CN100517141C - System and method for detecting date and diagnosing failure of propylene polymerisation production - Google Patents

Abstract

A propylene polymerization production data detection and failure diagnosis system, consists of the field intelligent instruments connected with the propylene polymerization production process, DCS system and host, the DCS system has the data interface, control station, and database; the intelligent instruments, DCS system, and host are interconnected in turn, the host has the standardized processing module, main element analyzing module, residual error analyzing module, signal collecting module, diagnostic data specifying module and troubleshooting module. And the invention provides a failure diagnosis method. The invention provides a propylene polymerization production data detection and failure diagnosis system and method for on-line measuring of fuse index, obtaining the excellent diagnosis effect, and effective reducing of the error rate.

Description

Detection of propylene polymerization production data and fault diagnosis system and method

(1) technical field

The present invention relates to the industrial process fault diagnosis field, especially, relate to detection of a kind of propylene polymerization production data and fault diagnosis system and method.

(2) background technology

Polypropylene is to be the main a kind of synthetic resin that is polymerized with the propylene monomer, is the staple product in the plastics industry.Because polypropylene has light specific gravity, anti-anti-impact, corrosion-resistant, high transparent, avirulence, intensity is good, electrical insulation capability is good and be easy to premium properties such as processing, thereby be widely used in fields such as light industry, chemical industry, chemical fibre, building materials, household electrical appliances, packing, automobile, in the polyolefin resin of present China, become the third-largest plastics that are only second to tygon and Polyvinylchloride.

Industrialized polypropylene production process complicacy height, invest huge, its process units long-term safety, reliable, quiet run is very important.In polypropylene production process, melting index (MI) is an important indicator of reflection product quality, is the important evidence that the quality of production control and the trade mark switch.But MI can only offline inspection, and general off-line analysis needs nearly 2 hours at least, and is expensive and consuming time, particularly can't in time understand the state of polypropylene production process during 2 of off-line analysis hours.Therefore, choose with the closely-related easy survey variable of melting index as secondary variable, therefrom analyze melting index, whether normal, propylene polymerization production process is monitored just seem important unusually if detecting production run.

In the existing polypropylene industrial production run,, there is certain correlativity between each variable,, should adopts Multivariate Control Chart in essence the monitoring of process because variable is more.Therefore existing fault diagnosis system and the method for with a plurality of single argument control charts a plurality of variablees being monitored simultaneously will be difficult to the operation conditions of accurate interpretation process, and can increase rate of false alarm.

(3) summary of the invention

For overcome that existing propylene polymerization production data detects and fault diagnosis system can not the on-line measurement melting index, be difficult to obtain diagnosis effect, the higher deficiency of rate of false alarm preferably, the invention provides and a kind ofly can realize the on-line measurement melting index, can access good diagnosis effect, effectively reduce the detection of a kind of propylene polymerization production data and the fault diagnosis system and the method for rate of false alarm.

The technical solution adopted for the present invention to solve the technical problems is:

A kind of propylene polymerization production data detects and fault diagnosis system, comprises the field intelligent instrument, DCS system and the host computer that are connected with propylene polymerization production process, and described DCS system is made of data-interface, control station, database; Intelligence instrument, DCS system, host computer link to each other successively, and described host computer comprises:

The standardization module, be used for to the database acquisition system just often the data of key variables carry out standardization, the average of each variable is 0, variance is 1, obtains input matrix X, adopts following process to finish:

1) computation of mean values: $\stackrel{\‾}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{TX}}_i,---\left(1\right)$

2) calculate variance: ${\mathrm{\σ}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}({\mathrm{TX}}_i-\stackrel{\‾}{\mathrm{TX}}),---\left(2\right)$

3) standardization: $X=\frac{\mathrm{TX}-\stackrel{\‾}{\mathrm{TX}}}{{\mathrm{\σ}}_x},---\left(3\right)$

Wherein, TX is a training sample, and N is a number of training, and TX is the average of training sample;

The pivot analysis module is used to carry out pivot analysis and extracts major component, according to the pivot variance extraction ratio that is provided with, adopts the method for covariance svd, adopts following steps to realize:

1. calculate the covariance matrix of X, be designated as ∑ _X

2. to ∑ _XCarry out svd, obtain characteristic root λ ₁, λ ₂..., λ _p, λ wherein ₁〉=λ 2 〉=... 〉=λ p, the characteristic of correspondence vector matrix is U;

3. calculate population variance and each eigenwert corresponding variance contribution rate, adding up from big to small by the variance contribution ratio of each eigenwert reaches set-point up to total variance contribution ratio, and it is k that note is chosen number;

4. the preceding k of selected characteristic vector matrix U is listed as, as transformation matrix T;

5. calculate pivot, calculate pivot F by formula F=T * X;

