CN106092625B - The industrial process fault detection method merged based on amendment type independent component analysis and Bayesian probability - Google Patents
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- 238000012544 monitoring process Methods 0.000 claims abstract description 18
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Abstract
The present invention relates to a kind of industrial process fault detection methods merged based on amendment type independent component analysis and Bayesian probability.Traditional fault detection method based on amendment type independent component analysis needs the non-quadratic function of selection one, to measure non-Gaussian system size.However, different industrial process datas or object can cause to be difficult to obtain enough Heuristicses in practical application and go to instruct the selection of non-quadratic function.In this regard, the method for the present invention first against different non-quadratic functions, trains to obtain different amendment type independence meta-models using the normal data of industrial process；Then the result of decision of this multiple Fault Model is integrated by Bayesian probability fusion method, obtains final probabilistic type monitoring index.Compared with the conventional method, the present invention can solve the model uncertain problem because of caused by non-quadratic function diversity, fully considered multiple model possibilities, largely enhanced the reliability of Fault Model.
Description
Technical field
The invention belongs to industrial process control field, more particularly to one kind are general based on amendment type independent component analysis and Bayes
The industrial process fault detection method of rate fusion.
Background technology
More stringent requirements are proposed for production safety of the market competition being growing more intense to modern industry process, timely and reliable
Fault detection method have become component part indispensable in entire production system.Due to modern industry process complexity and greatly
Type trend inevitably will appear different types of failure in operational process.If fault warning can not be triggered timely, it is possible to
Cause operating accident, seriously affects the quality of product, or even cause loss difficult to the appraisal on life and property.Therefore, how
Relatively reliable and effective Fault Model is established, the failure of production process appearance is screened out in time, is all the time
One of the problem of industrial quarters and academia pay much attention to.
In existing fault detection method system, the fault detection method of data-driven has been developed as mainstream technology
Means.Wherein, using pivot analysis (PCA) and independent component analysis (ICA) method as representative, pass through what is hidden in extraction process data
Useful information describes the essence of process operation, and whether monitor production process operating status exception occurs on this basis.Phase
Than in PCA methods, ICA methods are no longer required for process data approximation Gaussian distributed, and the higher order statistical letter of data is utilized
Breath carrys out the ingredient that hides of extraction process data, more suitable for the modeling of processing non-gaussian industrial process data and fault detection problem.
However, traditional ICA methods deposit some inevitable problems when establishing Fault Model.Firstly, since initial value
It randomly generates, results in the uncertainty of established model.Secondly, it is used for the non-quadratic function of predictor non-Gaussian system degree
There are three types of optional forms, this will also result in the uncertainty of institute's established model.Although independent component analysis method (MICA) energy of amendment type
Preferably overcome first problem, still, the model uncertainty caused by the diversity of non-quadratic function selection can reduce
The accuracy and reliability of corresponding failure detection method.And wants to find and a kind of the non-quadratic function of various fault types is suitble to be not
It is possible.If the failure detection result of the MICA models corresponding to different non-quadratic functions can be merged, that is, consider institute
Some model possibilities, this will significantly promote the accuracy and reliability of MICA methods detection failure.
Invention content
In order to overcome the shortcomings of that existing method, the present invention provide a kind of based on amendment type independent component analysis and Bayesian probability
The industrial process fault detection method of fusion.Technical solution is used by the present invention solves above-mentioned technical problem：One kind is based on
The industrial process fault detection method of amendment type independent component analysis and Bayesian probability fusion, includes the following steps：
(1) sampled data under production process normal operating condition is collected, the training dataset of modeling is formed：X=
[x1, x1..., xn]T, wherein X ∈ Rn×m, n is number of training, and m is process measurement variable number, and R is set of real numbers, Rn×mIndicate n
The real number matrix of × m dimensions, upper label T representing matrix transposition.
(2) data set X is standardized, it is 0 to make the mean value of each measurand, and variance 1 obtains new number
According to matrix
(3) utilize PCA methods to data matrixIt carries out whitening processing and obtains data matrix Z ∈ Rn×M.Wherein, M≤m is
Variable number in matrix Z.
