CN107817784B - A kind of procedure failure testing method based on concurrent offset minimum binary - Google Patents
A kind of procedure failure testing method based on concurrent offset minimum binary Download PDFInfo
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Abstract
The present invention provides a kind of procedure failure testing method based on concurrent offset minimum binary, including the use of penicillin fermentation process Fault Model processing input variable data and output variable data based on concurrent offset minimum binary；Calculate Hotelling statistic of the input variable data in the principal component space of Hotelling statistic, the uncorrelated space of output in output correlation space completely and the SPE statistic of SPE statistic, output variable data in the Hotelling statistic and residual error space in the principal component space for inputting uncorrelated space in residual error space；Calculate combination statistic；Carry out procedure fault judgement.The method of the present invention extracts the output relevant information of input variable data using orthogonal offset minimum binary, reduce computational load number, output relevant information and output irrelevant information are thoroughly separated using complete orthogonal decomposition, accurately detect failure, clearly whether identification failure is related to output, to operate equipment preferably to reduce cost, improve yield and quality.
Description
Technical field
The invention belongs to fault detection technique fields, and in particular to a kind of procedure fault inspection based on concurrent offset minimum binary
Survey method.
Background technique
The fermentation process of penicillin is as shown in Figure 1.With the development of modern technologies, penicillin fermentation process increasingly tends to
Enlargement, serialization and automation, the structure and composition of equipment are sufficiently complex, and production scale is very huge, each production cycle it
Between connection also especially closely.On the one hand, this raising for facilitating the decline of cost, penicillin yield and quality；On the other hand,
Device fails and the loss caused by stopping work significantly increases.Therefore, in penicillin fermentation process, as far as possible early
Detect process exception variation and reduce wrong report be very necessary.
For penicillin fermentation process, the patent of invention of Patent No. ZL201410337732.X proposes a kind of penicillin
Fermentation process method for diagnosing faults, although this patent can be diagnosed to be the fault type of penicillin fermentation process.But it improves
Offset minimum binary when dividing directly related with output principal component space, the not substantial skill with traditional offset minimum binary
Art difference and progress are being improved which results in exporting relevant information and exporting incoherent information not separated completely
Offset minimum binary detection in: (1) export relevant information and output irrelevant information do not separated completely；(2) need compared with
High computational load number；(3) the concurrent offset minimum binary detection model obtained, which is relatively difficult to resolve, to be released.So export dependent failure and
The verification and measurement ratio for exporting uncorrelated failure can reduce or even will appear whether failure can not be identified related to output.Patent No.
In the patent of invention of ZL201410337732.X, failure 1 and failure 2 are to export relevant failure, can from attached drawing 2 and attached drawing 3
To find out: the Hotelling statistic of the performance variable principal component space in penicillin fermentation process and process variable residual error space
Hotelling statisticAnd SPE statistic detects failure between 200 to 300 sampled points, this says
There is the relevant information of some outputs in uncorrelated space in bright export.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of procedure fault detection based on concurrent offset minimum binary
Method.