The residual analysis module is used to calculate residual analysis control limit, and it is α that insolation level is set, and the control limit is pressed following formula (4) and calculated:

$Q_{\mathrm{\α}}={\mathrm{\θ}}_1{[\frac{C_{\mathrm{\α}}{h}_0\sqrt{{2\mathrm{\θ}}_2}}{{\mathrm{\θ}}_1}+1+\frac{{\mathrm{\θ}}_2{h}_0(h_0-1)}{{\mathrm{\θ}}_2}]}^{\frac{1}{h_0}}---\left(4\right)$

In the formula:

${\mathrm{\θ}}_i=\underset{j=k+1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\λ}}_j^i,i=\mathrm{1,2,3}$ $h_0=1-\frac{2{\mathrm{\θ}}_1{\mathrm{\θ}}_3}{{3\mathrm{\θ}}_2^2}$

λ _iBe the eigenwert of X covariance matrix, C _αBe that the normal distribution degree of confidence is the statistics of α.

Signal acquisition module is used to set the time slot of each sampling, the signal of collection site intelligence instrument;

The diagnostic data determination module, the data that are used for gathering are sent to the DCS real-time data base, from the real-time data base of DCS database, obtain up-to-date variable data as diagnostic data VX at each timing cycle;

Fault diagnosis module is used for TX and σ that data to be tested VX the time is obtained with training _x ²Carry out standardization, and with the input of the data after the standardization as the pivot analysis module, the transformation matrix T that obtains during with training carries out conversion to input, matrix is input to the residual analysis module after the conversion, calculates the Q statistic of input data, if Q＜Q _α, judge that sample point Q statistics is normal, the process object is normal; If Q＞Q _α, judge that sample point Q statistics is unusual, the process object breaks down.

As preferred a kind of scheme: described host computer also comprises: the discrimination model update module, be used for regularly adding the normal point of process status to training set VX, output to standardization module, pivot analysis module, residual analysis module, and upgrade the model in the residual analysis module.

As preferred another kind of scheme: described host computer also comprises: display module as a result, be used for fault diagnosis result is passed to the DCS system, and, by DCS system and fieldbus process status information is delivered to operator station simultaneously and shows at the control station procedure for displaying state of DCS.

As preferred another scheme: described key variables comprise catalyzer flow rate f ₄, cocatalyst flow rate f ₅, three gangs of propylene feed flow rate (f ₁, f ₂, f ₃), hydrogen volume concentration α in liquid level l and the still in the still inner fluid temperature T, still inner fluid pressure P, still.

The method for diagnosing faults that the described propylene polymerization production data of a kind of usefulness detects and fault diagnosis system realizes, described method for diagnosing faults may further comprise the steps:

(1), from the historical data base of DCS database acquisition system just often the data of key variables as training sample TX;

(2), in the pivot analysis module of host computer, residual analysis module, pivot analysis variance extraction ratio, residual analysis confidence limit alpha parameter are set respectively, set the sampling period among the DCS;

(3), training sample TX in host computer, data are carried out standardization, make that the average of each variable is 0, variance is 1, obtains input matrix X, adopts following process to finish:

3.1) computation of mean values: $\stackrel{\‾}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{TX}}_i---\left(1\right),$

3.2) the calculating variance: ${\mathrm{\σ}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}({\mathrm{TX}}_i-\stackrel{\‾}{\mathrm{TX}})---\left(2\right),$

3.3) standardization: $X=\frac{\mathrm{TX}-\stackrel{\‾}{\mathrm{TX}}}{{\mathrm{\σ}}_x}---\left(3\right),$

Wherein, N is a number of training, and N is a number of training, and TX is the average of training sample;

(4), carry out pivot analysis and extract major component, adopt the method for covariance svd, adopt following steps to realize:

1. calculate the covariance matrix of X, be designated as ∑ _X

2. to ∑ _XCarry out svd, obtain characteristic root λ ₁, λ ₂..., λ _p, λ wherein ₁〉=λ 2 〉=... 〉=λ p, the characteristic of correspondence vector matrix is U;

3. calculate population variance and each eigenwert corresponding variance contribution rate, adding up from big to small by the variance contribution ratio of each eigenwert reaches set-point up to total variance contribution ratio, and it is k that note is chosen number;

4. the preceding k of selected characteristic vector matrix U is listed as, as transformation matrix T;

5. calculate pivot, calculate pivot F by formula F=T * X;

(5), calculate residual analysis control limit; When insolation level was α, the control limit was pressed following formula (4) and is calculated:

$Q_{\mathrm{\α}}={\mathrm{\θ}}_1{[\frac{C_{\mathrm{\α}}{h}_0\sqrt{2{\mathrm{\θ}}_2}}{{\mathrm{\θ}}_1}+1+\frac{{\mathrm{\θ}}_2{h}_0(h_0-1)}{{\mathrm{\θ}}_2}]}^{\frac{1}{h_0}}---\left(4\right)$

