CN101738998A  System and method for monitoring industrial process based on local discriminatory analysis  Google Patents
System and method for monitoring industrial process based on local discriminatory analysis Download PDFInfo
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 CN101738998A CN101738998A CN200910155009A CN200910155009A CN101738998A CN 101738998 A CN101738998 A CN 101738998A CN 200910155009 A CN200910155009 A CN 200910155009A CN 200910155009 A CN200910155009 A CN 200910155009A CN 101738998 A CN101738998 A CN 101738998A
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Classifications

 Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSSSECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSSREFERENCE 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 system and a method for monitoring an industrial process based on local discriminatory analysis. The monitoring system comprises a measuring instrument, a distributed control system, a server and an upper computer, wherein the server comprises a realtime database and a relation database; the upper computer comprises an offline modeling module and an online modeling module. The offline modeling module comprises the following modules: a) a training data extracting module, b) a first standardization module, and c) a model training module. The online monitoring module comprises the following modules: a) a signal acquisition module, b) a second standardization module, c) a fault detection and diagnosis module, and d) a result display module. The invention realizes the detection and the diagnosis of a fault at the same time, and has an obviously better effect than that of the system and the method for monitoring the industrial process based on the local discriminatory analysis of the conventional system.
Description
Technical field
The present invention relates to the Industrial Process Monitoring field, relate in particular to a kind of Industrial Process Monitoring system and method based on local discriminatory analysis.
Technical background
The normal even running of keeping complex industrial process is the common objective that each industrial trade such as oil, chemical industry, pharmacy, food is pursued.Its meaning not only is to guarantee the safe and reliable operation produced, also is to realize strict production assurance and controls environment to pollute etc.Effectively the realtime process monitoring system is the key that guarantees large complicated industrial process even running.
The widespread use in industrial process along with Distributed Control System (DCS) and various smart instrumentation, computer resource with low cost and reliable popularizing of Storage Techniques, process and qualitative datas a large amount of in the modern industry process are measured in real time and are noted.These data have accurately been described status of processes, for process monitoring provides reliable foundation.Use various data analysing methods that the representative process historical data of gathering under normal operating condition and under the fault condition is carried out modeling respectively based on the method for datadriven, analyze the process data realization monitoring of online measurement then according to institute's established model.These class methods only depend on measurement data, are specially adapted to be difficult to obtain accurately and the monitoring of the largescale complex process of complete mechanism model.Process monitoring comprises the detection and the diagnosis of fault.The former judges whether current system exists fault, and the latter judges which classification is the fault taken place belong to, for the reparation of fault provides foundation.Existing about based on the patent of the process monitoring system of these class methods as:
Chinese invention patent 200610154826.9 discloses a kind of industrial process nonlinear fault diagnosis system based on FISHER, comprise the field intelligent instrument, DCS system and the host computer that are connected with industrial process object, described DCS system is made of datainterface, control station, database; Intelligence instrument, DCS system, host computer link to each other successively, and described host computer comprises standardization module, FISHER discriminatory analysis module and fault diagnosis module.Can access good fault diagnosis effect.
Chinese invention patent 200610154825.4 discloses a kind of industrial processes fault detection method based on wavelet analysis, comprise the field intelligent instrument, DCS system and the host computer that are connected with industrial process object, described DCS system is made of datainterface, control station, database; Intelligence instrument, DCS system, host computer link to each other successively, and described host computer comprises standardization module, wavelet decomposition module, pivot analysis functional module, wavelet reconstruction functional module, support vector machine classifier functional module and fault judgement module.Can access good diagnosis effect.
But there are two subject matters in current process monitoring method based on datadriven:
A) fault and diagnosis as two tasks independently.Promptly carry out fault detect earlier, after decision process is in malfunction, the classification of failure judgement again.When fault detect, only utilize gathered data under the process normal condition like this, do not utilized fault data.Therefore the effect of fault detect also has the space of further improving.
B) fault diagnosis uses the Fisher discriminatory analysis that fault data is carried out modeling.The Fisher discriminatory analysis only has only two classes in the data of analyzing, and every class data all meet under the situation of Gaussian distribution of identical covariance matrix for optimum.And the classification of procedure fault and corresponding fault data is often more than two classes, and also Gaussian distributed not necessarily of every class data.