The technical scheme of the present invention is realized as follows:
A kind of procedure failure testing method based on concurrent offset minimum binary, including
The online input variable data and output variable data for obtaining penicillin fermentation process；
Using based on concurrent offset minimum binary penicillin fermentation process Fault Model processing input variable data and
Output variable data: input variable data are expressed as to export the pivot of related space data sets, the uncorrelated space of output completely
Output variable data are expressed as inputting related sky by space data sets and the sum of the residual error space data sets for exporting uncorrelated space
Between the sum of data set, the principal component space data set in the uncorrelated space of input and the residual error space data sets in the uncorrelated space of input；
Input variable data are calculated in the master for exporting the Hotelling statistic in correlation space, the uncorrelated space of output completely
Hotelling statistic in first space, the SPE statistic in the residual error space in the uncorrelated space of output, calculate output variable data
SPE system in the Hotelling statistic in the principal component space for inputting uncorrelated space and the residual error space for inputting uncorrelated space
Metering；
Calculate Hotelling statistic and input of the output variable data in the principal component space for inputting uncorrelated space not phase
Close the combination statistic of the SPE statistic in the residual error space in space；
Current penicillin fermentation process breakdown judge:
Judge whether Hotelling statistic of the input variable data being calculated in output correlation space completely is higher than
Its corresponding control limit: being that then output dependent failure occurs in current penicillin fermentation process；Otherwise:
Judge Hotelling statistic and input variable of the input variable data in the principal component space for exporting uncorrelated space
It is corresponding whether data have at least one statistic to be higher than it in the SPE statistic in the residual error space for exporting uncorrelated space
Control limit: being, then current penicillin fermentation process occurs exporting the uncorrelated but relevant failure of input, otherwise:
Judge Hotelling statistic and output variable of the output variable data in the principal component space for inputting uncorrelated space
Whether the combination statistic of SPE statistic of the data in the residual error space for inputting uncorrelated space is higher than its corresponding control
Limit: being, then uncertain output dependent failure occurs in current penicillin fermentation process, otherwise continues online acquisition subsequent time
The input variable data and output variable data of penicillin fermentation process.
The input variable data be the concentration of substrate in penicillin fermentation process, the concentration of carbon dioxide, ventilation rate,
Agitator power, pH value and substrate feeding temperature, the output variable data are the dense of the penicillin in penicillin fermentation process
The temperature of degree and fermentation reactor.
Input variable is handled using the penicillin fermentation process Fault Model based on concurrent offset minimum binary described
Before data and output variable data, specification and standard are carried out to the input variable data and output variable data that obtain online
Change.
The specification and standardization specifically make the mean value 0 of input variable data and output variable data and side respectively
Difference is 1.
The method for building up of the penicillin fermentation process Fault Model based on concurrent offset minimum binary is as follows:
Acquire the offline history normal data set of input variable and the offline history normal data set of output variable；
The offline history normal data set of input variable is divided into the related spatial data of output using orthogonal offset minimum binary
Collection and output orthogonal intersection space data set；
Related space data sets will be exported using complete orthogonal decomposition and the offline history normal data set of output variable is distinguished
Complete orthogonal decomposition is carried out, the complete output correlation space data sets, complete of the offline history normal data set of input variable are obtained
The input correlation space data set for exporting uncorrelated space data sets and the offline history normal data set of output variable and input are not
Correlation space data set；
Uncorrelated space is exported by the output orthogonal intersection space data set of the offline history normal data set of input variable and completely
Data set merges into the uncorrelated space data sets of output；
Uncorrelated space data sets will be exported and be divided into the principal component space data set for exporting uncorrelated space and output not phase
Close the residual error space data sets in space；
The uncorrelated space data sets of input of the offline history normal data set of output variable are divided into the uncorrelated sky of input
Between principal component space data set and input the residual error space data sets in uncorrelated space；
Input variable data are expressed as to export the principal component space number of related space data sets, the uncorrelated space of output completely
According to collecting and the sum of residual error space data sets for exporting uncorrelated space, output variable data are expressed as input correlation space data
Collect, input the sum of the principal component space data set in uncorrelated space and the residual error space data sets in the uncorrelated space of input to get arriving
Penicillin fermentation process Fault Model based on concurrent offset minimum binary.
The offline history normal data set of input variable is divided into the related sky of output using orthogonal offset minimum binary described
Between data set and output orthogonal intersection space data set before, the offline history normal data set of input variable and output variable are gone through offline
History normal data set is standardized and is standardized.
