CN110298385A - One kind is based on * information and the online incipient fault detection method of increment SVDD - Google Patents

One kind is based on * information and the online incipient fault detection method of increment SVDD Download PDF

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CN110298385A
CN110298385A CN201910486042.3A CN201910486042A CN110298385A CN 110298385 A CN110298385 A CN 110298385A CN 201910486042 A CN201910486042 A CN 201910486042A CN 110298385 A CN110298385 A CN 110298385A
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sample
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svdd
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CN110298385B (en
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周猛飞
张强
刘志红
蔡亦军
潘海天
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Zhejiang University of Technology ZJUT
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
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    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/19—Recognition using electronic means
    • G06V30/192—Recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references
    • G06V30/194—References adjustable by an adaptive method, e.g. learning

Abstract

The invention proposes one kind to be based onThe online incipient fault detection method of information and increment SVDD.It is based onThe online incipient fault detection method of information and increment Support Vector data description SVDD, is first standardized the process measurement data under nominal situation, obtains process training sample set and correspondingThe value of information；Calculate each measurand withAssociation relationship between effect filters out characteristic variable relevant to efficiency according to accumulative mutual information contribution rate；SVDD initial model is established, adaptive updates association relationship and more new increment SVDD model are carried out for the newly-increased sample of process respectively, obtain the increment SVDD state model under nominal situation；The model under different conditions is obtained according to the measurement data of different conditions, and is used for online incipient fault detection.The advantages of present invention incorporates based on ENERGY METHOD and based on data-driven method, during not only can solve the problem of system parameter time-varying characteristics, energy feature variable during can also efficiently extracting, the rate and precision for improving incipient fault detection, effectively realize the on-line checking of initial failure.

Description

One kind is based on * information and the online incipient fault detection method of increment SVDD
Technical field
The present invention relates to process initial failure on-line monitoring fields more particularly to one kind to be based onInformation and increment support to Measure the online incipient fault detection method of data description (Support Vector Data Description, SVDD).
Background technique
SVDD is single class sorting algorithm based on support region, has the description of intuitive data and good popularization performance, also It can solve the problems, such as that real process failure classes sample is difficult to obtain, have been widely used for the fields such as fault diagnosis.But SVDD Need to solve quadratic programming problem, training complexity is higher, and it trains complexity and sample number exponentially grade relationship.? In SVDD fault detection research, in order to reduce SVDD computation complexity, while the validity of sample data is improved, usually with statistics Method carries out Feature Dimension Reduction and extracts feature samples, then establishes SVDD disaggregated model using this feature sample to realize failure Detection.As the concept for combining the first and second law of thermodynamics, it can be used to more fully understand that process, quantization are inefficient Direction and differentiation energy quality, it is the multi-field universal concept such as physics, chemistry, Mechanics of Machinery, is usedConcept also can Reduce data dimension.It is based onModeling work amount can be effectively reduced in the method for information, increases computational efficiency, in model dimensionality reduction While remain significantly similitude.Effect is whole in Chemical Processing SystemsThe specific manifestation form of information, and And be also the evaluation index of process integral energy quality, so being based onThe method of information extraction is will in the processEffect is extracted Out, to achieve the effect that dimensionality reduction.
However in actual industrial processes, systematic procedure parameter can change with the propulsion of time, therefore Barrier feature can also change therewith, and Fault Model needs the newly-increased sample of training, and SVDD has to give up original instructed at this time The model perfected, need to by new data together with historical data re -training, by its algorithm complexity it is found that with new data sample It is continuously updated, the computation complexity of the algorithm exponentially increases.Once there is the case where mass data sample on-line training, The training process of SVDD can waste a large amount of calculating time and memory space, and algorithm is caused to be unable to satisfy the need of system real-time update It asks.Relative to traditional batch type SVDD, incremental learning technology can inherit the knowledge acquired, on the basis of original model According to new data sample come more new model, this can not only make model knowledge have hereditability, additionally it is possible to cope with real process Time-varying problem.Meanwhile history energy feature variable can not also be suitable for new process operating condition and new data, need to exist again Energy feature variable is extracted in historical data and the set of new data.Usual newest measurand data can highlight active procedure The characteristic of system, and the system performance that older historical data is reflected differs remoter with newest system mode.So in order to It can reflect system current properties in time, need historical data to be added weight to be added to reduce the influence power of historical data Forgetting factor.Furthermore, it is necessary to rationally adjust forgetting factor according to the situation of change of current system parameter.If can be from sample set Effectively On-line testingInformation reuses fault detection algorithm after constructing energy feature sample set, it will is obviously improved early stage The efficiency of fault detection.