In the formula:

${\mathrm{\θ}}_i=\underset{j=k+1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\λ}}_j^i,i=\mathrm{1,2,3}$ $h_0=1-\frac{{2\mathrm{\θ}}_1{\mathrm{\θ}}_3}{{3\mathrm{\θ}}_2^2}$

λ _iBe the eigenwert of X covariance matrix, C _αBe that the normal distribution degree of confidence is the statistics of α;

(6), the data of gathering are sent in the DCS real-time data base, from the real-time data base of DCS database, obtain up-to-date variable data at each timing cycle as diagnostic data VX; TX and σ that data to be tested VX the time is obtained with training _x ²Carry out standardization, and with the input of the data after the standardization as the pivot analysis module, the transformation matrix T that obtains during with training carries out conversion to input, matrix is input to the residual analysis module after the conversion, calculates the Q statistic of input data, if Q＜Q _α, judge that sample point Q statistics is normal, the process object is normal; If Q＞Q _α, judge that sample point Q statistics is unusual, the process object breaks down.

As preferred a kind of scheme: described method for diagnosing faults also comprises: (7), regularly the normal point of process status is added among the training set VX, repeat the training process of (3)～(5), so that the model of the residual analysis module that upgrades in time.

As preferred another kind of scheme: in described (6), host computer is passed to the DCS system with fault diagnosis result, and, by DCS system and fieldbus process status information is delivered to operator station simultaneously and shows at the control station procedure for displaying state of DCS.

As preferred another scheme: described key variables comprise catalyzer flow rate f ₄, cocatalyst flow rate f ₅, three gangs of propylene feed flow rate (f ₁, f ₂, f ₃), hydrogen volume concentration α in liquid level l and the still in the still inner fluid temperature T, still inner fluid pressure P, still.

Technical conceive of the present invention is: in the polypropylene industrial process, because variable is more, have certain correlativity between each variable.In the prior art, with a plurality of single argument control charts a plurality of variablees are monitored the operation conditions that will be difficult to the correct interpretation process simultaneously, and can increase rate of false alarm.The present invention adopts Multivariate Control Chart to the monitoring of process.

Pivot analysis (PCA) and residual analysis (Q statistic) are combined, can well be applied to the monitoring of multivariable process statistics.Be used for the multivariate monitoring because pivot analysis and residual analysis combine, when making full use of data message, reduced system's dimension, make that monitoring can be more accurately, reliably.

Beneficial effect of the present invention mainly shows: 1, the pivot analysis algorithm is simple, convergence is good, reduction system dimension under the prerequisite of loss of information can exceeded, with the Q statistic analysis monitoring is carried out in the residual error space of pivot analysis, have the mature theory basis, calculate simple, monitoring effect is good, make fault diagnosis effective and rapid, can better instruct production, improve productivity effect; 2, fault diagnosis system is based upon on the existing DCS system, implements simply, does not need hardware modification substantially, and cost is low, is easy to promote.

(4) description of drawings

Fig. 1 is the hardware structure diagram of fault diagnosis system proposed by the invention;

Fig. 2 is a fault diagnosis system functional block diagram proposed by the invention;

Fig. 3 is a polypropylene production procedure sketch;

Fig. 4 is that pivot analysis and residual analysis (PCA-Q) detect design sketch;

Fig. 5 is the theory diagram of host computer of the present invention.

(5) embodiment

Below in conjunction with accompanying drawing the present invention is further described.The embodiment of the invention is used for the present invention that explains, rather than limits the invention, and in the protection domain of spirit of the present invention and claim, any modification and change to the present invention makes all fall into protection scope of the present invention.

Embodiment 1

With reference to Fig. 1, Fig. 2, Fig. 3, Fig. 4 and Fig. 5, a kind of propylene polymerization production data detects and fault diagnosis system, comprise the field intelligent instrument 2, DCS system and the host computer 6 that are connected with propylene polymerization production process, described DCS system is made of data-interface 3, control station 4, database 5; Intelligence instrument 2, DCS system, host computer 6 link to each other successively by fieldbus, and described host computer 6 comprises:

Standardization module 7 is used for database acquisition system data are just often carried out standardization, and the average of each variable is 0, and variance is 1, obtains input matrix X, adopts following process to finish:

1) computation of mean values: $\stackrel{\‾}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{TX}}_i,---\left(1\right)$

2) calculate variance: ${\mathrm{\σ}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}({\mathrm{TX}}_i-\stackrel{\‾}{\mathrm{TX}}),---\left(2\right)$

