CN102110187A - Method and system for diagnosing mixed failure based on PCA and artificial immune system - Google Patents

Abstract

The invention provides a method and a system for diagnosing mixed failure based on a PCA and an artificial immune system, and the method and the system can be applied in industrial processes as chemical industry, oil refining, biopharmaceutics, and the like. The PCA algorithm is used for detecting failure, the artificial immune system is used for diagnose failure kinds of self and non-self judgments, and self-adaptation and self-study capabilities of the artificial immune system are used, so that the updating of a failure diagnosis system during an online operation process is realized. Antigens and the antibodies of the artificial immune system are represented as a matrix composed of data samples of time series, wherein data to be detected is generated as antigens, all types of historical data are generated as different antibodies, the kind of working condition is judged by computing the difference between the antigen and the antibody, so as to realize failure detection and diagnosis. A clone and variation computation is also presented, so as to produce a large amount of different antibodies from the known working condition data during the failure diagnosis process, and the antibodies are automatically updated during the diagnosis, so that the requirements of actual industrial process on adaptability are met.

Description

A kind of process industry mixed fault diagnostic method and system based on PCA and artificial immune system

Invention field

The present invention relates to a kind of mixed fault diagnostic method and system based on PCA and artificial immune system, it is mainly used in chemical process, oil refining process, the fault diagnosis field of process industries such as bio-pharmaceuticals process.

Background technology

Along with science and technology development, in modern industry was produced, the complexity of commercial unit system improved day by day, for continuous large batch of modern production process, sets up supervisory system, in time finds and diagnose the reason that is out of order, and was very necessary.Fault diagnosis is the very practical technology that many science are intersected as a comprehensive branch of learning.Effectively, fault diagnosis not only can guarantee the normal operation of producing, the generation of prevention catastrophic failure timely, can also give works engineer to instruct, and in time repairs the position of breaking down, and the effect of directiveness is arranged for production.

Three phases has mainly been experienced in the development of fault diagnosis.First stage is according to sense organ, instrument, relies on operator's experience to be judged, this for simple and mechanical process still relatively effectively.Second stage is to utilize sensor technology and technique of dynamic measurement, and signal is carried out modeling analysis, and this has obtained using widely, and than the previous stage, the precision and the standardized degree in this stage are greatly improved.Three phases is just in the developing Intelligent Information Processing stage, and this also is along with technical development of computer, with bionical, the machine-processed a kind of new trial that combines of artificial intelligence.

Fault diagnosis has been passed through years of development, and theoretical method mainly contains three classes: based on the method for analytic model, based on the method for knowledge, based on the method for signal, typical method has neural network method, least square method, wavelet analysis method, principal component analysis (PCA) etc.Yet traditional method for diagnosing faults does not have large-scale application in the fault diagnosis of process industry, and practical application mainly also is confined to individual equipment, also has deficiency for the fault diagnosis of flow process itself.Main cause is that the actual industrial flow process has instability, can be along with the improvement of technology and the needs of production change, and traditional method for diagnosing faults scarcely possesses enough adaptability for the variation of flow process, is difficult to satisfy actual process industry needs.At this present situation, the artificial immune system that has the scholar to propose to have adaptivity and self-learning capability is incorporated into fault diagnosis field.

Artificial immune system is an a kind of comprehensive intelligent system, and it organically combines immunology and engineering science, utilizes technology such as mathematics, computing machine to set up the immunologic mechanism model, and is applied to the aspects such as design, enforcement of engineering.In recent years, the judgement for oneself and nonego is incorporated into fault diagnosis field by reference in the artificial immunity.Yet traditional based on all that system is the instantaneous variable of the fault diagnosis algorithm of artificial immune system as antigen and antibody, data are implemented continuous feature in the process industry that is beyond expression.In view of this, seek a kind of new antigen and antibody expression method and relevant a series of artificial immune systems thereof and have crucial meaning for the fault diagnosis of process industry.

Summary of the invention

The objective of the invention is to overcome existing method applicability and self-learning capability defect of insufficient, artificial immune system is applied to the fault diagnosis of process industry.Be different from traditional artificial immune system, the present invention proposes the expression way of new antigen and antibody and the method for diversity factor calculating and clonal propagation, and proposed a kind of mixed fault diagnostic method based on PCA and artificial immunity.

According to an aspect of the present invention, provide a kind of mixed fault diagnostic method, it is characterized in that comprising based on PCA and artificial immune system:

The PCA modeling procedure generates pca model with original normal sample,

Antibody generates step, generates antibody by the primary fault sample,

Failure detection steps is carried out fault detect with described pca model.