Summary of the invention
The present invention is difficult to anti-aiming at the problem that system parameter time-varying characteristics in real process with the energy feature filtered out Incremental Learning Algorithm and the power feature extraction based on variable forgetting factor has been respectively adopted in the problem of reflecting the dynamic characteristic of process Method, in extracted in self-adaptive procedure faultWhile information, moreover it is possible to establish the incremental detection model of continuous renewal, Jin Erti For a kind of incipient fault detection method.
One kind is based onInformation and the online incipient fault detection method of increment SVDD, comprising the following steps:
(1) collect process measurand withSample is imitated, and it is standardized, is obtainedAnd Y0；To sample This progress data prediction is mainly pre-processed using data normalization, and specific implementation step is as follows:
For training sample data x1,x2,…xN, sample xiStandardization calculation formula are as follows:
Wherein, xi、Sample after respectively indicating i-th of original offline sample and standardization, θ are all sample datas Arithmetic mean of instantaneous value, σ are the variance of all samples.By standardization, detection error caused by variable range can be eliminated；
(2) it calculates in step 1And Y0Between different measurands withThe association relationship of effectAccording to tired Meter mutual information contribution rate principle filter out withImitate maximally related one group of energy feature sample set X0, establish energy feature sample Detailed step are as follows:
In order to reduce influence of the sample size to Mutual Information Estimation, the mutual trust between two variables is calculated using k-nearest neighbor Breath value:
Wherein, N is the number of total sample, and k is neighbour's number, sxAnd syIt is expressed as in the subspace of X and Y close to most The number of samples of neighbour, φ () are digamma function.Simultaneously to ensure that mutual information can efficiently extract energy information, can make The accumulation mutual information contribution degree of g measurand of selection are as follows:
G feature is included before CPMI is illustratedEffect information accounts for whole systemsThe ratio for imitating information, usually uses it To determine the Characteristic Number filtered out.Since the mutual information of each feature is greater than 0, then CPMI is in the feature filtered out Monotonic increase in number value range.In order to reach relatively good Feature Dimension Reduction effect, need so that CPMI is greater than defined control System limit；
(3) the energy feature sample set X obtained by step 20, and construct initial SVDD model Γ0, establish the detailed of model Step are as follows:
Give a training set [x1, x2..., xN], wherein N is sample number.A and R respectively indicates hyperspherical center and half Diameter.By structural risk minimization it is found that this suprasphere radius minimization problem can be described as it is following with inequality The quadratic programming problem of constraint, while introducing slack variable ξiAnd penalty factor:
For the quadratic programming problem of above-mentioned with constraint conditions, Lagrange multiplier can be introduced, by the quadratic programming problem After being converted into its dual form, often it is more easier to solve, and kernel function can be introduced, luv space is projected into higher dimensional space To solve nonlinear problem:
By asking for quadratic programming optimization algorithm SMO algorithm (Sequential Minimal Optimization, SMO) Solution obtains optimal solution αi, to obtain the model parameter Γ of SVDD0；
(4) increment sample is introducedAnd Y1Afterwards, according to the power feature extraction rule based on variable forgetting factor come adaptive Answer the more corresponding association relationship of new increment sampleThe energy feature of increment sample is filtered out according to Feature Dimension Reduction principle SetThe step of adaptive energy feature extraction are as follows:
After the corresponding association relationship of newly-increased data sample all adds forgetting factor ρ, for the t+1 times newly-increased measurement sample ThisWith corresponding bright effect data sample Yt+1, association relationship between the two are as follows:
Wherein ρ ∈ (0,1],Represent the column vector that j-th of measurand feature is constituted in t+1 newly-increased samples, m Measured variable number is referred to,H(Yt+1)、Respectively indicate the comentropy and between the two of variable Joint entropy.
The size of current forgetting factor can adaptively be adjusted according to the Parameters variation of time-varying system by becoming forgetting factor method, right In the variable forgetting factor ρ of kth+1 time newly-increased sample association relationship are as follows:
CPMI in formulat+1Refer to the accumulative mutual information contribution rate of the t+1 times newly-increased data character pair sample, gt+1The choosing of value Taking is determined according to the accumulative mutual information contribution rate principle of current sample, CPMIt+1Value is typically larger than 0.85.Finally, according to The CPMI of current sample sett+1Filter out new energy feature set
(5) according to the initial SVDD model Γ of step 30With the energy feature sample of step 4)Update obtains increment SVDD model Γ1, the specific steps of incremental update are as follows:
1) statistic and statistics limit；
Given sample set X=[x1,x2,…,xN], the dual form of SVDD suprasphere radius optimization problem are as follows:
Whereinδ is the Optimization Compensation factor, and kernel function can be denoted as Kij=K (xi,xj)；By KKT condition it is found that W to there is optimal solution, to αi, the first derivative of δ need to meet following condition:
Wherein Ω (xi) be SVDD sample discriminant function.
