,47,49 |
| 2 | 5,6,13,18,24,31,35,40,41,42,45,50,51 |
| 3 | 28,44,46,48 |
| 4 | 8,9,11,12,17,19,21,23,25,36,38, |
| 5 | 1,2,3,10,14,32, |
Claims (5)
1. a kind of dynamic distributed monitoring method towards gigawatt extra-supercritical unit variable parameter operation, which is characterized in that
Include the following steps：
(1) normal processes data are obtained：If including J process variable in a gigawatt extra-supercritical unit, sample every time
The vector of a 1 × J can be obtained, the two-dimensional matrix X under a normal processes is obtained after sampling M timesn(M×J)；
(2) non-stationary variable is identified：Using Augmented Dickey-Fuller (ADF) method of inspection identification two-dimensional matrix Xn(M
× J) in non-stationary variable, obtain non-stationary variable data matrix Xns(M×N)；
(3) the non-stationary variable data matrix X obtained in (2) is utilizedns(M × N)=[x1,x2,…,xN], xt=(x1,x2,…,
xN)TEstablish sparse co-integration model.It establishes sparse co-integration model and specifically includes following sub-step：
(3.1) to xtEstablish Vector Autoression Models
xt=Π1xt-1+…+Πpxt-p+c+μt (1)
Wherein, Π1~ΠpIt is the coefficient matrix of (N × N), μtFor (N × 1) matrix, white Gaussian noise, μ are indicatedt~N (0, Ξ),
C is (N × 1) matrix, indicates constant；
(3.2) x is subtracted at formula (1) both endst-1Obtain error correction model
Wherein,INFor the unit matrix of (N × N)；
(3.3) Γ in step (3.2) is decomposed into matrix Γ=Α Β of two sequency spectrumsT, formula (2) becomes
Wherein, Α (N × R), Β (N × R)；
(3.4) the whole vector matrix Β of association in formula (3) is estimated by Maximum Likelihood Estimation
Wherein, L (*) indicates maximum likelihood function, the mark of tr (*) representing matrix.X=(Δ xp+1,...,ΔxM)T, Y=(Δs
Yp+1,...,ΔYM)T,Ω=(Ω1,...,Ωp-1)T, Z=
(xt-1,...,xM-1)T, Θ=Ξ-1；
(3.5) characteristic equation solution procedure can be converted into the Maximum-likelihood estimation of formula (4)
Wherein,
Wherein parameter matrix ΘiAnd Φi, i=1,2 ..., p-1 can be acquired by least-squares algorithm；
(3.6) penalty is added to the object function of formula (4) and obtains the sparse whole vector of association
Wherein, P1, P2, P3For parameter Β, the penalty of Ω, Θ, using a normal form；Wherein Β is to assist whole vector matrix,
Ω, Θ are parameters to be estimated；Adjusting parameter λ1And λ2It is determined using crosscheck, adjusting parameter λ3Using Bayesian Information
Criterion determines.By the way that the whole vector of sparse association can be obtained to the solution of formula (6).
(4) variable can be divided using the sparse association whole vector obtained in formula (6), specifically includes following sub-step：
(4.1) the sparse whole vector matrix Β of association is obtained according to formula (6)s(N × K)=[βs,1,βs,2,...,βs,K], wherein K is indicated
The number of the sparse whole vector of association；
(4.2) steady residual sequence is obtained according to the sparse whole vector of associationWherein k=1,2 ..., K, t=1,2 ...,
M.The consistent level for weighing residual sequence, and record residual difference sequence γ are examined using ADFk,tADF test statistics tk
(4.3) the test statistics t to obtainingkCarry out ascending sort, the corresponding sparse whole vector of association of minimum inspection statistics magnitude
It is retained.The corresponding variable of nonzero element in the sparse whole vector of association is assigned in subgroup, and is denoted as Xb。
(4.4) by XbIn variable from initial data concentrate remove, raw data set is denoted as X at this timeL。
(4.5) iteration step (4.1)-(4.4) are all assigned in different subgroups until all variables, at this time original variable
It is divided into Z different set of variables.