According to a further aspect of the present invention, above-mentioned mixed fault diagnostic method based on PCA and artificial immune system further comprises:

Antigen generation step is judged under the situation that the fault existence is arranged in described failure detection steps, with the real time data generation antigen of schedule time length before the detection constantly of described failure detection steps,

Troubleshooting step utilizes described antigen and described antibody, judges the kind of fault.

According to a further aspect of the present invention, above-mentioned mixed fault diagnostic method based on PCA and artificial immune system further comprises:

Data acquisition and treatment step are gathered required flow process variable data, and described flow process variable data comprises historical data and described real time data, and described historical data comprises described original normal sample and described primary fault sample.

According to a further aspect of the present invention, above-mentioned mixed fault diagnostic method based on PCA and artificial immune system further comprises:

Described antigen is carried out clonal vaviation, thereby generates new antibody,

Upgrade antibody in the described antibody library with described new antibody,

Determine the described threshold value of described new antibody.

According to another aspect of the present invention, provide a kind of mixed fault diagnostic system, it is characterized in that comprising based on PCA and artificial immune system:

The PCA model building device is used for generating pca model with original normal sample,

The antibody generating apparatus is used for generating antibody by the primary fault sample,

Failure detector is used for carrying out fault detect with described pca model.

According to a further aspect of the present invention, above-mentioned mixed fault diagnostic system based on PCA and artificial immune system further comprises:

The antigen generating apparatus is used for judging under the situation that the fault existence is arranged in described failure detection steps, with the real time data generation antigen of schedule time length before the detection constantly of described failure detection steps,

Trouble-shooter is used to utilize described antigen and described antibody, judges the kind of fault.

According to a further aspect of the present invention, above-mentioned mixed fault diagnostic system based on PCA and artificial immune system further comprises:

Data acquisition and treating apparatus are used to gather required flow process variable data, and described flow process variable data comprises historical data and described real time data, and described historical data comprises described original normal sample and described primary fault sample.

According to a further aspect of the present invention, above-mentioned mixed fault diagnostic system based on PCA and artificial immune system further comprises:

Thereby described antigen is carried out the device that clonal vaviation generates new antibody,

Upgrade the device of the antibody in the described antibody library with described new antibody,

Determine the device of the described threshold value of described new antibody.

Description of drawings

Fig. 1 has shown the process flow diagram of method according to an embodiment of the invention.

Fig. 2 has shown the formation of the antibody that method according to an embodiment of the invention adopted (antigen).

Fig. 3 has shown the antibody that method according to an embodiment of the invention adopted (antigen) diversity factor calculating synoptic diagram.

Fig. 4 has shown the synoptic diagram when method according to an embodiment of the invention is used to epoxypropane hydration reaction flow process.

Embodiment

The objective of the invention is to overcome existing method applicability and self-learning capability defect of insufficient, artificial immune system is applied to the fault diagnosis of process industry.Be different from traditional artificial immune system, the present invention proposes the expression way of new antigen and antibody and the method for diversity factor calculating and clonal propagation, and proposed a kind of mixed fault diagnostic method based on PCA and artificial immunity.

At the fault diagnosis of process industry, as shown in Figure 1, method according to an embodiment of the invention comprises:

-data acquisition and processing

Mixed fault diagnostic system according to an embodiment of the invention can directly be connected with production equipment, at step S101, by such as PLC (Programmable Logic Controller), DCS (Distributed Control System (DCS)) and LIMS (Laboratory Information Management System) etc. gather required flow process variable data.Wherein known historical data is used for system initialization, sets up pca model and produces original antibody, and real-time online data then is used for fault detection and diagnosis.All data were carried out normalized by formula (1) before initialization or fault diagnosis:

$X=0.5+\frac{x-\stackrel{\‾}{X}}{X_{\max }-X_{\min }}---\left(1\right)$

Wherein x is a real data, X, X _MaxWith X _MinThe mean value of this variable data, maximal value and minimum value when being respectively nominal situation.

In according to one embodiment of present invention, data acquisition and treatment step can be used as implements the preposition step that method of the present invention is carried out before.

-pca model is set up

Production data obtains original normal sample after the normalization of step S102, enter the principle modeling that step S103 utilizes PCA (principal component analysis (PCA)).The sample data note is made X ∈ R ^{N * m}(N is that specimen sample is counted, and m is the measurand number) calculated the covariance matrix R of X by formula (2).

$R=\frac{1}{n-1}{X}^{T}X---\left(2\right)$

R is carried out svd, the result as the formula (3):

$R=\left[P\tilde{P}\right]\mathrm{\Λ}{\left[P\tilde{P}\right]}^T---\left(3\right)$

Λ=diag (λ wherein ₁, λ ₂... λ _m), λ ₁〉=λ ₂〉=... 〉=λ _mBe the eigenwert of R,

Be the matrix that the characteristic of correspondence vector is formed, P ∈ R ^{M * k}Be loading matrix, $\tilde{P}\∈R^{m\×(m-k)}$ Loading matrix for residual error.K is the pivot number of model, is determined by formula (4).