By formula (10) it is found that the KKT condition of SVDD is usually divided into three classes training sample set: non-supporting vector R, mark in ball Quasi- supporting vector S and boundary supporting vector E.For the new samples x being just addedu, need to change model parameter to make the sample of extension This collection reaches KKT condition again, so that new optimal solution is obtained, model coefficient variation are as follows:
Wherein αuIt is the new samples x being just addeduCorresponding Lagrange multiplier, Δ αuFor new samples Lagrange multiplier Variable quantity, Δ αjOriginal sample set Lagrange multiplier variable quantity after being added for new samples.Standard supporting vector model coefficient Variable quantity is writeable are as follows:
Enable T=K-1, for the sample in E, R set, κi≡ 0, from formula (13):
It enables
In formula,For model boundary sensitive factor, for the sample in S set,Carry it into formula (12) In, then have:
2) sample attribute migrates；
Sample attribute migration is by increment sample pattern index variation amount Δ αuThe variation of original sample pattern coefficient is adjusted, Extension sample set is set to meet KKT condition again, to tend to the process of a new equilibrium state.In increment sample attribute transition process In, attribute is all satisfied Δ αu> 0, six kinds introduced below different sample attribute migration situations:
①κsAs the corresponding property value set of standard supporting vector collectionIn s-th of number, root Δ α can be calculated according to formula (14)sIf it meets upper limit Δ αs≤C-αs, then Δ αuAnd κsJack per line, xsBecome by standard supporting vector For boundary supporting vector:
In formula, λ is migration factor.
2. if Δ αsIn lower limit Δ αs≥-αs, then Δ αuAnd κsContrary sign, xsBecome non-supporting in ball from standard supporting vector Vector:
3. for vector x non-supporting in ballr, then have dr> 0,R-th as vector sensitive factor collection non-supporting in ball Δ d can be calculated according to formula (16) in elementr.If Δ dr< 0, non-supporting vector x in ballrIt can become standard supporting vector:
4. for boundary supporting vector xe, then have dr< 0, e-th of element of non-supporting vector sensitive factor collection can table in ball It is shown asIt can then be calculatedIf Δ de> 0, boundary supporting vector xeIt can become standard supporting vector:
5. usually setting increment sample xuInitial model sample coefficient be zero, work as duSample x is then thought when >=0uFor ball Interior target class sample, then its model sample coefficient is without updating.Work as du< 0, increment sample xuThen become standard supporting vector:
6. during increment SVDD model training, αuUpper dividing value is penalty factor, works as αuWhen≤C, increment sample αuIt will Become boundary supporting vector, increment coefficient variable quantity are as follows:
λma=C- αu (22)
In increment SVDD, Δ α is enabledmax=min { λsp, λsm, λrs, λes, λc, λmaIt is used as increment sample αuModel coefficient Variable quantity, formula (14) can obtain archetype sample coefficient variation delta αi；
3) T matrix update；
It also needs to update simultaneously during T matrix model increment iterative in formula (13), in order to reduce calculating finding the inverse matrix Complexity, take following more new strategy.For an arbitrary sample xl∈R∪E∪{xuBecome standard supporting vector, t The T of+1 renewal processt+1Are as follows:
As standard supporting vector xlWhen leaving set S, Tt+1Are as follows:
(6) increment sample is continually introduced, dynamically updates the increment SVDD model after t times out according to step 4), step 5) Γt.And for different faults state sample, calculate the increment SVDD model Γ of different faults stateth；
(7) to on-line testing sample setData normalization and power feature extraction are carried out, it is calculated and corresponds to h failure shape The relative distance γ of states modelh(xhi), it is based on relative distance minimum principle, detection and identification goes out the corresponding malfunction class of sample Not, detailed step are as follows:
Diagnostic method based on relative distance can be by divided by the suprasphere radius in each failure state model, Lai Jiaqiang The fault condition detection rate of decision rule raising algorithm.Relative distance not only can detect that failure, moreover it is possible to judge out of order tight Weight degree.Use relative distance as criterion:
Compared with traditional technology, the medicine have the advantages that
The advantages of present invention incorporates based on ENERGY METHOD and based on data-driven method, system during not only can solve The problem of parameter time varying characteristic, moreover it is possible to which the energy feature variable during efficiently extracting improves the rate of incipient fault detection And precision, effectively realize the on-line checking of initial failure.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention；
Fig. 2 is the schematic diagram of industrial propylene rectification tower；
Fig. 3 is increment type and batch type model training time comparison diagram；
Fig. 4 a~Fig. 4 c is the existing online incipient fault detection of SVDD method as a result, wherein Fig. 4 a is sample in normal shape Performance in states model, Fig. 4 b are performance of the sample on medium degenerate state model, and Fig. 4 c is sample in serious degenerate state Performance on model；
Fig. 5 a~Fig. 5 c is the online incipient fault detection of increment type SVDD method as a result, wherein Fig. 5 a is sample in normal shape Performance in states model, Fig. 5 b are performance of the sample on medium degenerate state model, and Fig. 5 c is sample in serious degenerate state Performance on model；
Fig. 6 a~Fig. 6 c is the online incipient fault detection of the method for the present invention as a result, wherein Fig. 6 a is sample in normal condition mould Performance in type, Fig. 6 b are performance of the sample on medium degenerate state model, and Fig. 6 c is sample in serious degenerate state model On performance.