Therefore existing method for diagnosing faults can not provide optimum accuracy rate of diagnosis under many circumstances.
Summary of the invention
In order to improve the deficiency that limitation is big, effect is general that existing process monitoring system uses, the invention provides a kind of applied widely, realize the detection and the diagnosis of fault simultaneously, and effect obviously is better than the Industrial Process Monitoring system based on local discriminatory analysis of existing system.
A kind of Industrial Process Monitoring system based on local discriminatory analysis, the host computer that comprises the measuring instrument, the Distributed Control System (DCS) that are connected with industrial process object, is used for storing the server of Distributed Control System (DCS) institute image data and is used for the image data that processing server stores.
Wherein measuring instrument is used to gather the real time data of industrial process object, and Distributed Control System (DCS) (DCS) is controlled industrial process object according to the real time data of measuring instrument collection.
Described server comprises the realtime data base of the real time data that is used for storage industry process object and is used to store the relational database of the data of described industrial process object under normal condition and all kinds of malfunction; Data in the described relational database, can be described as historical data, industrial process object is in normal condition or is in malfunction in the historical data, and the type of malfunction all is clear and definite, can think that each data point all has corresponding class sign.
Described host computer comprises offline modeling module and online monitoring module;
Wherein said offline modeling module comprises:
A) training data extraction module is used for extracting the class sign of data under normal condition and all kinds of malfunction and each data point correspondence as training data from relational database;
Before extracting, can preestablish the data variable that needs extraction, initial sum termination time, the sample number of sampling interval and each data class.Extraction obtains data set
R
^{D}The real number space of expression D dimension and corresponding class sign l
_{1}... l
_{n}∈ 0,1,2...c}.The data type of available 0 mark normal condition, 1, the data type under the different fault of 2...c mark.
Each data point that expression is extracted from relational database;
B) first standardized module is used for training data is done standardization, obtains the training data x after the standardization
_{i}(i=1 ... n, n are that training data is counted); The step of standardization is as follows:
1) computation of mean values:
Wherein
Be the data point of extracting from relational database, n is the data point number, and i is the data point call number;
2) calculate variance vectors:
3) translation is flexible:
Wherein ./be that vectorial corresponding element is divided by σ
_{x}Be the standard deviation vector,
The average that obtains for step 1).
The training data that extracts is done standardization, and can make each variable (data point is made up of the value of a plurality of variablees) average is zero, and variance is 1.
C) model training module is used for the training data x after the standardization
_{i}Carry out the partial structurtes modeling, find the solution again and obtain optimum projection matrix A, with this optimum projection matrix training data is projected to the subspace and obtain the picture y of training data in the subspace
_{i}(i=1,2...n are natural number);
Described online monitoring module comprises:
A) signal acquisition module; Be used for gathering the real time data of industrial process object from realtime data base;
B) second standardized module; Be used to utilize the average of training data of first standardized module and variance to collecting to such an extent that real time data does that translation is flexible to be handled, obtain the real time data x of translation after flexible; Still use formula when translation is flexible
But wherein
Replace with real time data, and σ
_{x}With
The still average and the variance of the training data that obtains with first standardized module.
C) fault detection and diagnosis module; Real time data x after flexible projects in the subspace the optimum projection matrix A that obtains with the model training module with translation, obtains the picture y of real time data in the subspace, (y=A
^{T}X), use the nearest neighbor search that defines based on Euclidean distance to seek the picture y of training data in the subspace
_{i}(i=1 ... n) from picture y nearest some y
_{p}, according to y
_{p}Status categories judge the status categories of real time data;
D) display module as a result; Be used to show the status categories of fault detection and diagnosis module judgement.