$\frac{\underset{i=1}{\overset{k}{\mathrm{\Σ}}}{\mathrm{\λ}}_i}{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{\mathrm{\λ}}_i}\≤85\%---\left(4\right)$

Obtain pivot score matrix T ∈ R respectively by formula (5) (6) again ^{N * k}Be that loading matrix and residual error get sub matrix $\tilde{T}\∈R^{N\×(m-k)}:$

T＝XP (5)

$\tilde{T}=X\tilde{P}---\left(6\right)$

Having obtained principal component model like this is

$X=T{P}^T+\tilde{T}{\tilde{P}}^T---\left(7\right)$

Calculate T respectively by formula (5) (6) again ²The control limit δ of statistic and SPE statistic _TAnd δ _SPE, be used for fault detect

${\mathrm{\δ}}_T=\frac{k(m-1)}{m-k}{F}_{k,m-1,\mathrm{\α}}---\left(8\right)$

${\mathrm{\&delta;}}_{\mathrm{SPE}}={\mathrm{\&theta;}}_1{[\frac{C_{\mathrm{\&alpha;}}\sqrt{2{\mathrm{\&theta;}}_2{\mathrm{<figure-callout\; id="0"\; label="h"\; filenames="H2009102440664E00021.png"\; state="\{\{state\}\}">h</figure-callout>}}_0^2}}{{\mathrm{\&theta;}}_1}+1+\frac{{\mathrm{\&theta;}}_2{h}_0(h_0-1)}{{\mathrm{\&theta;}}_1^2}]}^{\frac{1}{h_0}}---\left(9\right)$

Wherein α is an insolation level, gets the decimal between 0.9 to 1.F _{K, m-1, α}Corresponding insolation level is α, and degree of freedom is k, the F distribution critical value under m-1 sample point. ${\mathrm{\&theta;}}_i=\underset{j=k+1}{\overset{m}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_j^i,$ ${\mathrm{<figure-callout\; id="0"\; label="h"\; filenames="H2009102440664E00021.png"\; state="\{\{state\}\}">h</figure-callout>}}_0=1-\frac{2{\mathrm{\&theta;}}_1{\mathrm{\&theta;}}_3}{3{\mathrm{\&theta;}}_2^2},$ C _αCorresponding normal distribution degree of confidence is the statistics fiducial limit of (1-α) %.

So just set up the pca model that can be used for fault detect.

-generation antibody library

For the primary fault sample that after S102 step normalization, obtains, at step S104, determine its failure mode and time, get the data that fault is introduced back set time length, generate first generation antibody, note is made Ab_fault=[Ab ₁, Ab ₂..., Ab _n].The matrix that these antibody all are made up of the seasonal effect in time series data sample, Ab _iBe the time series of each variable, as shown in Figure 2, v-1～v-5 is respectively the data of 5 different variablees of system.First generation antibody of the same type generates a large amount of second generation antibody through step S105 by formula (10) variation and clone, has constituted antibody library.

$X^{*}=\left\{\begin{array}{cc}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{a}_i{X}_i+b(X_c-X_d),& \underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{a}_i=1...n>1\\ (1+\frac{b}{2}){\mathrm{<figure-callout\; id="1"\; label="X"\; filenames="H2009102440664E00021.png,H2009102440664E00022.png"\; state="\{\{state\}\}">X</figure-callout>}}_1& ...n=1\end{array}\right.---\left(10\right)$

Wherein n is the number of known this kind first generation antibody, X _iBe known first generation antibody, X ^*Be the second generation antibody that generates through the variation clone.a _iBe the decimal at random in 0 to 1, b is the decimal at random between-1 to 1.C and d are the random integers between 1 to n.

Calculate the threshold value of every kind of fault type antibody again.

For two antibody (or antigen) of determining, can adopt the diversity factor of Dynamic Time Warping algorithm computation antigen and antibody.At first get the time series calculated difference degree of identical variable, following d _k(i j) represents antigen and antibody respectively at i, and j is the Euclidean distance of variable k constantly:

d _k(i，j)＝|Ab _k(j)-Ag _k(i)|j∈[1，n]，i∈[1，n] (13)

Construct a similarity matrix D and be used for describing between antigen and antibody track about the minimum difference degree of this variable.Iterative computation can be by d _k(i j) obtains D _k(i j), at first determines the iteration initial point.