Specific embodiment
Come that the present invention is described in detail below with reference to factory's embodiment and attached drawing, this is fully understood with case study The process of invention application implementation and the validity of method.
One kind is based onInformation and the online incipient fault detection method of increment SVDD, specific embodiment are as follows:
Using certain industrial propylene rectification tower as research object, the charging of tower mainly based on propane and propylene, the two it is opposite Volatility, in order to reach preferable separating effect, is operated close to 1 using cascade towers.Certain industrial propylene rectification tower process is such as Shown in Fig. 2.1# propylene rectification tower is equivalent to the stripping section of distillation process, which is equipped with 77 layers of column plate, in the condition of flow control Lower its tower reactor discharging is used as recycled propane.The kettle material of 2# propylene rectification tower squeezes into 1# propylene rectification tower tower by reflux pump Top, as being 1# propylene rectification tower overhead reflux.Propylene product is sent to propylene product guard bed after tower top cooler, thus Remove some oxide impurities.The industry rectifying case is built upon on the ASPEN Math of certain factory's propylene rectification tower, The propylene distillation process is simulated using 8.4 software of Aspen Tech.In order to simulate fouling of heat exchangers event in distillation process Barrier, process are calculated using the double steam heat exchanger modules calculated in detail by the film coefficient and tube wall resistance of geometry calculation Overall heat-transfer coefficient chooses the Fouling in Condenser factor and Spline smoothing occurs to simulate distillation process initial failure.If condenser Fouling factor increases, and will lead to heat transfer efficiency reduction, and exchanger heat flux is reduced, and process is effectiveIt is wasted,Effect will It reduces.The index as measurement process available energy utilization efficiency is imitated, can significantly reflect distillation process initial failure situation, institute Whether to illustrate initial failure using the Spline smoothing of the Fouling in Condenser factor.In simulation process, by by the condensation of No. 2 towers The fouling factor parameter of device changes to 8.6 × 10- from 03(sqmK)/Watt, to simulate the initial failure in distillation process.
(1) collect process measurand withSample is imitated, and it is standardized, is obtainedAnd Y0；To sample This progress data prediction is mainly pre-processed using data normalization, and specific implementation step is as follows:
For training sample data x1,x2,…xN, sample xiStandardization calculation formula are as follows:
Wherein, xi、Sample after respectively indicating i-th of original offline sample and standardization, θ are all sample datas Arithmetic mean of instantaneous value, σ are the variance of all samples.By standardization, detection error caused by variable range can be eliminated；
(2) it calculates in step 1And Y0Between different measurands withThe association relationship of effectAccording to tired Meter mutual information contribution rate principle filter out withImitate maximally related one group of energy feature sample set X0, establish energy feature sample Detailed step are as follows:
In order to reduce influence of the sample size to Mutual Information Estimation, the mutual trust between two variables is calculated using k-nearest neighbor Breath value:
Wherein, N is the number of total sample, and k is neighbour's number, sxAnd syIt is expressed as in the subspace of X and Y close to most The number of samples of neighbour, φ () are digamma function.Simultaneously to ensure that mutual information can efficiently extract energy information, can make The accumulation mutual information contribution degree of g measurand of selection are as follows:
G feature is included before CPMI is illustratedEffect information accounts for whole systemsThe ratio for imitating information, usually uses it To determine the Characteristic Number filtered out.Since the mutual information of each feature is greater than 0, then CPMI is in the feature filtered out Monotonic increase in number value range.In order to reach relatively good Feature Dimension Reduction effect, need so that CPMI is greater than defined control System limit, usually control limit take 85%；
(3) the energy feature sample set X obtained by step 20, and construct initial SVDD model Γ0, establish the detailed of model Step are as follows:
Give a training set [x1, x2..., xN], wherein N is sample number.A and R respectively indicates hyperspherical center and half Diameter.By structural risk minimization it is found that this suprasphere radius minimization problem can be described as it is following with inequality The quadratic programming problem of constraint, while introducing slack variable ξiAnd penalty factor:
For the quadratic programming problem of above-mentioned with constraint conditions, Lagrange multiplier can be introduced, by the quadratic programming problem After being converted into its dual form, often it is more easier to solve, and kernel function can be introduced, luv space is projected into higher dimensional space To solve nonlinear problem:
By asking for quadratic programming optimization algorithm SMO algorithm (Sequential Minimal Optimization, SMO) Solution obtains optimal solution αi, to obtain the model parameter Γ of SVDD0；
(4) increment sample is introducedAnd Y1Afterwards, according to the power feature extraction rule based on variable forgetting factor come adaptive Answer the more corresponding association relationship of new increment sampleThe energy for filtering out increment sample according to Feature Dimension Reduction principle is special Collection is closedThe step of adaptive energy feature extraction are as follows:
After the corresponding association relationship of newly-increased data sample all adds forgetting factor ρ, for the t+1 times newly-increased measurement sample ThisWith corresponding bright effect data sample Yt+1, association relationship between the two are as follows:
Wherein ρ ∈ (0,1],Represent the column vector that j-th of measurand feature is constituted in t+1 newly-increased samples, m Measured variable number is referred to,H(Yt+1)、Respectively indicate the comentropy and between the two of variable Joint entropy.
