CN103713628B  Fault diagnosis method based on signed directed graph and data constitution  Google Patents
Fault diagnosis method based on signed directed graph and data constitution Download PDFInfo
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 CN103713628B CN103713628B CN201310753722.XA CN201310753722A CN103713628B CN 103713628 B CN103713628 B CN 103713628B CN 201310753722 A CN201310753722 A CN 201310753722A CN 103713628 B CN103713628 B CN 103713628B
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Abstract
The invention discloses a fault diagnosis method based on a signed directed graph and data constitution. The fault diagnosis method includes the steps of firstly, collecting normal data to conduct offline training, decomposing preprocessed data through a PCA, and solving a control limit of an SPE; secondly, setting up an SDG model according to a system flow graph, and determining parameters of all variables Vmask after setting the missing alarm rate, the false alarm rate and the detection offset; thirdly, collecting process data of system unknown state in real time, monitoring a CUSUM of all the variables and the SPE of a sample, if the SPE exceeds the control limit, diagnosing that a fault happens to a system, determining an effective node through the CUSUM, and conducting reconstitution on data in all compatible path directions by searching for all possible compatible paths, wherein the direction with the largest fault isolation index is taken as a real fault propagation direction, the starting node in the direction is taken as a causal variable of the fault, and an event which causes the node to be abnormal is taken as the root cause of the fault.
Description
Technical field
The present invention relates to the fault diagnosis system field of multivariate complication system, more particularly, to one kind are based on signed digraph
The method for diagnosing faults of data reconstruct.
Background technology
Constantly develop towards largescale, direction that is intelligent and complicating with industrial process control system, ask safely
Topic is increasingly becoming one of subject matter of everybody care.One of core component as Process Control System, dynamical system
Fault detection and diagnosis (fdd) technology be exactly in order to adapt to industrial system to improve reliability and reduce accident risk needs
And what formation and development was got up.In the past few decades, troubleshooting issue has obtained the extensive concern of Chinese scholars, gushes
Reveal the various methods with regard to fault detect and isolation.These methods can be divided into qualitative method and quantitative analyses two on the whole
Big class.Wherein, in quantitative analysis method, it is to pay close attention to most methods in recent years based on the method for datadriven.
In the method for diagnosing faults based on datadriven, it is to enjoy in recent years based on the method for diagnosing faults of multivariate statistics
One of method of concern.Traditional is to differentiate which variable makes corresponding statistic surpass based on the multivariate statistical method of contribution plot
Go out the most popular method of normal value, the variable that those have maximum contribution value to statistic is considered as cause fault former
Dependent variable.But the maximum deficiency of the method is, contribution margin easily transfers to its dependent variable from a variable, and that is, contribution margin is maximum
Variable be not necessarily the basic reason causing fault.Additionally, traditional is not accounted for based on the multivariate statistical method of contribution plot
To fault propagation problem in systems, therefore it is difficult to detect the basic reason causing fault.
Therefore, those skilled in the art is devoted to developing a kind of fault diagnosis based on the reconstruct of signed digraph data
Method, it is considered to fault propagation problem in systems, is diagnosed to be the basic reason causing fault effectively.
Content of the invention
In view of the drawbacks described above of prior art, the technical problem to be solved is to provide one kind to have based on symbol
To the method for diagnosing faults of figure data reconstruct, the method uses square prediction error (spe) and accumulation and (cusum) statistic
Carry out fault detect, by being reconstructed to the sample data of all compatible path direction of sdg when fault occurs, after reconstruct
The maximum direction of residual error change is considered as the direction of propagation of fault, and start node in this direction is for becoming the reason causing trouble
Amount, is diagnosed to be the basic reason causing fault effectively.
For achieving the above object, the invention provides a kind of based on signed digraph data reconstruct fault diagnosis side
Method, comprises the steps:
Step one: a number of multivariate normal data in acquisition system running, and normal to described multivariate
Data carries out pretreatment, as known state measurement data；
Step 2: pca decomposition is carried out to known state measurement data described in step one, by the measurement of described known state
Data is decomposed into pivot part and residual error portion, and obtains the control of the square prediction error of the measurement data of described known state
Limit；
Step 3: the architectural characteristic according to described system and response characteristic, set up the oriented graphical diagram of described system
(sdg), the node of described oriented graphical diagram is system univariate parameter, and sets rate of false alarm, rate of failing to report parameter, according to system
Sdg determines the parameter value of each variable vmask of described known state measurement data；
Step 4: a number of multivariable process data in realtime acquisition system running, and to described multivariate
Process data carries out pretreatment, as unknown state measurement data, counts described unknown state measurement data accumulation and cusum system
Evaluation and the square prediction error spe of described unknown state measurement data, if spe exceed step 2 control limit then it represents that
System malfunctions；
Step 5: if system does not break down, repeat step four, if system malfunctions, double by vmask
Whether effectively arm judges the node of described sdg, and for the described process data variable more than underarm on v, node symbol is respectively
"+" and "" number；
Step 6: after determining all node states, search for all of compatible path in described sdg, i.e. adjacent node symbol
It is multiplied for positive path；
Step 7: in all effective node direction and described compatible path direction, described unknown state measurement data is entered
Line reconstruction, direction maximum for reconstruct index is just set to the propagation path of fault, judges rising on the propagation path of described fault
The basic reason that beginning node occurs for causing trouble.