D _k(1，1)＝d _k(1，l) (14)

Further iterative computation when j＜n:

$D_k(i,j^{*})=\min \left\{\begin{array}{cc}{D}_k(i-1,j)+d_k(i,j)......l\left(i\right)=j^{*}=j& \mathrm{if\; l}(i-1)\&NotEqual;l(i-2)\\ D_k(i-1,j)+d_k(i,j+1)...l\left(i\right)=j^{*}=j+1& \mathrm{if\; j}<n\\ D_k(i-1,j)+d_k(i,j+2)...l\left(i\right)=j^{*}=j+2& \mathrm{if\; j}<n-1\end{array}\right\}---\left(15\right)$

Stop iteration when j=n, the iterations note of this moment is m=i, has found a shortest calculating path of diversity factor, as shown in Figure 3.Can calculate antigen and this variable diversity factor of antibody is by this iteration result:

${\mathrm{\&eta;}}_k=\frac{\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}|\mathrm{Ab}\left(i\right)-\mathrm{Ag}\left(l\left(i\right)\right)|}{m}---\left(16\right)$

The process stronger to linearity, the diversity factor of same variable are calculated and also can be adopted the straightforward procedure of formula (7) directly to calculate:

${\mathrm{\&eta;}}_k=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}|\mathrm{Ab}\left(i\right)-\mathrm{Ag}\left(i\right)|}{n}---\left(17\right)$

The diversity factor of calculating all variablees just can obtain the total variances degree vector of antigen and antibody:

η＝[η ₁，η ₂，…，η _n] (18)

Utilize the diversity factor between all antibody of the same type in formula (13)-(18) calculating antibody storehouse, the note be η (i, j).The threshold value of being calculated each variable of the type antibody by formula (7) respectively is:

${\mathrm{threshold}}_k=\underset{1\&le;i\&le;\mathrm{<figure-callout\; id="1"\; label="n\; min"\; filenames="H2009102440664E00021.png,H2009102440664E00022.png"\; state="\{\{state\}\}">n</figure-callout>}}{\mathrm{<figure-callout\; id="1"\; label="max"\; filenames="H2009102440664E00021.png,H2009102440664E00022.png"\; state="\{\{state\}\}">max</figure-callout>}}\underset{}{\min }\underset{1\&le;j\&le;n,i\&NotEqual;j}{}\left|{\mathrm{\&eta;}}_k(i,j)\right|---\left(19\right)$

-utilize pca model to carry out fault detect

After step S106 normalization, obtain real-time detection data x _NewEnter step S107, utilize PCA to carry out fault detect, comprising:

Calculate T respectively by formula (20) (21) ²Statistic and SPE statistic:

${\mathrm{<figure-callout\; id="2"\; label="T"\; filenames="H2009102440664E00021.png,H2009102440664E00022.png"\; state="\{\{state\}\}">T</figure-callout>}}^2={\left|\right|{\mathrm{\&Lambda;}}_k^{-\frac{1}{2}}T\left|\right|}^2={\left|\right|{\mathrm{\&Lambda;}}_k^{-\frac{1}{2}}{P}^T{x}_{\mathrm{new}}\left|\right|}^2---\left(20\right)$

$\mathrm{SPE}={\left|\right|\tilde{P}{\tilde{P}}^T{x}_{\mathrm{new}}\left|\right|}^2---\left(21\right)$

Wherein

Be-1/2 power of preceding k diagonal element among the Λ, Λ, P,

All when setting up pca model, step S103 obtains;

Compare T ²Whether statistic and SPE statistic are limit greater than control, if wherein there is a statistic then to think and may break down greater than the control limit, all prescribe a time limit greater than control when the statistic such as continuous three points, enter troubleshooting step, detect next group data otherwise return; And

Alternatively, can also use the statistic of PVR or CVR to carry out fault detect.

-utilize antibody library to carry out fault diagnosis

Break down in case detect affirmation at step S107, handle entering step S108, with the data generation antigen of set time length before detecting constantly, note is made Ag=[Ag ₁, Ag ₂..., Ag _n].These antigens are identical with the form of antibody in the antibody library, matrixes of being made up of the seasonal effect in time series data sample all, Ag _iTime series for each variable.

After antigen generates, handle the failure mode diagnosis that enters step S109, utilize formula (13)-(18) to calculate the diversity factor of all antibody of antigen and antibody library.When antibody calculates diversity factor less than corresponding threshold value, think that then fault is consistent with this antibody.If all do not satisfy the threshold value requirement with the diversity factor of all antibody in the antibody library, promptly be judged as a kind of new fault.

The fault diagnosis result of refer step S109, the operator can manually diagnose (S201), and is input to system, and system enters the step 110, and the result who accepts input is as final diagnostic result.

-antibody library upgrades

At step S104, with antigen as first generation antibody.Enter step S105 again, the type antibody is carried out clonal vaviation, upgrade the antibody in the antibody library, and upgrade the threshold value of this antibody by formula (19) by formula (10).