Wherein said model training module comprises:
The partial structurtes MBM; With the training data x of dispersion matrix between local within class scatter matrix drawn game category after to standardization
_{i}Local geometry and local differentiate structure modeling respectively;
Module is found the solution in the optimum decision projection; Be used for local within class scatter matrix R according to training data
_{w}, dispersion matrix R between local class
_{b}With local population variance degree matrix R
_{t}Try to achieve between the local class of data after the projection maximum and optimum projection A dispersion minimum in the local class of dispersion;
The training data projection module; With optimum projection matrix A with training data project to obtain in the subspace training data in the subspace the picture y
_{i}
Described partial structurtes MBM is with the training data x of dispersion matrix between local within class scatter matrix drawn game category after to standardization
_{i}Local geometry and local differentiate structure modeling respectively, step is as follows:
1) each training data point x under the definition of calculating Euclidean distance
_{i}Karest neighbors set of data points
With M (x
_{i}) separated into two parts M
_{w}(x
_{i}) and M
_{b}(x
_{i}), M wherein
_{w}(x
_{i}) be and x
_{i}Belong to of a sort point, M
_{b}(x
_{i}) be and x
_{i}Belong to inhomogeneous point.
The class here is meant x
_{i}Be normal condition or certain malfunction, can identify by class and discern.K is the size of regional area, promptly with the number of each training data point arest neighbors data point.
2) calculate adjacency matrix W in the local class
_{w}Adjacency matrix W between the drawn game category
_{b}
W in the formula
_{w}And W
_{b}Subscript i, the j respectively line number of representing matrix and the index of columns.
3) calculate local within class scatter matrix R
_{w}, dispersion matrix R between local class
_{b}With local population variance degree matrix R
_{t}
R
_{t}＝R
_{w}+R
_{b}。
Wherein subscript T represents transposed matrix; N represents training data point number.
Module is found the solution in described optimum decision projection, according to the local within class scatter matrix R of training data
_{w}, dispersion matrix R between local class
_{b}With local population variance degree matrix R
_{t}Ask between the local class of the data that make after the projection maximum and optimum projection matrix A dispersion minimum in the local class of dispersion, step is as follows:
1) to R
_{t}Implement characteristic value decomposition R
_{t}=Q Λ Q
^{T}, Q=[q wherein
_{1}... q
_{m}] form by nonzero eigenvalue characteristic of correspondence vector; Wherein subscript T represents that transposed matrix, Λ represent by R
_{t}The diagonal matrix formed of nonzero eigenvalue;
2) compute matrix Λ
^{1}Q
^{T}R
_{b}D proper vector b of Q eigenwert maximum
_{1}... b
_{d}, be worth descending arrangement by characteristic of correspondence, the dimensionality reduction subspace dimension of d for setting;
3) calculate optimum projection matrix A=QB ∈ R
^{D * d}, B=[b wherein
_{1}... b
_{d}];
R
^{D * d}Be the real number matrix of the capable d row of D, D is the dimension of training data.
Described training data projection module with optimum projection matrix with training data x
_{i}(i=1 ... n) project to obtain in the subspace training data in the subspace the picture y
_{i}, y
_{i}=A
^{T}x
_{i}∈ R
^{d}(i=1 ... n).
The present invention also provides a kind of Industrial Process Monitoring method based on local discriminatory analysis, comprises the steps:
1) data and the corresponding class of extracting under industrial process object normal condition and all kinds of malfunction identifies as training data; Training data is done standardization, obtain the training data data x after the standardization
_{i}(i=1 ... n); To the training data x after the standardization
_{i}Carry out the partial structurtes modeling, find the solution and obtain optimum projection matrix, with this optimum projection matrix training data is projected to the subspace and obtain the picture data y of training data in the subspace
_{i}(i=1 ... n);
2) gather the industrial process object real time data; Utilize the average of training data in the step (1) and variance to collecting to such an extent that real time data does that translation is flexible to be handled, obtain the real time data y of translation after flexible
_{i}(i=1 ... n); Utilize the real time data x after the optimum projection matrix that obtains in the step 1) stretches translation to project in the subspace, obtain the picture y of real time data in the subspace, y=A
^{T}X uses based on the nearest neighbor search of Euclidean distance definition and seeks the picture y of training data in the subspace
_{i}(i=1 ... n) from picture y nearest some y
_{p}, according to y
_{p}Status categories judge the status categories of real time data;
3) status categories to real time data shows.
Step (1) is described, and that training data is made the step of standardization is as follows:
1) computation of mean values:
Wherein
Be the normal condition of industrial process object or the data point under certain class malfunction, n is the number of data point;
2) calculate variance vectors:
3) translation is flexible:
Wherein ./be that vectorial corresponding element is divided by σ
_{x}Be the standard deviation vector,
The average that obtains for step 1).