(5) local static, dynamic monitoring statistic are established in each set of variables：
(5.1) local static monitoring and statistics amount is established
Wherein,
γs,b,tFor the steady residual sequence in each set of variables, Bf,bFor the whole vector matrix of association calculated in each set of variables,
Λs,b=(XbBf,b)T(XbBf,b)/(M-1) (b=1 ..., Z) it is covariance matrix.Z is the number of variable grouping, is indicated original
Variable is divided into Z set of variables, xb,tFor XbThe transposition of middle row vector indicates the sampled point in t moment.
(5.2) local dynamic station monitoring and statistics amount is established
Wherein,
B indicates that b-th of set of variables, p indicate time lag, Δ xp+1,bIndicate the Difference Terms, that is, Δ x at p+1 momentp+1,b=
xp+1,b-xp,b, xp,bFor XbThe transposition of middle row vector indicates the sampled point at the p moment, ΘbAnd Φb(b=1 ..., Z) it is to wait estimating
Parameter is counted, can be acquired by least square.Be,bAnd Bf,bFor EbAnd FbCalculate the weight matrix obtained after canonical correlation analysis.It is dynamic
State monitoring and statistics amount is
Wherein, te,bFrom matrix Te,b, it is Te,bMiddle column vector.
(6) global monitoring and statistics amount is established
(6.1) global static monitoring techniques statistic is established：
Wherein, Z indicates the number of set of variables；PS(xb)=P (xb|Ns)P(Ns)+P(xb|
Fs)P(Fs)；P(Ns) it is confidence interval, i.e. P (Ns)=α, P (Fs)=1- α； For sample xb'sMonitoring and statistics amount,For monitoring and statistics amount's
Control limit；
(6.2) global dynamic monitoring statistic is established：
Wherein, Z indicates the number of set of variables； Pe(xb)=P (xb|Ne)P(Ne)+P
(xb|Fe)P(Fe)；P(Ne) it is confidence interval, i.e. P (Ne)=α, P (Fe)=1- α； For sample xb'sMonitoring and statistics amount,For monitoring and statistics amount's
Control limit；
(7) online process monitoring
(7.1) to new collected non-stationary variable sample xnew,t(N × 1) carries out variable grouping, which is divided into Z variable
Group is xnew,b,t, b=1,2 ..., Z.
(7.2) method recorded according to step 5 calculates local static monitoring and statistics amount
xnewi,tWith xnew,t(N × 1) what relationship, xnewi,tWhat is indicated
(7.3) local dynamic station monitoring and statistics amount is calculated
(7.4) global static, dynamic monitoring statistic is calculated
IfWithIt is limited beyond control, it is meant that static, dynamic long-run equilibrium relationship is broken, and event occurs for process
Barrier；IfWhile alarm,Normal range (NR) is returned to after being limited beyond control, means process at this time
Operating point changes；IfWithIt is all limited without departing from control, means that process is operated in new operating mode at this time
Under, but the static state, dynamic long-run equilibrium relationship between variable are not broken or the operating mode is included in modeling data.
If local static, dynamic monitoring statistic are influenced by failure, BICsOr BICeAlso exceed control limit, mean at this time
The failure not only influences the local state of process, while also affecting global state, if BICsOr BICeDo not exceed control
Limit, indicates the local state of the failure influence process.
2. according to the method described in claim 1, it is characterized in that, in the step 2, N indicates non-stationary variable number.
3. according to the method described in claim 1, it is characterized in that, in the step 3, xtFor data matrix XnsIn (M × N)
The transposition of one row vector indicates the sampled value in t moment, t=1,2 ..., M.
4. according to the method described in claim 1, it is characterized in that, in the step 3.1, p is model order.
5. according to the method described in claim 1, it is characterized in that, in the step 5.1, M is sampled point number.
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