Embodiment:

The method according to this invention has been applied to the fault diagnosis of the reaction realistic model of an epoxypropane hydration generation propylene glycol.

This process flow diagram flow chart as shown in Figure 4.This reaction is all finished in stirred tank R-1.Pressure in the course of reaction in the stirred tank is P0, and temperature is T0, and liquid level is L0.R-1 follows two strands and becomes a mandarin, and one is epoxypropane solution, enters R-1 through pump P-1 and valve V-1, wherein contains a spot of magazine methyl alcohol, and the flow of this stream thigh is F1, and temperature is T1, and propylene oxide concentrations is C1-1, and methanol concentration is C1-2.Another enters R-1 for the reaction water through pump P-2 and valve V-2, and flow is F2, and temperature is T2.

Product enters the T-1 jar through the V-3 valve, and product stream plume amount is F3, and temperature is T3, and product propylene glycol concentration is C3.

Because this reaction is themopositive reaction, needs to use the chilled water heat exchange.Chilled water is through V-4 valve and stirred tank heat exchange, and cooling water flow is F4, and temperature is T4.

Also have XC, three controllers of LC and TC in the flow process.Wherein, XC is the aperture change flow F1 by valve V-1, with control product propylene glycol concentration C 3.LC is the aperture change flow F3 by valve V-3, with the liquid level L0 of control stirred tank R-1.XC is the aperture change flow F1 by valve V-1, with control product propylene glycol concentration C 3.

The controlling index of whole process main operating parameters and controller is as shown in table 1.

Table 1 nominal situation parameter is provided with

This model mainly comprises five detection variable: the mole fraction-C3 of product propylene glycol, temperature of reaction-T0, product flow-F3, cooling water flow-F4, reactor liquid level-L0.

Carry out fault diagnosis, at first obtain the detection data of these five variablees by step S101, sampling time interval is 5 seconds.

After step S102 is one group of normal data sample normalization of 50000 seconds, set up pca model at step S103 again.

In addition, 2 too high the raw water temperature T, impurity methyl alcohol mole fraction C1-2 is too high and each 2 groups of data of the malfunctioning three kinds of faults of temperature of reaction T0 controller, carry out normalization at step S102,10 sampling numbers and generate antibody library variation and the clone that step S105 carries out antibody according to generating antibody near step S104 gets the fault introducing time again.

Finish the initialization of fault diagnosis system like this, again real time data has been carried out fault detection and diagnosis after step S106 normalization.This example has adopted 13 groups of real time data samples that method is verified altogether.

Be example with sample 1 at first, sample 1 is a nominal situation, after S106 step normalization, enters S107 step PCA and detects.Calculate the data T of all sampled points ²Statistic and SPE statistic are always less than the control limit, so testing result thinks that this sample is a nominal situation.

Be example with sample 2 again, sample 2 is the too high fault of the raw water temperature T introduced of 595s 2, after S106 step normalization, entering S107 step PCA detects, all limit to the T2 statistic and the SPE statistic of continuous three points of 630s at 620s greater than control, think fault has taken place, enter S108 and go on foot by ten sampling numbers before failure detection time according to generating antigen.Go on foot by S109 again, utilize artificial immune system tracing trouble kind, calculate the diversity factor of all antibody in antigen and the antibody library, find in the too high fault antibody library of this antigen and T2 the antibody diversity factor less than the type fault threshold, and with other types antibody diversity factor all greater than its corresponding threshold.Therefore diagnostic result is the too high fault of T2, and it is too high to confirm that through step S110 artificial diagnostic result is T2, enters the S104 step, generates new antibodies by antigen.By the S105 step, carry out the clone and the variation of antibody again, upgrade antibody library.

Be example with sample 11 again, sample 11 is the fault of the temperature of reaction T0 sensor zero point drift of 290s introducing, after S106 step normalization, enters S107 step PCA and detects, and arrives the T of continuous three points of 310s at 300s ²Statistic and SPE statistic all greater than the control limit, are thought fault have been taken place, and enter S108 and go on foot by ten sampling numbers before failure detection time according to generating antigen.By the S109 step, utilize artificial immune system tracing trouble kind again, calculate the diversity factor of all antibody in antigen and the antibody library, find that this antigen and all antibody diversity factoies are all greater than its corresponding threshold.Therefore diagnostic result thinks that time sample is new fault.Confirm that through step S110 artificial diagnostic result is the drift of T0 sensor zero point.Enter the S104 step again, generate new antibodies by antigen.By the S105 step, carry out the clone and the variation of antibody again, upgrade antibody library.

All fault diagnosis results of this example are as shown in table 2.