3) standardization: $X=\frac{\mathrm{TX}-\stackrel{\‾}{\mathrm{TX}}}{{\mathrm{\σ}}_x},---\left(3\right)$

Wherein, TX is a training sample, and N is a number of training, and TX is the average of training sample;

Pivot analysis module 8 is used to carry out pivot analysis and extracts major component, according to the pivot variance extraction ratio that is provided with, adopts the method for covariance svd, adopts following steps to realize:

1. calculate the covariance matrix of X, be designated as ∑ _X

2. to ∑ _XCarry out svd, obtain characteristic root λ ₁, λ ₂..., λ _p, λ wherein ₁〉=λ 2 〉=... 〉=λ p, the characteristic of correspondence vector matrix is U;

3. calculate population variance and each eigenwert corresponding variance contribution rate, adding up from big to small by the variance contribution ratio of each eigenwert reaches set-point (generally getting greater than 80%) up to total variance contribution ratio, and it is k that note is chosen number;

4. the preceding k of selected characteristic vector matrix U is listed as, as transformation matrix T;

5. calculate pivot, calculate pivot F by formula F=T * X;

Residual analysis module 9 is used to calculate residual analysis control limit, and it is α that insolation level is set, and the control limit is pressed following formula (4) and calculated:

$Q_{\mathrm{\α}}={\mathrm{\θ}}_1{[\frac{C_{\mathrm{\α}}{h}_0\sqrt{2{\mathrm{\θ}}_2}}{{\mathrm{\θ}}_1}+1+\frac{{\mathrm{\θ}}_2{h}_0(h_0-1)}{{\mathrm{\θ}}_2}]}^{\frac{1}{h_0}}---\left(4\right)$

In the formula:

${\mathrm{\θ}}_i=\underset{j=k+1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\λ}}_j^i,i=\mathrm{1,2,3}$ $h_0=1-\frac{{2\mathrm{\θ}}_1{\mathrm{\θ}}_3}{{3\mathrm{\θ}}_2^2}$

λ _iBe the eigenwert of X covariance matrix, C _αBe that the normal distribution degree of confidence is the statistics of α.

Signal acquisition module 10 is used to set the time slot of each sampling, the signal of collection site intelligence instrument;

Diagnostic data determination module 11, the data that are used for gathering are sent to the DCS real-time data base, from the real-time data base of DCS database, obtain up-to-date variable data as diagnostic data VX at each timing cycle;

Fault diagnosis module 12 is used for TX and σ that data to be tested VX the time is obtained with training _x ²Carry out standardization, and with the input of the data after the standardization as the pivot analysis module, the transformation matrix T that obtains during with training carries out conversion to input, matrix is input to the residual analysis module after the conversion, calculates the Q statistic of input data, if Q＜Q _α, judge that sample point Q statistics is normal, the process object is normal; If Q＞Q _α, judge that sample point Q statistics is unusual, the process object breaks down.

Described host computer also comprises: discrimination model update module 13, be used for regularly adding the normal point of process status to training set VX, output to standardization module 7, pivot analysis module 8, residual analysis module 9, and upgrade the model in the residual analysis module 9.。

Described host computer also comprises: display module 14 as a result, are used for fault diagnosis result is passed to the DCS system, and at the control station procedure for displaying state of DCS, by DCS system and fieldbus process status information are delivered to operator station simultaneously and show.

The hardware structure diagram of fault diagnosis system of the present invention as shown in Figure 1, the core of described fault diagnosis system is made of the host computer 6 that comprises three big functional modules such as standardized module 7, pivot analysis module 8, residual analysis module 9 and man-machine interface, comprise in addition: field intelligent instrument 2, DCS system and fieldbus.Described DCS system is made of data-interface 3, control station 4, database 5; Propylene polymerization production process 1, intelligence instrument 2, DCS system, host computer 6 link to each other successively by fieldbus, realize uploading and assigning of information flow.Fault diagnosis system is moved on host computer 6, can carry out message exchange with first floor system easily, in time the answering system fault.

The functional block diagram of fault diagnosis system of the present invention mainly comprises three big functional modules such as standardized module 7, pivot analysis module 8, residual analysis module 9 as shown in Figure 2.