(4) it is directed to different non-quadratic functions, calls MICA iterative algorithms to establish different MICA Fault Models, always
Meter establishes three MICA models；
(5) corresponding T is constructed to training data2And Q statistical magnitude, and respectively obtain T using with density estimation (KDE) method2With
Q statistical magnitude control limit corresponding under the conditions of confidence alpha=99%；
(6) new process sampled data x is collectedmew∈Rm×1, and be standardized to obtain
(7) different MICA models are respectively adopted, fault detect is carried out to it, that is, establish statistic T2And Q, such 3
MICA models total provides 3 groups and corresponds to new samples dataMonitoring result；
(8) by Bayesian inference, by obtain 3 groups of monitoring results be merged in the form of probability to get to point
It Dui Yingyu not T2With the probabilistic type monitoring index of Q statistical magnitudeWith BIQ, and make about new dataWhether normally determine
Plan.
Compared with the conventional method, the advantage of the invention is that：The present invention is established accordingly respectively using each non-quadratic function
MICA models.Then, it introduces Bayesian inference method and integrated and probability fusion is carried out to the monitoring result under different models, with
Obtain last comprehensive monitoring index.Compared to existing method, the present invention can solve to cause because of non-quadratic function diversity
Model uncertain problem, increase the reliability and stability of Fault Model.Therefore, the present invention can be largely
Upper reduction MICA methods enhance the reliability of Fault Model, to be more conducive to industry to the dependence of priori
The implementation of automation.
Description of the drawings
Fig. 1 is the implementing procedure figure of method involved in the present invention.
Specific implementation method
The present invention will be described in detail in the following with reference to the drawings and specific embodiments.
As shown in Figure 1, the invention discloses a kind of industry merged based on amendment type independent component analysis and Bayesian probability
Procedure failure testing method.For industrial process fault detection problem, production process is being collected just first with data collecting system
Data set under normal operating status, and it is standardized.Secondly, corresponding MICA is established for different non-quadratic functions
Fault Model, and model parameter is preserved with spare.Then, three groups of different monitorings are calculated to new sampled data to unite
Measure T2And Q, and different monitoring results are integrated into probability level using Bayesian probability fusion method.Finally, according to probability
Monitoring indexWith BIQWhether occurrence, which transfinites, is made the whether normal decision of new data.Specific implementation step of the present invention is such as
Under：
The first step：The sampled data under production process normal operating condition is collected, the training dataset of modeling is formed：X
=[x1, x1..., xn]T。
Second step：Data set X is standardized, it is 0 to make the mean value of each measurand, variance 1, is obtained new
Data matrix
Third walks：Using PCA methods to data matrixIt carries out whitening processing and obtains data matrix Z ∈ Rn×M, concrete operations
Shown in steps are as follows：
(1) it calculatesCovariance matrixWherein S ∈ Rm×m；
(2) all characteristic values and feature vector of calculating matrix S, and reject less than 0.0001 characteristic value and its corresponding
Feature vector obtains eigenvectors matrix P=[p1, p2..., pM]∈Rm×MAnd characteristic value diagonal matrix D=diag (λ1,
λ2..., λM)∈RM×M；
(3) rightWhitening processing is carried out, is obtained
4th step：K-th of non-quadratic function G is selected from three kinds of non-quadratic functionskAfterwards, MICA iterative algorithms are called to seek
With GkCorresponding MICA Fault Models repeat this operation until establishing three MICA models, wherein k=1,2,3 difference
For the label of three kinds of different non-quadratic functions.
The step of calling MICA iterative algorithms to establish Fault Model is as follows：
(1) setting needs the independent entry number d extracted and selected non-quadratic function Gk, k=1,2,3, wherein non-
Quadratic function has following three kinds of optional forms：
G1(u)=log cosh (u), G2(u)=exp (- u2/ 2), G3(u)=u4 (1)
Wherein, u is function GkIndependent variable.