Table 2 fault diagnosis test result

Sequence number	Fault category	The introducing time	Detection time	Diagnostic-type
					1	Normally	-	-	Normally
2	T2 is too high	595s	630s	T2 is too high
					3	T2 is too high	120s	145s	T2 is too high
4	T2 is too high	230s	255s	T2 is too high
					5	C1-2 is too high	225s	255s	C1-2 is too high

6	C1-2 is too high	315s	340s	C1-2 is too high
					7	C1-2 is too high	320s	350s	C1-2 is too high
8	TC is malfunctioning	240s	350s	TC is malfunctioning
					Sequence number	Fault category	The introducing time	Detection time	Diagnostic-type
9	TC is malfunctioning	205s	230s	TC is malfunctioning
					10	TC is malfunctioning	325s	400s	TC is malfunctioning
11	The drift of T0 sensor zero point	290s	310s	New fault
					12	The drift of T0 sensor zero point	465s	485s	The drift of T0 sensor zero point
13	The drift of T0 sensor zero point	380s	400s	The drift of T0 sensor zero point

Should be understood that, in above narration and explanation to just explanation but not determinate of description that the present invention carried out, and do not breaking away under the prerequisite of the present invention that limits as appended claims, can carry out various changes, distortion and/or correction the foregoing description.

Claims (10)

1. mixed fault diagnostic method based on PCA and artificial immune system is characterized in that comprising:

The PCA modeling procedure generates pca model with original normal sample,

Antibody generates step, generates antibody by the primary fault sample,

Failure detection steps is carried out fault detect by described pca model.

2. according to the mixed fault diagnostic method based on PCA and artificial immune system of claim 1, it is characterized in that further comprising:

Antigen generation step is judged under the situation that the fault existence is arranged in described failure detection steps, with the real time data generation antigen of schedule time length before the detection constantly of described failure detection steps,

Troubleshooting step utilizes described antigen and described antibody, judges the kind of fault.

3. according to the mixed fault diagnostic method based on PCA and artificial immune system of claim 1 or 2, it is characterized in that further comprising:

Data acquisition and treatment step are gathered required flow process variable data, and described flow process variable data comprises historical data and described real time data, and described historical data comprises described original normal sample and described primary fault sample,

Wherein antibody generation step further comprises, from the described antibody that is generated by the primary fault sample, variation and clone generate second generation antibody.

4. according to the mixed fault diagnostic method based on PCA and artificial immune system of claim 2, it is characterized in that further comprising:

Data acquisition and treatment step, gather required flow process variable data, described flow process variable data comprises historical data and described real time data, described historical data comprises described original normal sample and described primary fault sample, and wherein said historical data and described real time data are subjected to normalized:

$X=0.5+\frac{x-\stackrel{\&OverBar;}{X}}{X_{\max }-X_{\min }}$

Wherein x is a real data, X, X _MaxWith X _MinThe mean value of this variable data when being respectively nominal situation, maximal value and minimum value,

Described antibody generates step and comprises:

Determine the failure mode and the time of described primary fault sample,

Data with the described primary fault sample of schedule time length after the fault introducing generate first generation antibody,

Described first generation antibody is made a variation and clones, thereby generate a large amount of second generation antibody,

Wherein said first generation antibody and described second generation antibody have constituted antibody library, and described first generation antibody and described second generation antibody all is used to described troubleshooting step,

Generate second generation antibody by formula (10) variation and clone,

$X^{*}=\left\{\begin{array}{c}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{a}_i{X}_i+b(X_c-X_d),\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{a}_i=1...n>1\\ \begin{array}{cc}(1+\frac{b}{2})X_1& ...n=1\end{array}\end{array}\right.---\left(10\right)$

Wherein

Ab_fault=[Ab ₁, Ab ₂..., Ab _n] the described first generation antibody of expression,

Ab _iBe the time series of each variable,

N is the number of known this kind first generation antibody,

X _iBe known first generation antibody,

X ^*Be the second generation antibody that generates through the variation clone,

a _iBe the decimal at random in 0 to 1,

B is the decimal at random between-1 to 1,

C and d are the random integers between 1 to n,

Calculate the threshold value of every kind of fault type antibody,

Described failure detection steps comprises:

Calculate T respectively by formula (20) (21) ²Statistic and SPE statistic:

$T^2={\left|\right|{\mathrm{\&Lambda;}}_k^{-\frac{1}{2}}T\left|\right|}^2={\left|\right|{\mathrm{\&Lambda;}}_k^{-\frac{1}{2}}{P}^T{x}_{\mathrm{new}}\left|\right|}^2---\left(20\right)$

$\mathrm{SPE}={\left|\right|\tilde{P}{\tilde{P}}^T{x}_{\mathrm{new}}\left|\right|}^2---\left(21\right)$