Described method for diagnosing faults is realized according to following steps:

1, from the historical data base of DCS database 5 data of just often following nine variablees of acquisition system as training sample TX: major catalyst flow rate f ₄, cocatalyst flow rate f ₅, three gangs of propylene feed flow rate (f ₁, f ₂, f ₃) hydrogen volume concentration α in liquid level l and the still in the still inner fluid temperature T, still inner fluid pressure P, still;

2, in the pivot analysis module 8 of host computer 6, residual analysis module 9, parameters such as pivot analysis variance extraction ratio, residual analysis confidence limit α are set respectively, set the sampling period among the DCS;

3, training sample TX passes through functional modules such as standardization 7, pivot analysis module 8, residual analysis module 9 successively in host computer 6, adopts following steps to finish the training of fault diagnosis system in the host computer 6:

1) the standardization functional module 7 of host computer 6 is carried out standardization to data, makes that the average of each variable is 0, and variance is 1, obtains input matrix X.Adopt following steps to realize:

1. computation of mean values: $\stackrel{\‾}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{TX}}_i---\left(1\right)$

2. calculate variance: ${\mathrm{\σ}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}({\mathrm{TX}}_i-\stackrel{\‾}{\mathrm{TX}})---\left(2\right)$

3. standardization: $X=\frac{\mathrm{TX}-\stackrel{\‾}{\mathrm{TX}}}{{\mathrm{\σ}}_x}---\left(3\right)$

Wherein N is a number of training, and N is a number of training, and TX is the average of training sample;

The standardization that the standardization functional module 7 of host computer 6 is carried out can eliminate each variable because the influence that the dimension difference causes.

2) the pivot analysis functional module 8 of host computer 6 is carried out pivot analysis and is extracted major component.Shown pivot analysis population variance extraction ratio is greater than 80%, and computation process adopts the method for covariance svd.Adopt following steps to realize:

1. calculate the covariance matrix of X, be designated as ∑ _X

2. to ∑ _XCarry out svd, obtain characteristic root λ ₁, λ ₂..., λ _p, λ wherein ₁〉=λ 2 〉=... 〉=λ p, the characteristic of correspondence vector matrix is U;

3. calculate population variance and each eigenwert corresponding variance contribution rate, adding up from big to small by the variance contribution ratio of each eigenwert reaches set-point (generally getting greater than 80%) up to total variance contribution ratio, and it is k that note is chosen number;

4. the preceding k of selected characteristic vector matrix U is listed as, as transformation matrix T;

5. calculate pivot, calculate pivot F by formula F=T * X.

Pivot analysis is lost under the minimum principle making every effort to data message, to the variable space dimensionality reduction of higher-dimension.Its essence is a few linear combination of research variable system, and the generalized variable that this several linear combination constituted will keep the information of former variable variation aspect as much as possible.Obviously, analytic system at a lower dimensional space than much easier at a higher dimensional space.

3) the residual analysis module 9 of host computer 6 is calculated residual analysis control limit.

When insolation level was α, the control limit can be calculated by following formula (4):

$Q_{\mathrm{\α}}={\mathrm{\θ}}_1{[\frac{C_{\mathrm{\α}}{h}_0\sqrt{2{\mathrm{\θ}}_2}}{{\mathrm{\θ}}_1}+1+\frac{{\mathrm{\θ}}_2{h}_0(h_0-1)}{{\mathrm{\θ}}_2}]}^{\frac{1}{h_0}}---\left(4\right)$

In the formula

${\mathrm{\θ}}_i=\underset{j=k+1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\λ}}_j^i,i=\mathrm{1,2,3}$ $h_0=1-\frac{{2\mathrm{\θ}}_1{\mathrm{\θ}}_3}{{3\mathrm{\θ}}_2^2}$

λ _iBe the eigenwert of X covariance matrix, C _αBe that the normal distribution degree of confidence is the statistics of α.

4, system begins to put into operation:

1) uses timer, set the time interval of each sampling;

2) field intelligent instrument 2 testing process data and being sent in the real-time data base of DCS database 5;

3) host computer 6 from the real-time data base of DCS database 5, obtains up-to-date variable data at each timing cycle, as diagnostic data VX;

4) data to be tested VX, in the standardization functional module 7 of host computer 6, the TX and the σ that obtain during with training _x ²Carry out standardization, and with the input of the data after the standardization as pivot analysis module 8;

5) the pivot analysis module 8 in the host computer 6, the transformation matrix T that obtains during with training carries out conversion to input, and the matrix after the conversion is input to residual analysis module 9, as the input of residual analysis module 9;

6) the residual analysis module 9 in the host computer 6, adopt following formula to calculate the Q statistic of input data:

For i sample,

Q _i＝e _ie _i′

E wherein _iThe i that is residual matrix E is capable, if Q＜Q _α, illustrate that this sample point Q statistics is normal, otherwise, if Q＞Q _α, illustrating that this sample point Q statistics is unusual, the process object breaks down;

7) host computer 6 is passed to DCS with fault diagnosis result, and at the control station 4 procedure for displaying states of DCS, by DCS system and fieldbus process status information is delivered to operator station simultaneously and shows, makes the execute-in-place worker in time to tackle.

5, model modification

In system puts into operation process, regularly the normal point of process status is added among the training set TX, the training process of repeating step 3 is so that the model in the residual analysis module 9 of the host computer 6 that upgrades in time keeps model to have effect preferably.