The size of current forgetting factor can adaptively be adjusted according to the Parameters variation of time-varying system by becoming forgetting factor method, right In the variable forgetting factor ρ of kth+1 time newly-increased sample association relationship are as follows:
CPMI in formulat+1Refer to the accumulative mutual information contribution rate of the t+1 times newly-increased data character pair sample, gt+1The choosing of value Taking is determined according to the accumulative mutual information contribution rate principle of current sample, CPMIt+1Value is typically larger than 0.85.Finally, according to The CPMI of current sample sett+1Filter out new energy feature set
(5) according to the initial SVDD model Γ of step 30With the energy feature sample of step 4)Update obtains increment SVDD model Γ1, the specific steps of incremental update are as follows:
1) statistic and statistics limit；
Given sample set X=[x1, x2..., xN], the dual form of SVDD suprasphere radius optimization problem are as follows:
Whereinδ is the Optimization Compensation factor, and kernel function can be denoted as Kij=K (xi, xj)；By KKT condition it is found that W to there is optimal solution, to αi, the first derivative of δ need to meet following condition:
Wherein Ω (xi) be SVDD sample discriminant function.
By formula (10) it is found that the KKT condition of SVDD is usually divided into three classes training sample set: non-supporting vector R, mark in ball Quasi- supporting vector S and boundary supporting vector E.For the new samples x being just addedu, need to change model parameter to make the sample of extension This collection reaches KKT condition again, so that new optimal solution is obtained, model coefficient variation are as follows:
Wherein αuIt is the new samples x being just addeduCorresponding Lagrange multiplier, Δ αuFor new samples Lagrange multiplier Variable quantity, Δ αjOriginal sample set Lagrange multiplier variable quantity after being added for new samples.Standard supporting vector model coefficient Variable quantity is writeable are as follows:
Enable T=K-1, for the sample in E, R set, κi≡ 0, from formula (13):
It enables
In formula,For model boundary sensitive factor, for the sample in S set,It carries it into formula (12), Then have:
2) sample attribute migrates；
Sample attribute migration is by increment sample pattern index variation amount Δ αuThe variation of original sample pattern coefficient is adjusted, Extension sample set is set to meet KKT condition again, to tend to the process of a new equilibrium state.In increment sample attribute transition process In, attribute is all satisfied Δ αu> 0, six kinds introduced below different sample attribute migration situations:
①κsAs the corresponding property value set of standard supporting vector collectionIn s-th of number, root Δ α can be calculated according to formula (14)sIf it meets upper limit Δ αs≤C-αs, then Δ αuAnd κsJack per line, xsBecome by standard supporting vector For boundary supporting vector:
In formula, λ is migration factor.