In the step (1) to the training data x after the standardization
_{i}Carry out the partial structurtes modeling, find the solution and obtain optimum projection matrix, with this optimum projection matrix training data is projected to the subspace and obtain the picture y of training data in the subspace
_{i}Step comprise:
Calculate the karest neighbors set of data points of each the training data point under the Euclidean distance definition
With M (x
_{i}) separated into two parts M
_{w}(x
_{i}) and M
_{b}(x
_{i}), M wherein
_{w}(x
_{i}) be and x
_{i}Belong to of a sort point, M
_{b}(x
_{i}) be and x
_{i}Belong to inhomogeneous point; K is the size of regional area;
Calculate adjacency matrix W in the local class
_{w}Adjacency matrix W between the drawn game category
_{b}
Calculate local within class scatter matrix R
_{w}, dispersion matrix R between local class
_{b}With local population variance degree matrix R
_{t}
R
_{t}＝R
_{w}+R
_{b}。
To R
_{t}Implement characteristic value decomposition R
_{t}=Q Λ Q
^{T}, Q=[q wherein
_{1}... q
_{m}] form by nonzero eigenvalue characteristic of correspondence vector; Compute matrix Λ
^{1}Q
^{T}R
_{b}D proper vector b of Q eigenwert maximum
_{1}... b
_{d}, be worth descending arrangement by characteristic of correspondence;
Calculate optimum projection matrix A=QB ∈ R
^{D * d}, B=[b wherein
_{1}... b
_{d}];
With optimum projection matrix with training data project to obtain in the subspace training data in the subspace the picture y
_{i}, y
_{i}=A
^{T}x
_{i}∈ R
^{d}(i=1 ... n).
Between the local class that the present invention adopts in the dispersion maximization drawn game category dispersion minimize criterion can make data after through the judgement projection on local granularity the inhomogeneity data separate as far as possible, homogeneous data is polymerization as far as possible, thereby makes the overlapping minimum between inhomogeneity.Cooperate local classifiers (the present invention has adopted the arest neighbors classification) can reach best classification accuracy to implementing classification through the data after the judgement projection.
Beneficial effect of the present invention mainly shows: realize judging the whether specific category of fault and failure judgement of system simultaneously 1..2. the constraint that not distributed by failure classes number and class, applied widely.3. judging nicety rate is higher than existing system.
Description of drawings
Fig. 1 is the structured flowchart of process monitoring of the present invention system;
Fig. 2 is the block diagram that has the host computer concrete structure in the process monitoring of the present invention system.
Embodiment
Below in conjunction with accompanying drawing the present invention is further described.
With reference to Fig. 1, Fig. 2, the present invention is based on the Industrial Process Monitoring system of local discriminatory analysis, comprise the insitus tester table 2 that is used to gather industrial process object 1 status data, be used for Distributed Control System (DCS) (DCS), server 4 and host computer 7 that industrial process object is controlled, be useful in the server to the host computer transmission and detect data in real time database 5 and the relational database 6 that is used for storing history data in real time, host computer 7 comprises offline modeling module 8 and online monitoring module 15.
Offline modeling module 8 comprises: training data extraction module 9, first standardized module 10 (being shown as standardized module among the figure) and model training module 11.
And comprise in the model training module 11 that partial structurtes MBM 12, optimum decision projection find the solution module 13 and training data projection module 14.
Online monitoring module 15 comprises signal acquisition module 16, second standardized module 17 (being shown as standardized module among the figure), fault detection and diagnosis module 18 and display module 19 as a result.
Workflow below in conjunction with each module declaration Industrial Process Monitoring of the present invention system.，
At first the training data extraction module 9, are used for extracting the historical data of industrial process object 1 under data under the normal condition and all kinds of malfunction from the relational database 6 that saves historical data and are used for model training.Set the data variable that needs extraction before extracting, initial sum termination time, the sample number of sampling interval and each data class.Obtain data set
And corresponding class sign l
_{1}... l
_{n}∈ 0,1,2...c}.With the normal class of class 0 mark, 1,2...c mark failure classes.The data of extracting are reached first standardized module 10.
First standardized module 10, the training data that imports into is done standardization, and making each variable average is zero, and variance is 1, and the data after the standardization are reached model training module 11, it is standby that the average of training data and each variable variance reach second standardized module 17.Wherein standardized step is as follows in first standardized module 10:
1) computation of mean values,
2) calculate variance vectors, each element is the variance of each variable.