Describe a specific embodiment of the present invention below in detail.

Producing HYPOL technology actual industrial production with polypropylene is example.Figure three has provided typical Hypol continuous stirred tank (CSTR) method and has produced polyacrylic process chart, and preceding 2 stills are that CSTR reactor, back 2 stills are fluidized-bed reactor (FBR).Choose in major catalyst flow rate, cocatalyst flow rate, three bursts of propylene feed flow rates, still inner fluid temperature, still inner fluid pressure, the still in liquid level, the still nine of hydrogen volume concentration and easily survey the input quantities of performance variables as model, from the DCS system of production run, obtain the data of these nine parameters as training sample, wherein 50 normal samples are as training set, and 22 sample points are as test set data verification diagnosis effect in addition.It is 7 that PCA extracts the major component number, fiducial probability 0.98, and the sampling period is 2 hours.Fig. 4 detects design sketch, the distribution of preceding two major components of only having drawn among the figure for PCA-Q.Table 1 listed with the corresponding test set of Fig. 4 in physical fault point and the detected trouble spot of native system, only fail to report No. 15 trouble spots as can be seen, rate of false alarm is 0.Obviously, native system has higher diagnostic accuracy.

The physical fault point	1，2，12，15，16
		The detection failure point	1，2，12，16

Table 1

Embodiment 2

With reference to Fig. 1, Fig. 2, Fig. 3, Fig. 4 and Fig. 5, a kind of propylene polymerization production data detects and method for diagnosing faults, and described method for diagnosing faults may further comprise the steps:

(1), from the historical data base of DCS database 3 acquisition system just often the data of key variables as training sample TX;

(2), in the pivot analysis module 8 of host computer, residual analysis module 9, pivot analysis variance extraction ratio, residual analysis confidence limit alpha parameter are set respectively, set the sampling period among the DCS;

(3), training sample TX in host computer, data are carried out standardization, make that the average of each variable is 0, variance is 1, obtains input matrix X, adopts following process to finish:

3.1) computation of mean values: $\stackrel{\‾}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{TX}}_i---\left(1\right),$

3.2) the calculating variance: ${\mathrm{\σ}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}({\mathrm{TX}}_i-\stackrel{\‾}{\mathrm{TX}})---\left(2\right),$

3.3) standardization: $X=\frac{\mathrm{TX}-\stackrel{\‾}{\mathrm{TX}}}{{\mathrm{\σ}}_x}---\left(3\right),$

Wherein, N is a number of training, and N is a number of training, and TX is the average of training sample;

(4), carry out pivot analysis and extract major component, adopt the method for covariance svd, adopt following steps to realize:

1. calculate the covariance matrix of X, be designated as ∑ _X

2. to ∑ _XCarry out svd, obtain characteristic root λ ₁, λ ₂..., λ _p, λ wherein ₁〉=λ 2 〉=... 〉=λ p, the characteristic of correspondence vector matrix is U;

3. calculate population variance and each eigenwert corresponding variance contribution rate, adding up from big to small by the variance contribution ratio of each eigenwert reaches set-point (generally getting greater than 80%) up to total variance contribution ratio, and it is k that note is chosen number;

4. the preceding k of selected characteristic vector matrix U is listed as, as transformation matrix T;

5. calculate pivot, calculate pivot F by formula F=T * X;

(5), calculate residual analysis control limit, when insolation level was α, the control limit press following formula (4) calculating:

$Q_{\mathrm{\α}}={\mathrm{\θ}}_1{[\frac{C_{\mathrm{\α}}{h}_0\sqrt{2{\mathrm{\θ}}_2}}{{\mathrm{\θ}}_1}+1+\frac{{\mathrm{\θ}}_2{h}_0(h_0-1)}{{\mathrm{\θ}}_2}]}^{\frac{1}{h_0}}---\left(4\right)$

In the formula:

${\mathrm{\θ}}_i=\underset{j=k+1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\λ}}_j^i,i=\mathrm{1,2,3},h_0=1-\frac{{2\mathrm{\θ}}_1{\mathrm{\θ}}_3}{{3\mathrm{\θ}}_2^2}$

λ _iBe the eigenwert of X covariance matrix, C _αBe that the normal distribution degree of confidence is the statistics of α;

(6), the data of gathering are sent in the DCS real-time data base 5, from the real-time data base of DCS database, obtain up-to-date variable data at each timing cycle as diagnostic data VX; TX and σ that data to be tested VX the time is obtained with training _x ²Carry out standardization, and with the input of the data after the standardization as the pivot analysis module, the transformation matrix T that obtains during with training carries out conversion to input, matrix is input to the residual analysis module after the conversion, calculates the Q statistic of input data, if Q＜Q _α, judge that sample point Q statistics is normal, the process object is normal; If Q＞Q _α, judge that sample point Q statistics is unusual, the process object breaks down.