2. if Δ αsIn lower limit Δ αs≥-αs, then Δ αuAnd κsContrary sign, xsBecome non-supporting in ball from standard supporting vector Vector:
3. for vector x non-supporting in ballr, then have dr> 0,R-th as vector sensitive factor collection non-supporting in ball Δ d can be calculated according to formula (16) in elementr.If Δ dr< 0, non-supporting vector x in ballrIt can become standard supporting vector:
4. for boundary supporting vector xe, then have dr< 0, e-th of element of non-supporting vector sensitive factor collection can table in ball It is shown asIt can then be calculatedIf Δ de> 0, boundary supporting vector xeIt can become standard supporting vector:
5. usually setting increment sample xuInitial model sample coefficient be zero, work as duSample x is then thought when >=0uFor ball Interior target class sample, then its model sample coefficient is without updating.Work as du< 0, increment sample xuThen become standard supporting vector:
6. during increment SVDD model training, αuUpper dividing value is penalty factor, works as αuWhen≤C, increment sample αuIt will Become boundary supporting vector, increment coefficient variable quantity are as follows:
λma=C- αu (22)
In increment SVDD, Δ α is enabledmax=min { λsp,λsm,λrs,λes,λc,λmaIt is used as increment sample αuModel coefficient Variable quantity, formula (14) can obtain archetype sample coefficient variation delta αi；
3) T matrix update；
It also needs to update simultaneously during T matrix model increment iterative in formula (13), in order to reduce calculating finding the inverse matrix Complexity, take following more new strategy.For an arbitrary sample xl∈R∪E∪{xuBecome standard supporting vector, t The T of+1 renewal processt+1Are as follows:
As standard supporting vector xlWhen leaving set S, Tt+1Are as follows:
(6) increment sample is continually introduced, dynamically updates the increment SVDD model after t times out according to step 4), step 5) Γt.And for different faults state sample, calculate the increment SVDD model Γ of different faults stateth；
(7) to on-line testing sample setData normalization and power feature extraction are carried out, it is calculated and corresponds to h failure shape The relative distance γ of states modelh(xhi), it is based on relative distance minimum principle, detection and identification goes out the corresponding malfunction class of sample Not, detailed step are as follows:
Diagnostic method based on relative distance can be by divided by the suprasphere radius in each failure state model, Lai Jiaqiang The fault condition detection rate of decision rule raising algorithm.Relative distance not only can detect that failure, moreover it is possible to judge out of order tight Weight degree.Use relative distance as criterion:
Increment SVDD and SVDD most essential difference are structure inheritance, so the training time of increment SVDD can be than passing The training time of system SVDD is short, as shown in Figure 3.As can be seen from the results, as sample size is constantly increasing, SVDD model training Time and the gap of increment SVDD are increasing.This is because tradition SVDD algorithm must be by last training sample and newly-increased Sample put together re -training solve double optimization problem, cause historical sample repeatedly to be trained, while to the sample being just added This is without rationally being screened；Unlike batch type SVDD, the inheritability of structure creates increment SVDD and can use The advantages of last training result carrys out more new model, and increment SVDD selectively carries out study instruction to increment sample Practice, it is possible to substantially reduce the training time.
The method of the present invention is compared with SVDD method, the online early detection rate of increment SVDD, testing result such as 1 institute of table Show.SVDD method is compared with the testing result of increment SVDD, it is known that quantitation algorithm and non incrementalalgorithm are examined in failure Difference is unobvious in survey rate.Increment type SVDD is smaller than the suprasphere space of non-increment type SVDD, and training data dimension and Number of samples is more, it is possible thereby to illustrate that increment SVDD can selectively retain the sample that those most possibly become supporting vector This, to shorten the training time while not reducing fault detection rate.That is, delta algorithm does not sacrifice classification Ability carrys out the training speed of lift scheme, illustrates that delta algorithm is more adaptive to system dynamic change problem.Table 1 and Fig. 4, Fig. 5, Result in Fig. 6 also shows the algorithm fault detection rate without power feature extraction than there is the event of the algorithm of power feature extraction Barrier verification and measurement ratio wants low, it was demonstrated that is based onThe Feature Dimension Reduction technology of information extraction can effectively reject unrelated noise information and Failure variation characteristic is extracted, increases the classification performance of model, to instruct actual production process to a certain extent.
Verification and measurement ratio of 1 distinct methods of table in initial failure state
Content described in this specification embodiment is only enumerating to the way of realization of inventive concept, protection of the invention Range should not be construed as being limited to the specific forms stated in the embodiments, and protection scope of the present invention is also and in art technology Personnel conceive according to the present invention it is conceivable that equivalent technologies mean.

Claims (1)

1. one kind is based onInformation and the online incipient fault detection method of increment SVDD, comprising the following steps:
1) collect process measurand withSample is imitated, and it is standardized, is obtainedAnd Y0；Sample is carried out Data prediction is pre-processed using data normalization, and specific implementation step is as follows:
For training sample data x1,x2,…xN, sample xiStandardization calculation formula are as follows:
Wherein, xi、Sample after respectively indicating i-th of original offline sample and standardization, θ are the arithmetic of all sample datas Average value, σ are the variance of all samples；
2) it calculates in step 1)And Y0Between different measurands withThe association relationship of effectAccording to accumulative mutual Information contribution rate principle filter out withImitate maximally related one group of energy feature sample set X0, establish the detailed of energy feature sample Step are as follows:
In order to reduce influence of the sample size to Mutual Information Estimation, the association relationship between two variables is calculated using k-nearest neighbor:
Wherein, N is the number of total sample, and k is neighbour's number, sxAnd syIt is expressed as in the subspace of X and Y close to arest neighbors Number of samples, φ () be digamma function.Simultaneously to ensure that mutual information can efficiently extract energy information, can make to choose G measurand accumulation mutual information contribution degree are as follows:
G feature is included before CPMI is illustratedEffect information accounts for whole systemsThe ratio for imitating information, is determined to sieve with it The Characteristic Number selected；Since the mutual information of each feature is greater than 0, then CPMI is in the Characteristic Number value model filtered out Enclose interior monotonic increase；In order to reach relatively good Feature Dimension Reduction effect, need so that CPMI is limited greater than defined control；
3) the energy feature sample set X obtained by step 2)0, and construct initial SVDD model Γ0, establish the detailed step of model Are as follows:
Give a training set [x1,x2,…,xN], wherein N is sample number；A and R respectively indicates hyperspherical center and radius. By structural risk minimization it is found that this suprasphere radius minimization problem can be described as it is following with inequality constraints Quadratic programming problem, while introducing slack variable ξiAnd penalty factor:
For the quadratic programming problem of above-mentioned with constraint conditions, Lagrange multiplier can be introduced, which is converted After its dual form, be often more easier to solve, and kernel function can be introduced, by luv space project to higher dimensional space to Solve nonlinear problem:
By solving for quadratic programming optimization algorithm SMO algorithm (Sequential Minimal Optimization, SMO) To optimal solution αi, to obtain the model parameter Γ of SVDD0；
4) increment sample is introducedAnd Y1Afterwards, according to the power feature extraction rule based on variable forgetting factor come adaptive updates The corresponding association relationship of increment sampleThe energy feature set of increment sample is filtered out according to Feature Dimension Reduction principleThe step of adaptive energy feature extraction are as follows:
After the corresponding association relationship of newly-increased data sample all adds forgetting factor ρ, for the t+1 times newly-increased measurement sampleWith it is correspondingImitate data sample Yt+1, association relationship between the two are as follows:
Wherein ρ ∈ (0,1],The column vector that j-th of measurand feature is constituted in t+1 newly-increased samples is represented, m is referred to Measured variable number,H(Yt+1)、Respectively indicate the comentropy and connection between the two of variable Close entropy；
The size of current forgetting factor can adaptively be adjusted according to the Parameters variation of time-varying system by becoming forgetting factor method, for the The variable forgetting factor ρ of k+1 newly-increased sample association relationship are as follows:
CPMI in formulat+1Refer to the accumulative mutual information contribution rate of the t+1 times newly-increased data character pair sample, gt+1The selection of value is It is determined according to the accumulative mutual information contribution rate principle of current sample；Finally, according to the CPMI of current sample sett+1Screening New energy feature set out
5) according to the initial SVDD model Γ of step 3)0With the energy feature sample of step 4)Update obtains increment SVDD mould Type Γ1, the specific steps of incremental update are as follows:
5-1) statistic and statistics limit；
Given sample set X=[x1,x2,…,xN], the dual form of SVDD suprasphere radius optimization problem are as follows:
Whereinδ is the Optimization Compensation factor, and kernel function can be denoted as Kij=K (xi,xj)；By KKT Condition it is found that W to there is optimal solution, to αi, the first derivative of δ need to meet following condition:
Wherein Ω (xi) be SVDD sample discriminant function；
By formula (10) it is found that the KKT condition of SVDD is usually divided into three classes training sample set: non-supporting vector R, standard branch in ball Hold vector S and boundary supporting vector E；For the new samples x being just addedu, need to change model parameter to make the sample set of extension Reach KKT condition again, so that new optimal solution is obtained, model coefficient variation are as follows:
Wherein αuIt is the new samples x being just addeduCorresponding Lagrange multiplier, Δ αuFor the change of new samples Lagrange multiplier Change amount, Δ αjOriginal sample set Lagrange multiplier variable quantity after being added for new samples.The variation of standard supporting vector model coefficient It measures writeable are as follows:
Enable T=K-1, for the sample in E, R set, κi≡ 0, from formula (13):
It enablesIn formula,For model boundary Sensitive factor, for S set in sample,It carries it into formula (12), then:
5-2) sample attribute migrates；
Sample attribute migration is by increment sample pattern index variation amount Δ αuThe variation for adjusting original sample pattern coefficient, makes to expand Exhibition sample set meets KKT condition again, to tend to the process of a new equilibrium state；In increment sample attribute transition process, Its attribute is all satisfied Δ αu> 0, sample attribute migration situation are divided into following six kinds:
①κsAs the corresponding property value set of standard supporting vector collectionIn s-th of number, according to formula (14) Δ α can be calculatedsIf it meets upper limit Δ αs≤C-αs, then Δ αuAnd κsJack per line, xsSide is become from standard supporting vector Boundary's supporting vector:
In formula, λ is migration factor；
2. if Δ αsIn lower limit Δ αs≥-αs, then Δ αuAnd κsContrary sign, xsFrom standard supporting vector become in ball it is non-supporting to Amount:
3. for vector x non-supporting in ballr, then have dr> 0,R-th yuan as vector sensitive factor collection non-supporting in ball Δ d can be calculated according to formula (16) in elementr；If Δ dr< 0, non-supporting vector x in ballrIt can become standard supporting vector:
4. for boundary supporting vector xe, then have dr< 0, e-th of element of non-supporting vector sensitive factor collection is represented by ballIt can then be calculatedIf Δ de> 0, boundary supporting vector xeIt can become standard supporting vector:
5. usually setting increment sample xuInitial model sample coefficient be zero, work as duSample x is then thought when >=0uFor mesh in ball Class sample is marked, then its model sample coefficient is without updating；Work as du< 0, increment sample xuThen become standard supporting vector:
6. during increment SVDD model training, αuUpper dividing value is penalty factor, works as αuWhen≤C, increment sample αuIt will become Boundary supporting vector, increment coefficient variable quantity are as follows:
λma=C- αu (22)
In increment SVDD, Δ α is enabledmax=min { λsp,λsm,λrs,λes,λc,λmaIt is used as increment sample αuModel coefficient variation Amount, formula (14) can obtain archetype sample coefficient variation delta αi；
5-3) T matrix update；
It also needs to update simultaneously during T matrix model increment iterative in formula (13), calculates answering for finding the inverse matrix to reduce Miscellaneous degree takes following more new strategy；For an arbitrary sample xl∈R∪E∪{xuBecome standard supporting vector, t+1 times The T of renewal processt+1Are as follows:
As standard supporting vector xlWhen leaving set S, Tt+1Are as follows:
6) increment sample is continually introduced, t times out increment SVDD model Γ is dynamically updated according to step 4), step 5)t；And it is right In different faults state sample, the increment SVDD model Γ of different faults state is calculatedth；
7) to on-line testing sample setData normalization and power feature extraction are carried out, it is calculated and corresponds to h failure state model Relative distance γh(xhi), it is based on relative distance minimum principle, detection and identification goes out the corresponding malfunction classification of sample, in detail Thin step are as follows:
Diagnostic method based on relative distance can be by divided by the suprasphere radius in each failure state model, to reinforce determining Rule improves the fault condition detection rate of algorithm；Relative distance not only can detect that failure, moreover it is possible to judge out of order serious journey Degree.Use relative distance as criterion:
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112184037A (en) * 2020-09-30 2021-01-05 华中科技大学 Multi-modal process fault detection method based on weighted SVDD
CN112733872A (en) * 2020-08-26 2021-04-30 南京航空航天大学 Aeroengine fault detection method based on dynamic radius support vector data description
CN113237920A (en) * 2021-05-17 2021-08-10 西南交通大学 Method for detecting fault heat source of valve-side sleeve of extra-high voltage converter transformer
CN112733872B (en) * 2020-08-26 2024-05-03 南京航空航天大学 Aeroengine fault detection method based on dynamic radius support vector data description

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105868783A (en) * 2016-03-31 2016-08-17 华东理工大学 Reduction type support vector data description method based on information entropy
CN106124988A (en) * 2016-06-28 2016-11-16 江苏科技大学 A kind of motor multi-state fault detection method based on RBF, multilamellar FDA and SVDD
US20190042977A1 (en) * 2017-08-07 2019-02-07 Sas Institute Inc. Bandwidth selection in support vector data description for outlier identification
US20190095400A1 (en) * 2017-09-28 2019-03-28 Sas Institute Inc. Analytic system to incrementally update a support vector data description for outlier identification

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105868783A (en) * 2016-03-31 2016-08-17 华东理工大学 Reduction type support vector data description method based on information entropy
CN106124988A (en) * 2016-06-28 2016-11-16 江苏科技大学 A kind of motor multi-state fault detection method based on RBF, multilamellar FDA and SVDD
US20190042977A1 (en) * 2017-08-07 2019-02-07 Sas Institute Inc. Bandwidth selection in support vector data description for outlier identification
US20190095400A1 (en) * 2017-09-28 2019-03-28 Sas Institute Inc. Analytic system to incrementally update a support vector data description for outlier identification

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
MADAKYARU M 等: "Improved data-based fault detection strategy and application to distillation columns", 《PROCESS SAFETY AND ENVIRONMENTAL PROTECTION》 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112733872A (en) * 2020-08-26 2021-04-30 南京航空航天大学 Aeroengine fault detection method based on dynamic radius support vector data description
CN112733872B (en) * 2020-08-26 2024-05-03 南京航空航天大学 Aeroengine fault detection method based on dynamic radius support vector data description
CN112184037A (en) * 2020-09-30 2021-01-05 华中科技大学 Multi-modal process fault detection method based on weighted SVDD
CN112184037B (en) * 2020-09-30 2022-11-11 华中科技大学 Multi-modal process fault detection method based on weighted SVDD
CN113237920A (en) * 2021-05-17 2021-08-10 西南交通大学 Method for detecting fault heat source of valve-side sleeve of extra-high voltage converter transformer
CN113237920B (en) * 2021-05-17 2022-04-22 西南交通大学 Method for detecting fault heat source of valve-side sleeve of extra-high voltage converter transformer

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