It is described to export related space data sets and the offline history normal data set of output variable using complete orthogonal decomposition
Complete orthogonal decomposition is carried out respectively, specifically:
The offline history normal data set of output variable is mapped to output correlation space and obtains regression coefficient matrix；
The related space data sets of output are mapped to regression coefficient matrix and obtain input correlation space data set, output is become
Measure offline history normal data set subtract input correlation space data set obtain inputting uncorrelated space data sets；
The transposed matrix of regression coefficient Matrix Multiplication regression coefficient matrix is obtained into regression matrix, singular value decomposition is carried out to it and is obtained
The mapping matrix mutually orthogonal to two；
The related space data sets of output are mapped to the mutually orthogonal mapping matrix of two above to obtain exporting correlation completely
Space data sets and uncorrelated space data sets are exported completely.
The utility model has the advantages that
The method of the present invention extracts the output relevant information of input variable data first with orthogonal offset minimum binary, to drop
Low computational load number, improves solution to model and releases performance, then thoroughly separate output correlation using complete orthogonal decomposition
Information and output irrelevant information, the failure that can more accurately detect penicillin fermentation process, more clearly from identification event
Whether barrier is related to output, thus the production for preferably operating the equipment of penicillin fermentation process to reduce cost, improve penicillin
Amount and quality.It is compared with the traditional method, the present invention improves the stability and accuracy of fault detection, passes through the simulation experiment result
Illustrate effectiveness of the invention and feasibility.
Detailed description of the invention
Fig. 1 is penicillin fermentation flow diagram；
The fault detection of 1 data of failure of Fig. 2 a specific embodiment of the invention counts spirogram；
It (a) is the Hotelling statistics of the online complete output correlation space for obtaining penicillin fermentation process input variable data
Spirogram；
(b) suddenly for the online principal component space for exporting uncorrelated space for obtaining penicillin fermentation process input variable data
Te Lin counts spirogram；
It (c) is the online residual error space for exporting uncorrelated space for obtaining penicillin fermentation process input variable data
SPE counts spirogram；
(d) suddenly for the online principal component space for inputting uncorrelated space for obtaining penicillin fermentation process output variable data
Special woods statistic and the penicillin fermentation process output variable data obtained online input the SPE in the residual error space in uncorrelated space
The combination of statistic counts spirogram；
The fault detection of 2 data of failure of Fig. 3 a specific embodiment of the invention counts spirogram；
It (a) is the Hotelling statistics of the online complete output correlation space for obtaining penicillin fermentation process input variable data
Spirogram；
(b) suddenly for the online principal component space for exporting uncorrelated space for obtaining penicillin fermentation process input variable data
Te Lin counts spirogram；
It (c) is the online residual error space for exporting uncorrelated space for obtaining penicillin fermentation process input variable data
SPE counts spirogram；
(d) suddenly for the online principal component space for inputting uncorrelated space for obtaining penicillin fermentation process output variable data
Special woods statistic and the penicillin fermentation process output variable data obtained online input the SPE in the residual error space in uncorrelated space
The combination of statistic counts spirogram.
Specific embodiment
Specific embodiments of the present invention will be described in detail with reference to the accompanying drawing.
Since the failure in penicillin fermentation process is the different faults input pair of ever-changing, different input variable
The influence of output variable is different, but finally can all influence the temperature of fermentation reactor and the output and quality of penicillin.
There are two kinds of failures for penicillin fermentation process in present embodiment:
Failure 1 is the failure that ventilation rate occurs, and is a kind of slope failure that slope is 0.5, in 101 sampled points to 200
Occur between a sampled point.Failure 2 is the failure that agitator power occurs, and is a kind of both phase step fault that amplitude is+3%, 101
A sampled point occurs between 200 sampled points.Using the procedure failure testing method based on concurrent offset minimum binary for logical
The penicillin fermentation process of 2 two kinds of different faults types of fault type of the fault type 1 and blender of wind rate is detected.