Described multivariate normal data pretreatment step in the better embodiment of the present invention, in described step one
For: first the described a number of multivariate normal data of collection is deducted the average of described multivariate normal data, then
Variance divided by described multivariate normal data.
In another better embodiment of the present invention, in described step 2, pca method is chosen according to eigenvalue contribution rate
Pivot is it is desirable to contribution rate is more than 85%.
In the better embodiment of the present invention, in described step 2, square prediction error control limit computing formula isWherein (1 α) × 100% is the described confidence level controlling limit, Hereλ_{i}For described pretreated described multivariate normal data sample
The ith big eigenvalue of this covariance matrix.
In another better embodiment of the present invention, described in described step 3, the parameter of vmask is:And h=d*k, wherein k is the slope of v, and d is nearest sampled point with a distance from v fixed point, h
For nearest sampled point with a distance from the upper underarm of v, α is rate of false alarm, and β is rate of failing to report, and δ is the sideplay amount (sample that can detect
The multiple of standard deviation), σ_{x}Standard deviation for sample.
In the better embodiment of the present invention, in described step 7 if reconstruct direction is exactly the propagation path of fault,
The square prediction error of the unknown state measurement data after so reconstructing should obtain the reduction of maximum, the subtracting of square prediction error
Shown in little degree such as formula (1):
Its span is wherein y between 0～1^{spe}For reconstruct before sample square prediction error,After reconstruct
The square prediction error of unknown state measurement data,For the projection in residual error space for the ζ, that is,C is the master of measurement
First space projection matrix, in formula (1)The η making^{2}Maximum direction is just the propagation path of fault, propagates
The basic reason that start node on path then occurs for fault.
What the present invention provided considers fault in systems based on the method for diagnosing faults of signed digraph data reconstruct
Propagation problem, by sdg data reconstruct combine, entered by the sample on compatible paths all to sdg when fault occurs
Line reconstruction, the wherein maximum direction of reconstruct index are considered as the direction of propagation of physical fault, and the initial section on the direction of propagation
Point is considered as the basic reason variable causing fault.By the architectural characteristic of system is combined with datadriven method, make
Obtaining the more traditional contribution drawing method of the present invention has more preferable diagnosis effect to the basic reason causing fault.
Technique effect below with reference to design, concrete structure and generation to the present invention for the accompanying drawing is described further, with
It is fully understood from the purpose of the present invention, feature and effect.
Brief description
Fig. 1 is the signed digraph of the te simulating experimental system of a preferred embodiment of the present invention；
Fig. 2 is the Troubleshooting Flowchart of a preferred embodiment of the present invention；
Fig. 3 is the test data square prediction error figure of the te procedure fault 1 of a preferred embodiment of the present invention；
Fig. 4 is Fault Isolation index η of a preferred embodiment of the present invention^{2}Block diagram.
Specific embodiment
A kind of method for diagnosing faults based on the reconstruct of signed digraph data, is instructed offline by gathering normal data
Practice, as known state measurement data, by pca, pretreated data is decomposed, and then obtain the control of spe
Limit.Then according to system flow, set up sdg model, after setting rate of failing to report, rate of false alarm, detecting sideplay amount, determine each variable v
The parameter of mask.Then the process data of realtime acquisition system unknown state, as unknown state measurement data, to each variable
Cusum and sample spe is monitored, if spe has exceeded control limit, expression system be there occurs fault, united by cusum afterwards
Metering determines effective node, by searching for all possible compatible path, the data on all compatible path directions is carried out with weight
Structure, the maximum direction of its Fault Isolation index, is real fault propagation direction, start node in this direction is considered as
The reason fault variable, and lead to the abnormal event of this node to be considered as the basic reason producing fault.
Fig. 1 is the signed digraph of fault diagnosis field typical emulation platform te process, and the node of in figure is a list
Variable, the parameter of certain part in expression system, solid line represents active influence, and that is, start node increases, the section that solid arrow points to
Point also increases, and dotted line is just contrary, represents node in figure circle,
Method for diagnosing faults flow process based on the reconstruct of signed digraph data is as shown in Fig. 2 specifically comprise the following steps that
Offline modeling process:
1st, data acquisition.Acquisition system process a number of multivariate normal number under system normal operating condition
According to.