3) translation is flexible, calculates
Wherein " ./" be that vectorial corresponding element is divided by σ
_{x}Be the standard deviation vector.
Training data after the standardization that local MBM 12 receptions first standardized module 10 imports into, local geometry and the modeling respectively of local differentiation structure with dispersion logm certificate between local within class scatter matrix drawn game category, and dispersion matrix, population variance degree matrix reach the judgement projection and find the solution module 13 between the local within class scatter matrix that will calculate, local class, and training data is reached fault detection and diagnosis module 18 in training data projection module 14 and the online monitoring module 15.
It is as follows to calculate between local within class scatter matrix, local class the step of dispersion matrix and population variance degree matrix:
1) the karest neighbors set of data points of each the training data point under the definition of calculating Euclidean distance
With M (x
_{i}) separated into two parts M
_{w}(x
_{i}) and M
_{b}(x
_{i}), M wherein
_{w}(x
_{i}) be and x
_{i}Belong to of a sort point, M
_{b}(x
_{i}) be and x
_{i}Belong to inhomogeneous point.
2) calculate adjacency matrix W in the local class
_{w}Adjacency matrix W between the drawn game category
_{b}
3) calculate local within class scatter matrix R
_{w}, dispersion matrix R between local class
_{b}With local population variance degree matrix R
_{t}
R
_{t}＝R
_{w}+R
_{b}。
The dimension d after module 13 is set projection is found the solution in the optimum decision projection, according to the local within class scatter matrix R of the training data that imports into
_{w}, dispersion matrix R between local class
_{b}With local population variance degree matrix R
_{t}Ask between the local class of the data that make after the projection maximum and optimum decision projection A dispersion minimum in the local class of dispersion, and reach training data projection module 14.Step is as follows:
1) to R
_{t}Implement characteristic value decomposition R
_{t}=Q Λ Q
^{T}, Q=[q wherein
_{1}... q
_{m}] form by nonzero eigenvalue characteristic of correspondence vector.
2) compute matrix Λ
^{1}Q
^{T}R
_{b}The d of Q eigenwert maximum vectorial b
_{1}... b
_{d}, big to minispread by characteristic of correspondence value size.
3) calculate optimum projection matrix A=QB ∈ R
^{D * d}, B=[b wherein
_{1}... b
_{d}].
Training data projection module 14, the training data that will import into according to the optimum projection matrix A that imports into projects to and obtains y in the subspace
_{i}=A
^{T}x
_{i}∈ R
^{d}(i=1 ... n), and reach fault detection and diagnosis module 18 in the online monitoring module 15.
Signal acquisition module 16 is set the time interval of each data sampling, gathers real time data and imports second standardized module 17 into; The data point that training data average that first standardized module 10 imports in second standardized module, the 17 use offline modeling modules and variance are imported into each signal acquisition module 16 is implemented the flexible processing of translation, and the result is reached fault detection and diagnosis module 18.The data point that standardisation process imported into any time
Calculate
Wherein " ./" be that vectorial corresponding element is divided by σ
_{x}Be the standard deviation vector.
The optimum projection matrix that fault detection and diagnosis module 18 usefulness model training modules 14 are imported into projects in the subspace y=A with the data x that second standardized module 17 imports into
^{T}X; Use is sought y based on the nearest neighbor search of Euclidean distance definition
_{1}... y
_{n}In from the nearest some y of y
_{p}, judge that the status categories of active procedure is y
_{p}Status categories l
_{p}And the result reached display module 19 as a result.
The process status classification imported into according to fault detection and diagnosis module 18 of display module 19 shows the state of active procedure on manmachine interface as a result, and display result is that current system is in normal condition or certain malfunction.