(2) when extracting i-th (i=1,2 ..., d) a independent entry, choose M × M dimension unit matrix in i-th row as to
Measure ciInitial value；
(3) renewal vector c according to the following formulai：
ci←E{Zg(ci TZ)}-E{g′(ci TZ)}ci (2)
Wherein, g and g ' is the single order and second dervative of function G respectively, and desired value is sought in E { } expressions；
(4) to updated vector ciCarry out orthogonal standardization according to the following formula successively：
ci←ci/||ci|| (4)
(5) step (3)~(4) are repeated until vector ciConvergence, and preserve vectorial ci；
(6) i=i+1 is set, and (2)~(5) that repeat the above steps are until obtain all d vector C=[c1, c2..., cd]
∈RM×d。
(7) it calculates and corresponds to non-quadratic function GkMICA models hybrid matrix Ak∈Rm×dWith separation matrix Wk∈Rd ×m, as follows：
Ak=PD1/2C (5)
Wk=CTD-1/2PT (6)
5th step：Corresponding T is constructed to training data2And Q statistical magnitude, and obtained respectively using with density estimation (KDE) method
To T2Control corresponding under the conditions of confidence alpha=99% is 99% limit with Q statistical magnitudeAnd Qlim；
6th step：Collect new process sampled data xnew∈Rm×1, and be standardized to obtain
7th step：Different MICA models are respectively adopted, fault detect is carried out to it, i.e., establishes statistics respectively according to the following formula
AmountAnd Qk, such 3 MICA models, which amount to, provides 3 groups corresponding to new samples dataMonitoring result；
Wherein, | | | | it indicates to calculate the 2- norms of vector.
8th step：By Bayesian inference, by obtain 3 groups of monitoring results be merged in the form of probability to get
To corresponding respectively to T2With the probabilistic type monitoring index of Q statistical magnitudeWith BIQ, and make about new dataIt is whether normal
Decision.Specific implementation detail is further described as follows：
First, Bayesian probability fusion is carried out：
(A) to three groups of T2Statistic is merged：
(1) new data is calculated according to the following formulaBelong to the probability of failure：
Wherein, probabilityCalculation it is as follows：
Wherein, N and F indicates normal and fault condition, prior probability respectivelyWithValue α and 1- α respectively,
Conditional probabilityWithCalculation it is as follows：
(2) final probabilistic type index is calculated by following formula
(B) three groups of Q statistical magnitudes are merged
It is directed to Q statistical magnitude, calculates new data firstThen probability, the conditional probability etc. for belonging to failure obtain final
Probabilistic type index BIQ, T is merged with above-mentioned2The mode of statistic is identical.
Secondly, failure decision is carried out：
By what is be calculatedWith BIQThe concrete numerical value of index controls limit 1- α with probability and is compared.If any one
Index value is more than 1- α, then decision new dataFor fault sample；Conversely, the dataFor normal sample, and then to next
A new obtained data that sample continue fault detect.
With reference to the example of a specific industrial process come illustrate the present invention relative to existing method superiority with
Reliability.The process data comes from the experiment of the U.S. Tennessee-Yi Siman (TE) chemical process, and prototype is Yi Siman Chemical Manufactures
One actual process flow in workshop.Currently, complexity of the TE processes because of its flow, has been used as a standard test platform wide
It is general to be studied for fault detect.Entire TE processes include that 22 measurands, 12 performance variables and 19 composition measurements become
Amount.The data acquired are divided into 22 groups, including the data set and 21 groups of fault datas under 1 group of nominal situation.And at these
In fault data, 16 are known fault types, such as the changing of cooling water inlet temperature or feed constituents, valve viscous, anti-
Dynamics drift etc. is answered, also 5 fault types are unknown.In order to be monitored to the process, as shown in Table 1 33 are chosen
Next a process variable is explained in detail specific implementation step of the present invention in conjunction with the TE processes.
1. acquiring the process data under nominal situation, while fault data different in 21 is acquired, and chooses 960 normally
Data form matrix X ∈ R960×33, it is standardized.
2. using PCA methods to the data after standardizationIt carries out whitening processing and obtains Z ∈ R960×31。
3. the independent entry number d=9 retained is arranged, confidence alpha=99% selects different non-quadratic functions to carry out MICA moulds
Type is established：
1) non-quadratic function G is selected1MICA modelings are carried out, corresponding hybrid matrix A is obtained1∈R33×9With separation matrix W1
∈R9×33.Then T is constructed2And Q statistical magnitude, it is used in combination KDE methods to determine its corresponding control limitAnd Q1, lim；
2) non-quadratic function G is selected2MICA modelings are carried out, corresponding hybrid matrix A is obtained2∈R33×9With separation matrix W2
∈R9×33.Then T is constructed2And Q statistical magnitude, it is used in combination KDE methods to determine its corresponding control limitAnd Q2, lim；
3) non-quadratic function G is selected3MICA modelings are carried out, corresponding hybrid matrix A is obtained3∈R33×9With separation matrix W3
∈R9×33.Then T is constructed2And Q statistical magnitude, it is used in combination KDE methods to determine its corresponding control limitAnd Q3, lim。
4. obtaining current new sampled data, and it is standardized to obtain
In order to verify superiority of the present invention relative to existing method, chooses 21 kinds of failures and tested, equally to sampling
Data are standardized.