Wherein

x _NewBe described real time data,

Be-1/2 power of preceding k diagonal element among the Λ,

Λ, P,

All obtain at described PCA modeling procedure;

Compare T ²Whether statistic and SPE statistic all prescribe a time limit greater than control when the statistic of predetermined number greater than the control limit, and then judge and break down,

Described troubleshooting step comprises:

The data of set time length before the fault detect are constantly generated antigen A g=[Ag ₁, Ag ₂..., Ag _n], these antigens are identical with the form of antibody in the described antibody library, matrixes of being made up of the seasonal effect in time series data sample all, Ag _iBe the time series of each variable,

Utilize formula (13)-(16) and (18) to calculate the diversity factor of all antibody of antigen and described antibody library:

d _k(i，j)＝|Ab _k(j)-Ag _k(i)|j∈[1，n]，i∈[1，n] (13)

D wherein _k(i j) represents antigen and antibody respectively at i, and j is the Euclidean distance of variable k constantly,

Construct a similarity matrix D and be used for describing between antigen and antibody track about the minimum difference degree of this variable, iterative computation can be by d _k(i j) obtains D _k(i j), at first determines the iteration initial point.

D _k(1，1)＝d _k(1，1) (14)

Further iterative computation when j＜n:

$D_k(i,j^{*})=\min \left\{\begin{array}{c}{D}_k(i-1,j)+d_k(i,j)......l\left(i\right)=j^{*}=j,\mathrm{ifl}(i-1)\&NotEqual;(i-2)\\ D_k(i-1,j)+d_k(i,j+1)...l\left(i\right)=j^{*}=j+1,\mathrm{ifj}<n\\ D_k(i-1,j)+d_k(i,j+2)...l\left(i\right)=j^{*}=j+2,\mathrm{ifj}<n-1\end{array}\right.---\left(15\right)$

Stop iteration when j=n, the iterations note of this moment is m=i, has found a shortest calculating path of diversity factor,

Can calculate antigen and this variable diversity factor of antibody is by this iteration result:

${\mathrm{\&eta;}}_k=\frac{\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}|\mathrm{Ab}\left(i\right)-\mathrm{Ag}\left(l\left(i\right)\right)|}{m}---\left(16\right)$

The diversity factor of calculating all variablees just can obtain the total variances degree vector of antigen and antibody:

η＝[η ₁，η ₂，…，η _n] (18)

When antibody calculates diversity factor less than corresponding threshold value, judge that then fault is consistent with this antibody,

If all do not satisfy the threshold value requirement with the diversity factor of all antibody in the described antibody library, promptly be judged as a kind of new fault.

5. according to the mixed fault diagnostic method based on PCA and artificial immune system of claim 2 or 4, it is characterized in that further comprising:

By formula (10) described antigen is carried out clonal vaviation, thereby generates new antibody,

Upgrade antibody in the described antibody library with described new antibody,

Determine the described threshold value of described new antibody.

6. mixed fault diagnostic system based on PCA and artificial immune system is characterized in that comprising:

The PCA model building device is used for generating pca model with original normal sample,

The antibody generating apparatus is used for generating antibody by the primary fault sample,

Failure detector is used for carrying out fault detect with described pca model.

7. according to the mixed fault diagnostic system based on PCA and artificial immune system of claim 6, it is characterized in that further comprising:

The antigen generating apparatus is used for judging under the situation that the fault existence is arranged in described failure detection steps, with the real time data generation antigen of schedule time length before the detection constantly of described failure detection steps,

Trouble-shooter is used to utilize described antigen and described antibody, judges the kind of fault.

8. according to the mixed fault diagnostic system based on PCA and artificial immune system of claim 6 or 7, it is characterized in that further comprising:

Data acquisition and treating apparatus are used to gather required flow process variable data, and described flow process variable data comprises historical data and described real time data, and described historical data comprises described original normal sample and described primary fault sample,

Wherein the antibody generating apparatus further comprises the described antibody that is used for from by the generation of primary fault sample, and variation and clone generate the device of second generation antibody.

9. according to the mixed fault diagnostic system based on PCA and artificial immune system of claim 7, it is characterized in that further comprising:

Data acquisition and treating apparatus, be used to gather required flow process variable data, described flow process variable data comprises historical data and described real time data, described historical data comprises described original normal sample and described primary fault sample, and wherein said historical data and described real time data are subjected to normalized:

$X=0.5+\frac{x-\stackrel{\&OverBar;}{X}}{X_{\max }-X_{\min }}$

Wherein x is a real data, X, X _MaxWith X _MinThe mean value of this variable data when being respectively nominal situation, maximal value and minimum value,

Described antibody generating apparatus is used to carry out following operation:

Determine the failure mode and the time of described primary fault sample,

Data with the described primary fault sample of schedule time length after the fault introducing generate first generation antibody,