Claims (9)
1. Industrial Process Monitoring system based on local discriminatory analysis, the host computer that comprises the measuring instrument, the Distributed Control System (DCS) that are connected with industrial process object, is used for storing the server of Distributed Control System (DCS) institute image data and is used for the image data that processing server stores is characterized in that:
Described server comprises the realtime data base of the real time data that is used for storage industry process object and is used to store the relational database of the data of described industrial process object under normal condition and all kinds of malfunction;
Described host computer comprises offline modeling module and online monitoring module;
Wherein said offline modeling module comprises:
A) training data extraction module is used for extracting the class sign of data under normal condition and all kinds of malfunction and each data point correspondence as training data from relational database;
B) first standardized module is used for training data is done standardization, obtains the training data x after the standardization
_{i}
C) model training module is used for the training data x after the standardization
_{i}Carry out the partial structurtes modeling, find the solution again and obtain optimum projection matrix A, with this optimum projection matrix training data is projected to the subspace and obtain the picture y of training data in the subspace
_{i}
Described online monitoring module comprises:
A) signal acquisition module; Be used for gathering real time data from realtime data base;
B) second standardized module; Be used to utilize the average of training data of first standardized module and variance to collecting to such an extent that real time data does that translation is flexible to be handled, obtain the real time data of translation after flexible;
C) fault detection and diagnosis module; Real time data after flexible projects in the subspace the optimum projection matrix A that obtains with the model training module with translation, obtains the picture y of real time data in the subspace, uses based on the nearest neighbor search of Euclidean distance definition and seeks the picture y of training data in the subspace
_{i}In from picture y nearest some y
_{p}, according to y
_{p}Status categories judge the status categories of real time data;
D) display module as a result; Be used to show the status categories of fault detection and diagnosis module judgement.
2. method for diagnosing faults as claimed in claim 1 is characterized in that: the step that described first standardized module is done standardization to training data is as follows:
1) computation of mean values:
Wherein
Be the data point of extracting from relational database, n is the data point number, and i is the data point call number;
2) calculate variance vectors:
${\mathrm{\σ}}_{x}^{2}=\frac{1}{n1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\stackrel{~}{x}}_{i}\stackrel{\‾}{x});$
3) translation is flexible:
Wherein ./be that vectorial corresponding element is divided by σ
_{x}Be the standard deviation vector,
The average that obtains for step 1).
3. method for diagnosing faults as claimed in claim 1 is characterized in that: described model training module comprises:
The partial structurtes MBM; With the training data x of dispersion matrix between local within class scatter matrix drawn game category after to standardization
_{i}Local geometry and local differentiate structure modeling respectively;
Module is found the solution in the optimum decision projection; Be used for local within class scatter matrix R according to training data
_{w}, dispersion matrix R between local class
_{b}With local population variance degree matrix R
_{t}Try to achieve between the local class of data after the projection maximum and optimum projection A dispersion minimum in the local class of dispersion;
The training data projection module; With optimum projection matrix A with training data project to obtain in the subspace training data in the subspace the picture y
_{i}
4. method for diagnosing faults as claimed in claim 3 is characterized in that: described partial structurtes MBM is with the training data x of dispersion matrix between local within class scatter matrix drawn game category after to standardization
_{i}Local geometry and local differentiate structure modeling respectively, step is as follows:
1) each training data point x under the definition of calculating Euclidean distance
_{i}Karest neighbors set of data points
With M (x
_{i}) separated into two parts M
_{w}(x
_{i}) and M
_{b}(x
_{i}), M wherein
_{w}(x
_{i}) be and X
_{i}Belong to of a sort point, M
_{b}(x
_{i}) be and X
_{i}Belong to inhomogeneous point; K is the size of regional area;
2) calculate adjacency matrix W in the local class
_{w}Adjacency matrix W between the drawn game category
_{b}
3) calculate local within class scatter matrix R
_{w}, dispersion matrix R between local class
_{b}With local population variance degree matrix R
_{t}
R
_{t}＝R
_{w}+R
_{b}。
5. method for diagnosing faults as claimed in claim 4 is characterized in that: module is found the solution in described optimum decision projection, according to the local within class scatter matrix R of training data
_{w}, dispersion matrix R between local class
_{b}With local population variance degree matrix R
_{t}Ask between the local class of the data that make after the projection maximum and optimum projection matrix A dispersion minimum in the local class of dispersion, step is as follows:
1) to R
_{t}Implement characteristic value decomposition R
_{t}=Q Λ Q
^{T}, Q=[q wherein
_{1}... q
_{m}] form by nonzero eigenvalue characteristic of correspondence vector, m is the nonzero eigenvalue number;
2) compute matrix Λ
^{1}Q
^{T}R
_{b}D proper vector b of Q eigenwert maximum
_{1}... b
_{d}, be worth descending arrangement by characteristic of correspondence, the dimensionality reduction subspace dimension of d for setting;
3) calculate optimum projection matrix A=QB ∈ R
^{D * d}, B=[b wherein
_{1}... b
_{d}], D is the training data dimension;
6. method for diagnosing faults as claimed in claim 5 is characterized in that: described training data projection module projects to training data with optimum projection matrix and obtains the picture y of training data in the subspace in the subspace
_{i}, y
_{i}=A
^{T}x
_{i}∈ R
^{d}(i=1 ... n).