5. implementing on-line monitoring process
1) new data is monitored using three kinds of different MICA Fault Models, constructs the statistics under corresponding model
AmountAnd Qk；
2) probabilistic type monitoring index is obtained using Bayesian probability fusion calculationWith BIQ, and by occurrence and probability control
System limit 1- α are compared, and whether decision current data is normal.
Table 1：TE process monitoring variables.
| Serial number | Variable description | Serial number | Variable description | Serial number | Variable description |
| 1 | Material A flow | 12 | Separator liquid level | 23 | D material inlet valves position |
| 2 | Material D flows | 13 | Separator pressure | 24 | E material inlet valves position |
| 3 | Material E flows | 14 | Separator bottom of tower flow | 25 | A material inlet valves position |
| 4 | Combined feed flow | 15 | Stripper grade | 26 | A and C material inlet valves position |
| 5 | Circular flow | 16 | Pressure of stripping tower | 27 | Compressor cycle valve location |
| 6 | Reactor feed | 17 | Stripper bottom rate | 28 | Empty valve location |
| 7 | Reactor pressure | 18 | Stripper temperature | 29 | Separator liquid phase valve location |
| 8 | Reactor grade | 19 | Stripper upper steam | 30 | Stripper liquid phase valve location |
| 9 | Temperature of reactor | 20 | Compressor horsepower | 31 | Stripper steam valve position |
| 10 | Rate of evacuation | 21 | Reactor cooling water outlet temperature | 32 | Reactor condensate flow |
| 11 | Separator temperature | 22 | Separator cooling water outlet temperature | 33 | Condenser cooling water flow |
It chooses 18 typical faults (removing failure 3, failure 9 and failure 15) to be monitored, by mean failure rate verification and measurement ratio pair
Than the results are shown in Table 2.As a result it shows that the method for the present invention has been obviously improved fault detect rate, improves fault detect effect.
Table 2：Mean failure rate verification and measurement ratio of the distinct methods to fault type in TE processes 18.
Above-mentioned case study on implementation only is used for illustrating the specific implementation of the present invention, rather than limits the invention.
In the protection domain of spirit and claims of the present invention, to any modification that the present invention makes, the protection of the present invention is both fallen within
Range.
Claims (2)
1. a kind of industrial process fault detection method merged based on amendment type independent component analysis and Bayesian probability, feature are existed
In：It the described method comprises the following steps：
(1) sampled data under production process normal operating condition is collected, the training dataset of modeling is formed：X=[x1,
x1..., xn]T, wherein X ∈ Rn×m, n is number of training, and m is process measurement variable number, and R is set of real numbers, Rn×mIndicate n × m
The real number matrix of dimension, upper label T representing matrix transposition；
(2) data set X is standardized, it is 0 to make the mean value of each measurand, and variance 1 obtains new data square
Battle array
(3) utilize PCA methods to data matrixIt carries out whitening processing and obtains data matrix Z ∈ Rn×M, wherein M≤m is matrix Z
Middle variable number, specific implementation are as follows：
1. calculatingCovariance matrixWherein S ∈ Rm×m；
2. all characteristic values and feature vector of calculating matrix S, and reject the characteristic value less than 0.0001 and its corresponding feature
Vector obtains eigenvectors matrix P=[p1, p2..., pM]∈Rm×MAnd characteristic value diagonal matrix D=diag (λ1, λ2...,
λM)∈RM×M；
3. rightWhitening processing is carried out, is obtained
(4) k-th of non-quadratic function G is selected from following three kinds optional non-quadratic functionsk：
G1(u)=logcosh (u), G2(u)=exp (- u2/ 2), G3(u)=u4
Wherein, k=1,2,3, u be the independent variable of function G, for non-quadratic function Gk, MICA iterative algorithms is called to establish failure inspection
Model is surveyed, specific implementation mode is as follows：