2nd, data prediction, by data center's nondimensionalization.As known state measurement data.Changeable collect
Amount normal data lines up matrix x_{n×m}, wherein n is the number of collecting sample, and m is variable number, for x_{n×m}In each arrange to
Amount x_{i}(i=1 ... m), by formulaObtain the newly vectorial y that average is that 0 variance is 1_{i}, whereinAnd s_{i}It is respectively
x_{i}Average and variance, such matrix x_{n×m}It is changed into matrix y after pretreatment_{n×m}.
3rd, pretreated data is carried out with pca decomposition, and calculates the spe of fault detect controlling limit.First obtain association side
Difference matrix y^{t}The eigenvalue of y and individual features vector λ_{i}And p_{i}(i=1 ... m), pivot number k being taken is according to eigenvalue summation
96% is set, and the corresponding characteristic vector of the big eigenvalue of front k constitutes principal component space, and other mk eigenvalue is corresponding
Characteristic vector constitutes residual error space.Thus sample vector y (m dimensional vector) can be decomposed intoWherein pivot
PartRepresent sample vector y in principal component space s_{p}Projection, that is,Residual error portionRepresent sample vector y
In residual error space s_{r}Projection, that is,Wherein i is unit matrix.The square prediction error formula of sample y isControl is limited toWherein (1 α) × 100%
For confidence level, and have:
Hereλ_{i}Ith feature value for covariance matrix.
4th, set up system symbol Directed Graph Model, set the whether abnormal parameters of detection node variable.According to system
Procedure graph, draws the signed digraph of system, as shown in Figure 1.In order to during diagnosis, whether decision node is effective, here
Whether normal range is exceeded come the cusum statistic of detection node variable by vmask.Vmask is to separate in cusum table
Go out the effective tool of abnormal data, its shape is in opening v font to the left, and its summit and nearest sampled point are in same level
On line, both distances are d, and underarm is the slope line that slope is k.Data in v word all represents normal data, otherwise for extremely
Data.The major parameter of vmask can determine according to formula below:
H=d*k
Wherein k is the slope of v, and d is nearest sampled point with a distance from v fixed point, and the sampled point that h is nearest is upper and lower from v
The distance of arm.α is rate of false alarm；β is rate of failing to report；δ is the sideplay amount (multiple of sample standard deviation) that can detect；σ_{x}For sample
Standard deviation.
Inline diagnosis process:
5th, realtime acquisition system process data, as unknown state measurement data.
6th, calculate the square prediction error spe of real time data, and it is controlled limit to be compared with spe, without super
Cross control limit, return the next process data of 5 collections.
7 if it exceeds spe controls limit, and the accumulation counting each variable sentenced with cusum statistic and by the both arms of vmask
Whether the node variable of disconnected sdg is effective node variable, for the variable more than underarm on v, node symbol be respectively "+" and
“”.The computing formula of accumulation and cusum statistic is:Wherein m is number of samples,For institute's test sample
This average,The average of ith variable that step 1 calculates.
8th, search for all possible compatible path in sdg, that is, adjacent node symbol is multiplied for positive path in this path
On, if the symbol of sdg be "+", correspondence direction vector analog value be 1, if the symbol of sdg be "", correspondence direction
The analog value of vector is 1, otherwise for 0.For example, the respective nodes detecting the 1st variable and the 3rd variable be "+" and "",
Other nodes are all 0 and the 1st variable and the 3rd variable on compatible path, then the direction vector on this path corresponds to position
The value put is exactly corresponding node state 1, 1 or 0, unitization after obtain the direction vector in this path and be[1,0,
1,0…0].
9th, in all effective node direction and compatible path direction, unknown state measurement data is surveyed and be reconstructed.Reconstruct
Purpose be exactly will measure sample along reconstruct direction move, until from principal component space near with a distance from, if reconstruct direction be exactly
The direction of propagation of fault, then the square prediction error of the unknown state measurement data after mobile should obtain the reduction of maximum.
Shown in the reduction degree such as formula (1) of square prediction error:
Its span is wherein y between 0～1^{spe}For reconstruct before sample square prediction error,For reconstruct after not
Know the square prediction error of state measurement data,For the projection in residual error space for the ζ, that is,In formula (1)The η making^{2}Maximum direction is just the propagation path of fault, and the start node on propagation path is then fault
The basic reason occurring.
Te is an emulation platform, and it simulates 20 faults, and Fig. 3 is te process (when a certain fault occurs) test data
Square prediction error figure, in figure dotted line is to control limit.