Described method for diagnosing faults also comprises: (7), regularly the normal point of process status is added among the training set VX, repeat the training process of (3)～(5), so that the model of the residual analysis module 9 that upgrades in time.

In described (6), host computer is passed to the DCS system with fault diagnosis result, and at the control station procedure for displaying state of DCS, by DCS system and fieldbus process status information is delivered to operator station simultaneously and shows.

Claims (8)

1, a kind of propylene polymerization production data detects and fault diagnosis system, comprises the field intelligent instrument, DCS system and the host computer that are connected with propylene polymerization production process, and described DCS system is made of data-interface, control station, database; Intelligence instrument, DCS system, host computer link to each other successively, and it is characterized in that: described host computer comprises:

The standardization module, be used for to the database acquisition system just often the data of key variables carry out standardization, the average of each variable is 0, variance is 1, obtains input matrix X, adopts following process to finish:

1) computation of mean values: $\stackrel{\‾}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{TX}}_i,---\left(1\right)$

2) calculate variance: ${\mathrm{\σ}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}({\mathrm{TX}}_i-\stackrel{\‾}{\mathrm{TX}}),---\left(2\right)$

3) standardization: $X=\frac{\mathrm{TX}-\stackrel{\‾}{\mathrm{TX}}}{{\mathrm{\σ}}_x},---\left(3\right)$

Wherein, TX is a training sample, and N is a number of training, and TX is the average of training sample;

The pivot analysis module is used to carry out pivot analysis and extracts major component, according to the pivot variance extraction ratio that is provided with, adopts the method for covariance svd, adopts following steps to realize:

1. calculate the covariance matrix of X, be designated as ∑ _X

2. to ∑ _XCarry out svd, obtain characteristic root λ ₁, λ ₂..., λ _p, λ wherein ₁〉=λ 2 〉=... 〉=λ p, the characteristic of correspondence vector matrix is U;

3. calculate population variance and each eigenwert corresponding variance contribution rate, adding up from big to small by the variance contribution ratio of each eigenwert reaches set-point up to total variance contribution ratio, and it is k that note is chosen number;

4. the preceding k of selected characteristic vector matrix U is listed as, as transformation matrix T;

5. calculate pivot, calculate pivot F by formula F=T * X;

The residual analysis module is used to calculate residual analysis control limit, and residual analysis confidence limit alpha parameter is set, and it is α that insolation level is set, and the control limit is pressed following formula (4) and calculated:

$Q_{\mathrm{\α}}={\mathrm{\θ}}_1{[\frac{C_{\mathrm{\α}}{h}_0\sqrt{{2\mathrm{\θ}}_2}}{{\mathrm{\θ}}_1}+1+\frac{{\mathrm{\θ}}_2{h}_0(h_0-1)}{{\mathrm{\θ}}_2}]}^{\frac{1}{h_0}}---\left(4\right)$

In the formula:

${\mathrm{\θ}}_i=\underset{j=k+1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\λ}}_j^i,i=\mathrm{1,2,3}$ $h_0=1-\frac{{2\mathrm{\θ}}_1{\mathrm{\θ}}_3}{{3\mathrm{\θ}}_2^2}$

λ _jBe the eigenwert of the covariance matrix of X, C _αBe that the normal distribution degree of confidence is the statistics of α;

Signal acquisition module is used to set the time slot of each sampling, the signal of collection site intelligence instrument;

The diagnostic data determination module, the data that are used for gathering are sent to the DCS real-time data base, from the real-time data base of DCS database, obtain up-to-date variable data as diagnostic data VX at each timing cycle;

Fault diagnosis module is used for TX and σ that diagnostic data VX the time is obtained with training _x ²Carry out standardization, and with the input of the data after the standardization as the pivot analysis module, the transformation matrix T that obtains during with training carries out conversion to input, matrix is input to the residual analysis module after the conversion, calculates the Q statistic of input data, if Q＜Q _α, judge that sample point Q statistics is normal, the process object is normal; If Q＞Q _α, judge that sample point Q statistics is unusual, the process object breaks down.

2, propylene polymerization production data as claimed in claim 1 detects and fault diagnosis system, and it is characterized in that: described host computer also comprises:

The discrimination model update module is used for regularly adding the normal point of process status to training set, outputs to standardization module, pivot analysis module, residual analysis module, and upgrades the model in the residual analysis module.

3, propylene polymerization production data as claimed in claim 1 or 2 detects and fault diagnosis system, and it is characterized in that: described host computer also comprises:

Display module is used for fault diagnosis result is passed to the DCS system as a result, and at the control station procedure for displaying state of DCS, by DCS system and fieldbus process status information is delivered to operator station simultaneously and shows.