The procedure failure testing method based on concurrent offset minimum binary of present embodiment, comprising:
Step 1: the online input variable data x for obtaining penicillin fermentation processnewWith output variable data ynew。
In penicillin fermentation process, temperature controller and PH are arranged to closed-loop control, are gradually added into process of production
Glucose, it is that straight line increases in a manner of 0.01 that additional amount, which is by slope,.Three above condition setting, which can be controlled preferably, fermented
The linear relationship of each variable of journey.In view of concurrent offset minimum binary requires each variable to meet Gaussian Profile, the concentration of penicillin and
The temperature of fermentation reactor is more convenient for detecting, and selects following variable as outputting and inputting:
Input variable data are preferably the concentration of the substrate in penicillin fermentation process, carbon dioxide in present embodiment
Concentration, ventilation rate, agitator power, pH value and substrate feeding temperature；
Output variable data are preferably the concentration of the penicillin in penicillin fermentation process and ferment anti-in present embodiment
Answer the temperature of device.
Before executing step 2, need to carry out the input variable data and output variable data that obtain online specification and
Standardization, specifically making the mean value 0 and variance of input variable data and output variable data respectively is 1.
Step 2: handling input variable using the penicillin fermentation process Fault Model based on concurrent offset minimum binary
Data and output variable data: input variable data are expressed as the related space data sets of complete output, export uncorrelated space
Principal component space data set and export the sum of the residual error space data sets in uncorrelated space, output variable data are expressed as inputting
The residual error space data sets of correlation space data set, the principal component space data set in the uncorrelated space of input and the uncorrelated space of input
The sum of.
The method for building up of penicillin fermentation process Fault Model based on concurrent offset minimum binary is as follows:
Step 2.1: the acquisition offline history normal data set of input variableWith the offline history normal number of output variable
According to collectionWherein, subscript N is the number of data sampling, and M, J are variable number.
The offline history normal data set of each input variable and the offline history normal data set of output variable respectively include 200
Sample.
It is normal to the offline history normal data set of input variable and the offline history of output variable before executing step 2.2
Data set is standardized and is standardized.
Step 2.2: the offline history normal data set of input variable being divided into output correlation using orthogonal offset minimum binary
Space data sets XcWith output orthogonal intersection space data set Xo；
Using orthogonal offset minimum binary as a kind of preprocess method in present embodiment, removes and exports incoherent information,
Quantity that computational load can be reduced, the computation complexity to reduce step 2.3.
Step 2.3: related space data sets X will be exported using complete orthogonal decompositioncIt is normal with the offline history of output variable
Data set Y carries out complete orthogonal decomposition respectively, obtains the complete output correlation space of the offline history normal data set of input variable
Data setUncorrelated space data sets are exported completelyIt is related to the input of the offline history normal data set of output variable empty
Between data set Yc, the uncorrelated space data sets Y of inputr。
What step 2.3 included comprises the concrete steps that:
Step 2.3.1: the offline history normal data set of output variable is mapped to output correlation space and obtains regression coefficient square
Battle array；
Wherein, Ξ is regression coefficient matrix, and subscript T indicates transposition, subscriptIndicate inverse matrix.
After traditional deflected secondary air decomposes, to incline since there are one the principal component space of input variable and residual error space
Oblique angle, results in output relevant information and output irrelevant information is not completely segregated.Present embodiment is in order to reach complete
Orthogonal Decomposition solves regression coefficient matrix using above-mentioned formula.Wherein, uncorrelated space data sets Y is inputtedrIt is with input data
It is completely orthogonal, therefore in above-mentioned formulaIt should be 0.
Step 2.3.2: related space data sets X will be exportedcIt is mapped to regression coefficient matrix and obtains input correlation space number
According to collection, the offline history normal data set of output variable is subtracted into input correlation space data set and obtains inputting uncorrelated spatial data
Collection.
Step 2.3.3: the transposed matrix of regression coefficient Matrix Multiplication regression coefficient matrix is obtained into regression matrix, surprise is carried out to it
Different value decomposes to obtain two mutually orthogonal mapping matrixes.
Wherein, PΞ, cIt is using regression coefficient matrix as the load in the space of base vector, PΞ, rIt is using regression coefficient matrix as base
The load of the orthogonal intersection space in the space of vector, Λ are by Ξ ΞTEigenvalue cluster at diagonal matrix,It is wherein one
A mapping matrix,It is another mapping matrix.