Described first generation antibody is made a variation and clones, thereby generate a large amount of second generation antibody,

Wherein said first generation antibody and described second generation antibody have constituted antibody library, and described first generation antibody and described second generation antibody all is used to described troubleshooting step,

Generate second generation antibody by following formula (10) variation and clone,

$X^{*}=\left\{\begin{array}{c}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{a}_i{X}_i+b(X_x-X_d),\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{a}_i=1...n>1\\ \begin{array}{cc}(1+\frac{b}{2})X_1& ...n=1\end{array}\end{array}\right.---\left(10\right)$

Wherein

Ab_fault=[Ab ₁, Ab ₂..., Ab _n] the described first generation antibody of expression,

Ab _iBe the time series of each variable,

N is the number of known this kind first generation antibody,

X _iBe known first generation antibody,

X ^*Be the second generation antibody that generates through the variation clone,

a _iBe the decimal at random in 0 to 1,

B is the decimal at random between-1 to 1,

C and d are the random integers between 1 to n,

Calculate the threshold value of every kind of fault type antibody,

Described failure detector is used to carry out following operation:

Calculate T respectively by formula (20) (21) ²Statistic and SPE statistic:

$T^2={\left|\right|{\mathrm{\&Lambda;}}_k^{-\frac{1}{2}}T\left|\right|}^2={\left|\right|{\mathrm{\&Lambda;}}_k^{-\frac{1}{2}}{P}^T{x}_{\mathrm{new}}\left|\right|}^2---\left(20\right)$

$\mathrm{SPE}={\left|\right|\tilde{P}{\tilde{P}}^T{x}_{\mathrm{new}}\left|\right|}^2---\left(21\right)$

Wherein

x _NewBe described real time data,

Be-1/2 power of preceding k diagonal element among the Λ,

Λ, P,

All obtain at described PCA modeling procedure;

Compare T ²Whether statistic and SPE statistic all prescribe a time limit greater than control when the statistic of predetermined number greater than the control limit, and then judge and break down,

Described trouble-shooter is used to carry out following operation:

The data of set time length before the fault detect are constantly generated antigen A g=[Ag ₁, Ag ₂..., Ag _n], these antigens are identical with the form of antibody in the described antibody library, matrixes of being made up of the seasonal effect in time series data sample all, Ag _iBe the time series of each variable,

Utilize formula (13)-(16) and (18) to calculate the diversity factor of all antibody of antigen and described antibody library:

d _k(i，j)＝|Ab _k(j)-Ag _k(i)|j∈[1，n]，i∈[1，n] (13)

D wherein _k(i j) represents antigen and antibody respectively at i, and j is the Euclidean distance of variable k constantly,

Construct a similarity matrix D and be used for describing between antigen and antibody track about the minimum difference degree of this variable, iterative computation can be by d _k(i j) obtains D _k(i j), at first determines the iteration initial point.

D _k(1，1)＝d _k(1，1) (14)

Further iterative computation when j＜n:

$D_k(i,j^{*})=\min \left\{\begin{array}{c}{D}_k(i-1,j)+d_k(i,j)......l\left(i\right)=j^{*}=j,\mathrm{ifl}(i-1)\&NotEqual;(i-2)\\ D_k(i-1,j)+d_k(i,j+1)...l\left(i\right)=j^{*}=j+1,\mathrm{ifj}<n\\ D_k(i-1,j)+d_k(i,j+2)...l\left(i\right)=j^{*}=j+2,\mathrm{ifj}<n-1\end{array}\right.---\left(15\right)$

Stop iteration when j=n, the iterations note of this moment is m=i, has found a shortest calculating path of diversity factor,

Can calculate antigen and this variable diversity factor of antibody is by this iteration result:

${\mathrm{\&eta;}}_k=\frac{\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}|\mathrm{Ab}\left(i\right)-\mathrm{Ag}\left(l\left(i\right)\right)|}{m}---\left(16\right)$

The diversity factor of calculating all variablees just can obtain the total variances degree vector of antigen and antibody:

η＝[η ₁，η ₂，…，η _n] (18)

When antibody calculates diversity factor less than corresponding threshold value, judge that then fault is consistent with this antibody,

If all do not satisfy the threshold value requirement with the diversity factor of all antibody in the described antibody library, promptly be judged as a kind of new fault.

10. according to the mixed fault diagnostic system based on PCA and artificial immune system of claim 7 or 9, it is characterized in that further comprising the antibody updating device, described antibody updating device further comprises:

By formula (10) thus described antigen is carried out the device that clonal vaviation generates new antibody,

Upgrade the device of the antibody in the described antibody library with described new antibody,

Determine the device of the described threshold value of described new antibody.

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