7. the Industrial Process Monitoring method based on local discriminatory analysis is characterized in that, comprises the steps:
1) the class sign of the data point under extraction industrial process object normal condition and all kinds of malfunction and each data point correspondence is as training data; Training data is done standardization, obtain the training data x after the standardization
_{i}To the training data x after the standardization
_{i}Carry out the partial structurtes modeling, find the solution and obtain optimum projection matrix, with this optimum projection matrix training data is projected to the subspace and obtain the picture y of training data in the subspace
_{i}
2) gather the industrial process object real time data; Utilize the average of training data in the step (1) and variance to collecting to such an extent that real time data does that translation is flexible to be handled, obtain the real time data of translation after flexible; The real time data after flexible projects in the subspace with translation to utilize the optimum projection matrix that obtains in the step 1), obtains the picture y of real time data in the subspace, uses based on the nearest neighbor search of Euclidean distance definition and seeks the picture y of training data in the subspace
_{i}In from picture y nearest some y
_{p}, according to y
_{p}Status categories judge the status categories of real time data;
3) status categories to real time data shows.
8. method for diagnosing faults as claimed in claim 7 is characterized in that: step (1) is described, and that training data is made the step of standardization is as follows:
1) computation of mean values:
Wherein
Be the normal condition of industrial process object or the data point under certain class malfunction, n is the number of data point, and i is the data point call number;
2) calculate variance vectors:
${\mathrm{\σ}}_{x}^{2}=\frac{1}{n1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\stackrel{~}{x}}_{i}\stackrel{\‾}{x});$
3) translation is flexible:
Wherein ./be that vectorial corresponding element is divided by σ
_{x}Be the standard deviation vector,
The average that obtains for step 1).
9. method for diagnosing faults as claimed in claim 8 is characterized in that: in the step (1) to the training data x after the standardization
_{i}Carry out the partial structurtes modeling, find the solution and obtain optimum projection matrix, with this optimum projection matrix training data is projected to the subspace and obtain the picture y of training data in the subspace
_{i}Step comprise:
Calculate each the training data point x under the Euclidean distance definition
_{i}Karest neighbors set of data points
With M (x
_{i}) separated into two parts M
_{w}(x
_{i}) and M
_{b}(x
_{i}), M wherein
_{w}(x
_{i}) be and X
_{i}Belong to of a sort point, M
_{b}(x
_{i}) be and x
_{i}Belong to inhomogeneous point; K is the size of regional area;
Calculate adjacency matrix W in the local class
_{w}Adjacency matrix W between the drawn game category
_{b}
Calculate local within class scatter matrix R
_{w}, dispersion matrix R between local class
_{b}With local population variance degree matrix R
_{t}
R
_{t}＝R
_{w}+R
_{b}。
To R
_{t}Implement characteristic value decomposition R
_{t}=Q Λ Q
^{T}, Q=[q wherein
_{1}... q
_{m}] form by nonzero eigenvalue characteristic of correspondence vector, m is the nonzero eigenvalue number; Compute matrix Λ
^{1}Q
^{T}R
_{b}D proper vector b of Q eigenwert maximum
_{1}... b
_{d}, the dimensionality reduction subspace dimension of d for setting; Be worth descending arrangement by characteristic of correspondence;
Calculate optimum projection matrix A=QB ∈ R
^{D * d}, B=[b wherein
_{1}... b
_{d}], D is the training data dimension;
With optimum projection matrix with training data project to obtain in the subspace training data in the subspace the picture y
_{i}, y
_{i}=A
^{T}x
_{i}∈ R
^{d}(i=1 ... n).
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