According to the characteristic of singular value decomposition, it is known thatThat is PΞ, cAnd PΞ, rIt is orthogonal.Therefore with the two to
Amount is that the space of base vector is mutually orthogonal space.
Step 2.3.4: the related space data sets of output are mapped to the mutually orthogonal mapping matrix of two above and have been obtained
It is complete to export related space data sets and export uncorrelated space data sets completely.
Wherein,It is the related space data sets of complete output,It is to export uncorrelated space data sets, T completelyΞ, c=
XcPΞ, cIt is the complete score matrix for exporting related space data sets, TΞ, r=XcPΞ, rIt is to export uncorrelated space data sets completely
Score matrix.
According to the characteristic of singular value decomposition, it is known thatTherefore
In addition,ThereforeWithIt is orthogonal.It can thus be seen that relative to biography
Input variable complete orthogonal decomposition is output relevant information and output irrelevant information in this method by the offset minimum binary of system.
Step 2.4: not by the output orthogonal intersection space data set of the offline history normal data set of input variable and complete output
Correlation space data set merges into the uncorrelated space data sets of output.
It considersAnd XoIt is in the incoherent information of output, it is not necessary that closed to its independent analysis, therefore by them
And.
Wherein, XrIt is the uncorrelated space data sets of output.
Step 2.5: uncorrelated space data sets will be exported using pivot analysis be divided into and export the pivot in uncorrelated space
Space data sets and the residual error space data sets for exporting uncorrelated space.
Wherein,It is the principal component space data set for exporting uncorrelated space,It is the residual error space for exporting uncorrelated space
Data set, TX, rIt is the score matrix for exporting the principal component space data set in uncorrelated space, PX, rIt is the master for exporting uncorrelated space
The load matrix of first space data sets.
Step 2.6: using pivot analysis by the uncorrelated spatial data of input of the offline history normal data set of output variable
Collection is divided into the principal component space data set for inputting uncorrelated space and inputs the residual error space data sets in uncorrelated space.
Wherein,It is the principal component space data set for inputting uncorrelated space,It is the residual error space for inputting uncorrelated space
Data set, TY, rIt is the score matrix for inputting the principal component space data set in uncorrelated space, PY, rIt is the master for inputting uncorrelated space
The load matrix of first space data sets.
Step 2.7: input variable data being expressed as to export the master of related space data sets, the uncorrelated space of output completely
It is related to be expressed as input by first space data sets and the sum of the residual error space data sets for exporting uncorrelated space for output variable data
The residual error space data sets of space data sets, the principal component space data set in the uncorrelated space of input and the uncorrelated space of input it
With to get arrive the penicillin fermentation process Fault Model based on concurrent offset minimum binary.
Step 3: calculating input variable data and exporting the Hotelling statistic in correlation space, the uncorrelated sky of output completely
Between principal component space in Hotelling statistic, the uncorrelated space of output residual error space in SPE statistic, calculate output and become
Data are measured in the Hotelling statistic in the principal component space for inputting uncorrelated space and are inputted in the residual error space in uncorrelated space
SPE statistic.
Wherein,It is Hotelling statistic of the input variable data in output correlation space completely,It is to export not phase
Close the Hotelling statistic in the principal component space in space, QxIt is the SPE statistic in the residual error space for export uncorrelated space,
It is Hotelling statistic of the output variable data in the principal component space for inputting uncorrelated space, QyIt is output variable data defeated
Enter the SPE statistic in the residual error space in uncorrelated space, xNew, cIt is the output correlation space data sets of input variable data,
xNew, oIt is the output orthogonal intersection space data set of input variable data,It is the uncorrelated space of output completely of input variable data
Data set, yNew, rIt is the uncorrelated space data sets of input of output variable data,It is input variable data
The uncorrelated space data sets of output,It is that the complete of input variable data exports obtaining for related space data sets
Divide vector,It is the online uncorrelated space data sets of output for obtaining penicillin fermentation process input variable data
Score vector,It is the score vector of the uncorrelated space data sets of input of output variable data,It is the residual error space data sets in the uncorrelated space of output of input variable data,It is the residual error space data sets in the uncorrelated space of input of output variable data.