4, propylene polymerization production data as claimed in claim 3 detects and fault diagnosis system, and it is characterized in that: described key variables comprise catalyzer flow rate f ₄, cocatalyst flow rate f ₅, three gangs of propylene feed flow rate f ₁, f ₂, f ₃, hydrogen volume concentration v in liquid level l and the still in the still inner fluid temperature T, still inner fluid pressure P, still.

5, the method for diagnosing faults that a kind of usefulness propylene polymerization production data as claimed in claim 1 detects and fault diagnosis system realizes, it is characterized in that: described method for diagnosing faults may further comprise the steps:

1), from the historical data base of DCS database acquisition system just often the data of key variables as training sample TX:

2), in the pivot analysis module of host computer, residual analysis module, pivot analysis variance extraction ratio, residual analysis confidence limit alpha parameter are set respectively, set the sampling period among the DCS;

3), training sample TX in host computer, data are carried out standardization, make that the average of each variable is 0, variance is 1, obtains input matrix X, adopts following process to finish:

3.1) computation of mean values: $\stackrel{\‾}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{TX}}_i---\left(1\right),$

3.2) the calculating variance: ${\mathrm{\σ}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}({\mathrm{TX}}_i-\stackrel{\‾}{\mathrm{TX}})---\left(2\right),$

3.3) standardization: $X=\frac{\mathrm{TX}-\stackrel{\‾}{\mathrm{TX}}}{{\mathrm{\σ}}_x}---\left(3\right),$

Wherein, TX is a training sample, and N is a number of training, and TX is the average of training sample;

4), carry out pivot analysis and extract major component, adopt the method for covariance svd, adopt following steps to realize:

1. calculate the covariance matrix of X, be designated as ∑ _X

2. to ∑ _XCarry out svd, obtain characteristic root λ ₁, λ ₂..., λ _p, λ wherein ₁〉=λ 2 〉=... 〉=λ p, the characteristic of correspondence vector matrix is U;

3. calculate population variance and each eigenwert corresponding variance contribution rate, adding up from big to small by the variance contribution ratio of each eigenwert reaches set-point up to total variance contribution ratio, and it is k that note is chosen number;

4. the preceding k of selected characteristic vector matrix U is listed as, as transformation matrix T;

5. calculate pivot, calculate pivot F by formula F=T * X;

5), calculate residual analysis control limit; When insolation level was α, the control limit was pressed following formula (4) and is calculated:

$Q_{\mathrm{\α}}={\mathrm{\θ}}_1{[\frac{C_{\mathrm{\α}}{h}_0\sqrt{{2\mathrm{\θ}}_2}}{{\mathrm{\θ}}_1}+1+\frac{{\mathrm{\θ}}_2{h}_0(h_0-1)}{{\mathrm{\θ}}_2}]}^{\frac{1}{h_0}}---\left(4\right)$

In the formula:

${\mathrm{\θ}}_i=\underset{j=k+1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\λ}}_j^i,i=\mathrm{1,2,3}$ $h_0=1-\frac{{2\mathrm{\θ}}_1{\mathrm{\θ}}_3}{{3\mathrm{\θ}}_2^2}$

λ _jBe the eigenwert of the covariance matrix of X, C _αBe that the normal distribution degree of confidence is the statistics of α;

6), the data of gathering are sent in the DCS real-time data base, from the real-time data base of DCS database, obtain up-to-date variable data as data to be tested VX at each timing cycle; TX and σ that data to be tested VX the time is obtained with training _x ²Carry out standardization, and with the input of the data after the standardization as the pivot analysis module, the transformation matrix T that obtains during with training carries out conversion to input, matrix is input to the residual analysis module after the conversion, calculates the Q statistic of input data, if Q＜Q _α, judge that sample point Q statistics is normal, the process object is normal; If Q＞Q _α, judge that sample point Q statistics is unusual, the process object breaks down.

6, method for diagnosing faults as claimed in claim 5, it is characterized in that: described method for diagnosing faults also comprises: 7), regularly process status is put normally and added in the training set, repeating step 3)～5 training process) is so that the model of the residual analysis module that upgrades in time.

7, as claim 5 or 6 described method for diagnosing faults, it is characterized in that: in described step 6), host computer is passed to the DCS system with fault diagnosis result, and, by DCS system and fieldbus process status information is delivered to operator station simultaneously and shows at the control station procedure for displaying state of DCS.

8, method for diagnosing faults as claimed in claim 7 is characterized in that: described key variables comprise catalyzer flow rate f ₄, cocatalyst flow rate f ₅, three gangs of propylene feed flow rate f ₁, f ₂, f ₃, hydrogen volume concentration v in liquid level l and the still in the still inner fluid temperature T, still inner fluid pressure P, still.

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