1. setting needs the independent entry number d extracted and selected non-quadratic function Gk；
2. when extracting i-th (i=1,2 ..., d) a independent entry, the i-th row in M × M dimension unit matrixs are chosen as vectorial ci's
Initial value；
3. according to formula ci←E{Zg(ci TZ)}-E{g′(ci TZ)}ciRenewal vector ci,
Wherein, g and g ' is function G respectivelykSingle order and second dervative, E { } expression seek desired value；
4. to updated vector ciCarry out orthogonal standardization according to the following formula successively：
ci←ci/||ci|| (2)
5. repeat step 3.~4. until vector ciConvergence, and preserve vectorial ci；
6. i=i+1 is set, repeat the above steps 2.~5. until obtain all d vector C=[c1, c2..., cd]∈RM×d；
Correspond to non-quadratic function G 7. calculatingkMICA models hybrid matrix Ak∈Rm×dWith separation matrix Wk∈Rd×m, as follows：
Ak=PD1/2C (3)
Wk=CTD-1/2PT (4)
And preservation matrix AkWith WkWith spare；
8. repeat step 1.~7. until establish three MICA models；
(5) corresponding T is constructed to training data2And Q statistical magnitude, and respectively obtain T using density estimation KDE methods2And Q statistical magnitude
Corresponding control limit under the conditions of confidence alpha=99%；
(6) new process sampled data x is collectednew∈Rm×1, and be standardized to obtain
(7) different MICA models are respectively adopted and carry out fault detect to it, that is, construct statistic as followsAnd Qk, this
3 MICA models totals of sample provide 3 groups and correspond to new samples dataMonitoring result：
Wherein, | | | | it indicates to calculate the length of vector；
(8) by Bayesian inference, obtain 3 groups of monitoring results are merged in the form of probability to get to probabilistic type
Monitoring indexWith BIQ, and make about new dataWhether normal decision.
2. the industrial process fault detect merged according to claim 1 based on amendment type independent component analysis and Bayesian probability
Method, which is characterized in that the step (8) is specially：
First, Bayesian probability fusion is carried out：
(A) to three groups of T2Statistic is merged：
1. calculating new data according to the following formulaBelong to the probability of failure：
Wherein, probabilityCalculation it is as follows：
Wherein, N and F indicates normal and fault condition, prior probability respectivelyWithValue α and 1- α respectively, condition
ProbabilityWithCalculation it is as follows：
2. final probabilistic type index is calculated by following formula
(B) three groups of Q statistical magnitudes are merged, specific implementation process is as follows：
1. calculating new data according to the following formulaBelong to the probability of failure：
Wherein, probabilityCalculation it is as follows：
Wherein, N and F indicates normal and fault condition, prior probability respectivelyWithValue α and 1- α respectively, condition
ProbabilityWithCalculation it is as follows：
2. final probabilistic type index BI is calculated by following formulaQ：
Secondly, failure decision is carried out, by what is be calculatedWith BIQThe concrete numerical value of index and probability control limit 1- α are carried out pair
Than if any one index value is more than 1- α, decision new dataFor fault sample；Conversely, the dataFor normal sample
This, and then fault detect is continued to next new obtained data that sample.
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| CN107357275B (en) * | 2017-07-27 | 2019-08-27 | 中南大学 | Non-gaussian industrial process fault detection method and system |
| CN108227649A (en) * | 2017-12-13 | 2018-06-29 | 华东理工大学 | The method of industrial p xylene oxidation reaction member monitoring running state |
| CN108375965B (en) * | 2018-03-19 | 2020-06-30 | 宁波大学 | non-Gaussian process monitoring method based on multi-variable block cross correlation elimination |
| CN109829468B (en) * | 2018-04-16 | 2021-09-03 | 南京航空航天大学 | Bayesian network-based civil aircraft complex system fault diagnosis method |
| CN108758969B (en) * | 2018-06-14 | 2020-09-15 | 河南科技大学 | Fault detection method and system for water chilling unit |
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