Step 4: calculating Hotelling statistic of the output variable data in the principal component space for inputting uncorrelated space and defeated
Enter the combination statistic of the SPE statistic in the residual error space in uncorrelated space.
Wherein, ΦyThe Hotelling statistic for being output variable data in the principal component space for inputting uncorrelated space and defeated
The combination statistic of SPE statistic of the variable data in the residual error space for inputting uncorrelated space out,It is output variable
The corresponding control limit of Hotelling statistic of the data in the principal component space for inputting uncorrelated space,It is output variable
The corresponding control limit of SPE statistic of the data in the residual error space for inputting uncorrelated space.
Step 5: current penicillin fermentation process breakdown judge:
Judge Hotelling statistic of the input variable data being calculated in output correlation space completelyIt is whether high
In its corresponding control limit: being that then output dependent failure occurs in current penicillin fermentation process；It is no to then follow the steps 6.
Step 6: judging Hotelling statistic of the input variable data in the principal component space for exporting uncorrelated spaceWith
SPE statistic Q of the input variable data in the residual error space for exporting uncorrelated spacexIn whether have at least one statistic high
In its corresponding control limit: being that then current penicillin fermentation process occurs exporting the uncorrelated but relevant failure of input, otherwise
Execute step 7.
Step 7: judging Hotelling statistic of the output variable data in the principal component space for inputting uncorrelated space and defeated
The combination statistic Φ of SPE statistic of the variable data in the residual error space for inputting uncorrelated space outyWhether its phase is higher than
The control limit answered: it is that then uncertain output dependent failure occurs in current penicillin fermentation process, otherwise continues online obtain
The input variable data and output variable data of subsequent time penicillin fermentation process, return step 3.
In present embodiment, 200 sampled points of the offline history normal data of penicillin fermentation process, benefit are chosen first
With the penicillin fermentation process Fault Model based on concurrent offset minimum binary, on-line checking fault type 1 and fault type
2, the data of 1~table of table 3 are partial data:
Table 1 establishes four groups of data in the penicillin fermentation process Fault Model based on concurrent offset minimum binary
Table 2 detects two groups of data in the fault data 1 of penicillin fermentation process
Table 3 detects two groups of data in the fault data 2 of penicillin fermentation process
Although Hotelling statistic of the input variable data in output correlation space completely as seen from Figure 2?
Exist between 101 to 108 sampled points and fail to report, control limit is less than in 100 sampled points, in 109-200 sampled point
It detected slope failure.And Hotelling statistic of the input variable data in the principal component space for exporting uncorrelated space
SPE statistic Q of the input variable data in the residual error space for exporting uncorrelated spacexNot phase is being inputted with output variable data
Hotelling statistic and output variable data in the principal component space in pass space is in the residual error space for inputting uncorrelated space
The combination statistic Φ of SPE statisticyControl limit is less than in 200 sampled points.This illustrates that this failure is an output
Relevant slope failure, is true to life.
Although occurring the point slightly beyond control limit as seen from Figure 3, input variable data are related empty in complete output
Between in Hotelling statisticHotelling statistic of the input variable data in the principal component space for exporting uncorrelated space
It is being inputted with Hotelling statistic of the output variable data in the principal component space for inputting uncorrelated space and output variable data
The combination statistic Φ of SPE statistic in the residual error space in uncorrelated spaceyControl is less than in 200 sampled points
Limit.And SPE statistic Q of the input variable data in the residual error space for exporting uncorrelated spacexIn failure introduce region (101-
It 200) has been more than control limit.This illustrates that this failure is the incoherent both phase step fault of output, is true to life.