Fig. 4 is Fault Isolation index η after reconstruct on all effective nodes and compatible path^{2}Block diagram, abscissa
Represent the numbering in effective node and compatible path, wherein numbering 1～12 is effective node, and numbering 13～15 is compatible road
Footpath, is respectively as follows:
As can be known from Fig. 4, the Fault Isolation index on this compatible path of numbering 15 is maximum, and therefore this path is fault
Propagation path, i.e. xc change leads to greatly reactor pressure p7 to increase, thus leading to separator pressure p13 and desorbing pressure tower p16 to increase
Greatly, due to only faulty 1 can make that xc component becomes big and xb component is constant in 20 faults of te process, can thus be concluded that it is fault
1 there occurs.
The preferred embodiment of the present invention described in detail above.It should be appreciated that those of ordinary skill in the art is no
Need creative work just can make many modifications and variations according to the design of the present invention.Therefore, all technology in the art
It is available that personnel pass through logical analysis, reasoning, or a limited experiment under this invention's idea on the basis of existing technology
Technical scheme, all should be in the protection domain being defined in the patent claims.
Claims (6)
1. a kind of method for diagnosing faults based on the reconstruct of signed digraph data is it is characterised in that comprise the steps:
Step one: a number of multivariate normal data in acquisition system running, and to described multivariate normal data
Carry out pretreatment, as known state measurement data；
Step 2: pca decomposition is carried out to known state measurement data described in step one, described known state measurement data is divided
Solve as pivot part and residual error portion, and obtain the control limit of the square prediction error of described known state measurement data；
Step 3: the architectural characteristic according to described system and response characteristic, set up the oriented graphical diagram (sdg) of described system, institute
The node stating oriented graphical diagram is system univariate parameter, and sets rate of false alarm, rate of failing to report parameter, and the sdg according to system determines
The parameter value of each variable vmask of described known state measurement data；
Step 4: a number of multivariable process data in realtime acquisition system running, and to described multivariable process
Data carries out pretreatment, as unknown state measurement data, counts described unknown state measurement data accumulation and cusum statistical value
And the square prediction error spe of described unknown state measurement data, if spe exceeds the control limit of step 2 then it represents that system
Break down；
Step 5: if system does not break down, repeat step four, if system malfunctions, sentenced by the both arms of vmask
Effectively whether, for the described unknown state measurement data more than underarm on vmask, node symbol divides the node of disconnected described sdg
Not Wei "+" and "" number；
Step 6: after determining all node states, search for all of compatible path in described sdg, that is, adjacent node symbol is multiplied
For positive path；
Step 7: weight is carried out to described unknown state measurement data on all effective node direction and described compatible path direction
Structure, direction maximum for reconstruct index is just set to the propagation path of fault, judges the initial section on the propagation path of described fault
The basic reason that point occurs for causing trouble.
2. the method for diagnosing faults based on the reconstruct of signed digraph data as claimed in claim 1 is it is characterised in that described
Described multivariate normal data pretreatment step in step one is: first will be normal for the described a number of multivariate of collection
Data deducts the average of described multivariate normal data, then divided by the variance of described multivariate normal data.
3. the method for diagnosing faults based on the reconstruct of signed digraph data as claimed in claim 1 is it is characterised in that described
In step 2, pca method chooses pivot it is desirable to contribution rate is more than 85% according to eigenvalue contribution rate.
4. the method for diagnosing faults based on the reconstruct of signed digraph data as claimed in claim 1 is it is characterised in that described
In step 2, square prediction error control limit computing formula isWhereinFor h^{spe}Card side
Distribution, (1 α) × 100% is the described confidence level controlling limit,Hereλ_{i}For described pretreated described multivariate normal data sample covariance matrix
Ith big eigenvalue, k is the pivot number of pca, and m is variable number.
5. the method for diagnosing faults based on the reconstruct of signed digraph data as claimed in claim 1 is it is characterised in that described
The parameter value of vmask described in step 3 is:And h=d*k, wherein k is the oblique of v
Rate, d is nearest sampled point with a distance from v fixed point, h be nearest sampled point with a distance from the upper underarm of v, α is rate of false alarm, β
For rate of failing to report, δ is the sideplay amount that can detect, and described sideplay amount is the multiple of sample standard deviation, σ_{x}Standard deviation for sample.
6. the method for diagnosing faults based on the reconstruct of signed digraph data as claimed in claim 1 is it is characterised in that described
In the step 7 if maximum direction of reconstruct index is exactly the propagation path of fault, then the unknown state measurement data after reconstruct
Square prediction error should obtain maximum reduction, the computing formula of the reduction degree of square prediction error is:
Its span is wherein y between 0～1^{spe}For reconstruct before sample square prediction error,For reconstruct after unknown
The square prediction error of state measurement data, f then represents fault size, and ζ represents the direction vector in compatible path,For ζ residual
The projection of difference space, that is,C is the principal component space projection matrix of measurement,Make η^{2}Maximum side
To the propagation path being just fault, the basic reason that the start node on the propagation path of fault then occurs for fault.
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