It can illustrate that this method can be good at detecting blueness by Fig. 2 (a)~(d) and Fig. 3 (a)~(d) testing result
Failure in mould category fermentation process.The testing result of the fault type 2 of fault type 1 and different amplitudes for Different Slope is shown in
4~table of table 5.
The verification and measurement ratio and test point of the fault data 1 of 4 penicillin fermentation process of table
Verification and measurement ratio, rate of false alarm and the test point of the fault data 2 of 5 penicillin fermentation process of table
Wherein, the test point in 4~table of table 5 is continuous five or more samples are more than the sampled point of control limit, inspection
The calculation method of survey rate and rate of false alarm is as follows:
As can be seen from Table 4, for different failure slopes, the input variable data in penicillin fermentation process are complete
Export the Hotelling statistic in correlation spaceVerification and measurement ratio 90% or more.And the input in penicillin fermentation process becomes
Measure Hotelling statistic of the data in the principal component space for exporting uncorrelated spaceInput variable in penicillin fermentation process
SPE statistic Q of the data in the residual error space for exporting uncorrelated spacexWith the output variable data in penicillin fermentation process
Output variable data in the Hotelling statistic and penicillin fermentation process in the principal component space for inputting uncorrelated space exist
Input the combination statistic Φ of the SPE statistic in the residual error space in uncorrelated spaceyVerification and measurement ratio within 10%.Therefore
And, it can be seen that failure is not detected in this three statistics.Further, it can be seen that the input variable in penicillin fermentation process
Hotelling statistic of the data in output correlation space completelyTest point within 110.It can thus be seen that for
The failure of Different Slope, this method can efficiently and accurately detected.
As can be seen from Table 5, the input variable data for the fault type 2 of different amplitudes, in penicillin fermentation process
SPE statistic Q in the residual error space for exporting uncorrelated spacexVerification and measurement ratio 98% or more, test point 103 with
It is interior, Hotelling statistic of the input variable data in output correlation space completely in penicillin fermentation processRate of false alarm
Within 1.5%.Therefore, this method can efficiently and accurately detected fault type 2.
It is available by the above results, by the method for the invention, different type, different faults size can be effectively detected
Output dependent failure and export uncorrelated failure.
Although specific embodiments of the present invention have been described above, those skilled in the art in the art should be managed
Solution, these are merely examples, and many changes and modifications may be made, without departing from the principle of the present invention
And essence.The scope of the present invention is only limited by the claims that follow.
Claims (6)
1. a kind of procedure failure testing method based on concurrent offset minimum binary, which is characterized in that including
The online input variable data and output variable data for obtaining penicillin fermentation process；
Utilize penicillin fermentation process Fault Model processing input variable data and output based on concurrent offset minimum binary
Variable data: input variable data are expressed as to export the principal component space of related space data sets, the uncorrelated space of output completely
Output variable data are expressed as input correlation space number by data set and the sum of the residual error space data sets for exporting uncorrelated space
According to the sum of the residual error space data sets of the principal component space data set and the uncorrelated space of input that collect, input uncorrelated space；
It is empty to calculate Hotelling statistic, the pivot in output uncorrelated space of the input variable data in output correlation space completely
Between in Hotelling statistic, the uncorrelated space of output residual error space inSPEStatistic calculates output variable data defeated
Enter the Hotelling statistic in the principal component space in uncorrelated space and inputs in the residual error space in uncorrelated spaceSPEStatistics
Amount；
Calculate Hotelling statistic and input uncorrelated sky of the output variable data in the principal component space for inputting uncorrelated space
Between residual error space inSPEThe combination statistic of statistic；
Current penicillin fermentation process breakdown judge:
Judge whether Hotelling statistic of the input variable data being calculated in output correlation space completely is higher than its phase
The control limit answered: being, then output dependent failure occurs in current penicillin fermentation process；Otherwise:
Judge Hotelling statistic and input variable data of the input variable data in the principal component space for exporting uncorrelated space
In the residual error space for exporting uncorrelated spaceSPEWhether there is at least one statistic to be higher than its corresponding control in statistic
Limit: being, then current penicillin fermentation process occurs exporting the uncorrelated but relevant failure of input, otherwise:
Judge that Hotelling statistic and output of the output variable data in the principal component space for inputting uncorrelated space are inputting not
In the residual error space of correlation spaceSPEWhether the combination statistic of statistic is higher than its corresponding control limit: be, then it is current green
There is uncertain output dependent failure in mycin fermentation process, otherwise continues online acquisition subsequent time penicillin fermentation process
Input variable data and output variable data;
The method for building up of the penicillin fermentation process Fault Model based on concurrent offset minimum binary is as follows:
Acquire the offline history normal data set of input variable and the offline history normal data set of output variable；
Using orthogonal offset minimum binary by the offline history normal data set of input variable be divided into the related space data sets of output and
Export orthogonal intersection space data set；
Related space data sets will be exported using complete orthogonal decomposition and the offline history normal data set of output variable carries out respectively
Complete orthogonal decomposition, the related space data sets of complete output for obtaining the offline history normal data set of input variable, output completely
The input correlation space data set of uncorrelated space data sets and the offline history normal data set of output variable and input are uncorrelated
Space data sets；
Uncorrelated spatial data is exported by the output orthogonal intersection space data set of the offline history normal data set of input variable and completely
Collection merges into the uncorrelated space data sets of output；
Uncorrelated space data sets will be exported and be divided into the principal component space data set for exporting uncorrelated space and the uncorrelated sky of output
Between residual error space data sets；
The uncorrelated space data sets of input of the offline history normal data set of output variable are divided into the uncorrelated space of input
Principal component space data set and the residual error space data sets for inputting uncorrelated space；
Input variable data are expressed as to export the principal component space data set of related space data sets, the uncorrelated space of output completely
With the sum of the residual error space data sets for exporting uncorrelated space, by output variable data be expressed as input correlation space data set,
It inputs the principal component space data set in uncorrelated space and inputs the sum of the residual error space data sets in uncorrelated space to get to being based on
The penicillin fermentation process Fault Model of concurrent offset minimum binary.
2. the method according to claim 1, wherein the input variable data are in penicillin fermentation process
Concentration, the concentration of carbon dioxide, ventilation rate, agitator power, pH value and the substrate feeding temperature of substrate, the output variable number
According to the temperature of concentration and fermentation reactor for the penicillin in penicillin fermentation process.
3. the method according to claim 1, wherein utilizing the penicillin based on concurrent offset minimum binary described
Before fermentation process Fault Model handles input variable data and output variable data, to the input variable number obtained online
It is standardized and is standardized according to output variable data.
4. according to the method described in claim 3, it is characterized in that, the specification and standardization, specifically become respectively input
The mean value of amount data and output variable data is 0 and variance is 1.
5. the method according to claim 1, wherein it is described using orthogonal offset minimum binary by input variable from
Line history normal data set is divided into before the related space data sets of output and output orthogonal intersection space data set, to input variable from
Line history normal data set and the offline history normal data set of output variable are standardized and are standardized.
6. the method according to claim 1, wherein described will export correlation space number using complete orthogonal decomposition
Complete orthogonal decomposition is carried out respectively according to collection and the offline history normal data set of output variable, specifically:
The offline history normal data set of output variable is mapped to output correlation space and obtains regression coefficient matrix；
Related space data sets will be exported be mapped to regression coefficient matrix and obtain input correlation space data set, by output variable from
Line history normal data set subtracts input correlation space data set and obtains inputting uncorrelated space data sets；
The transposed matrix of regression coefficient Matrix Multiplication regression coefficient matrix is obtained into regression matrix, singular value decomposition is carried out to it and obtains two
A mutually orthogonal mapping matrix；
The related space data sets of output are mapped to the mutually orthogonal mapping matrix of two above to obtain exporting correlation space completely
Data set and uncorrelated space data sets are exported completely.
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