CN109407640B - Dynamic process monitoring method based on dynamic orthogonal component analysis - Google Patents
Dynamic process monitoring method based on dynamic orthogonal component analysis Download PDFInfo
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Abstract
The invention discloses a dynamic process monitoring method based on dynamic orthogonal component analysis, which further considers how to further deeply consider the orthogonal characteristic between the dynamic process monitoring method and delay measurement data when mining potential characteristic components on the basis of a traditional principal component analysis algorithm. Therefore, the method firstly infers a dynamic orthogonal component analysis algorithm and then implements dynamic process monitoring on the basis of the algorithm. Compared with the traditional dynamic process monitoring method, the method of the invention has the effect superior to the traditional PCA or dynamic PCA method in the monitoring effect of the dynamic process. In addition, extra calculation amount is not increased in the two stages of off-line modeling and on-line monitoring of the method. It can be said that the method of the present invention is a more preferred dynamic process monitoring method.
Description
Technical Field
The invention relates to a data-driven fault detection method, in particular to a dynamic process monitoring method based on dynamic orthogonal component analysis.
Background
The purpose of process monitoring is to accurately find faults in time, which is of great significance to guarantee safe production and maintain stable product quality. At present, due to the large-scale construction of modern chemical engineering processes and the wide application of advanced instruments and computer technologies, mass data can be acquired in the production process, and the mainstream implementation technical means of process monitoring is gradually changed from a method based on a mechanism model to a data driving method. The development mode that the sampling data is easy to obtain and the mechanism model is difficult to obtain enables the traditional fault detection method based on the mechanism model to be gradually fallen. In contrast, the data-driven fault detection method does not need a mechanism model and only needs sampling data, and is suitable for monitoring the operation state of the modern industrial process. In essence, the data-driven fault detection method, which aims at mining potential features, is significantly different from the mechanism model-based fault detection method, which aims at generating errors. Developing to date, the field of data-driven fault detection research has emerged a number of feature mining algorithms and a wide variety of modeling strategies. Principal Component Analysis (PCA) is the most widely studied and applied method for feature mining algorithms.
Since the sampling frequency of each variable in the modern industrial process is high, the autocorrelation (or dynamic property) of the sampled data is a common problem. In the most classical processing mode, delay measurement values are introduced into all measurement variables, and then a fault detection model based on PCA is established, so that autocorrelation characteristics of sampled data can be taken into consideration. However, this dynamic PCA method confuses the autocorrelation and cross correlation, and cannot distinguish them for better describing the data characteristics. Generally speaking, when mining the latent feature components of data, if auto-correlation features can be filtered out at the same time, the corresponding process monitoring model may achieve more reliable and effective dynamic process monitoring performance. Taking the PCA algorithm as an example, two problems need to be considered in the process of extracting the potential feature components by PCA: one is that the feature components are orthogonal to each other, and the other is that the aim of mining the potential feature components is to maximize the variance. It can be seen that the classical PCA algorithm fails to take into account the sequence correlation of the samples. If an additional constraint condition is added to filter out the sequence autocorrelation of the sample data based on the PCA algorithm, the extracted feature components may be called dynamic orthogonal components.
Disclosure of Invention
The invention aims to solve the main technical problems that: how to further consider autocorrelation orthogonal constraint conditions in the traditional PCA algorithm and implement dynamic process monitoring on the basis of the autocorrelation orthogonal constraint conditions. Specifically, in the process of extracting the potential characteristic components by the traditional PCA algorithm, the method of the invention adds a constraint condition to require that the extracted potential characteristic components are orthogonal to a matrix formed by the first sampling data of the sample data. On the basis, the extracted dynamic orthogonal components are utilized to establish a corresponding dynamic process monitoring model to implement online fault detection.
The technical scheme adopted by the invention for solving the technical problems is as follows: a dynamic process monitoring method based on dynamic orthogonal component analysis comprises the following steps:
(1) collecting samples in normal operation state of production process to form training data matrix X belonging to Rn×mWherein n is the number of training samples, m is the number of process measurement variables, R is the set of real numbers, R is the number of training samplesn×mRepresenting a matrix of real numbers in dimension n x m.
(2) Standardizing each column vector in the matrix X to obtain a standardized matrix
Wherein xi∈Rm×1For the sample data at the ith sampling instant, the subscript i is 1, 2, …, n, and the superscript T denotes the transpose of the matrix or vector.
The normalization process in step (2) is intended to eliminate the influence of the measurement dimension of each column vector in the training data matrix X, and the specific implementation manner is as follows: the mean value is subtracted from each column vector and divided by its standard deviation.
(3) Sample data x1，x2，…，xn-1The data matrix Y is formed according to the formula shown below:
(4) computing matrix C-MZTAll eigenvalues λ of Z1，λ2，…，λmAnd its corresponding feature vector w1，w2，…，wmWherein Z ═ x3，x4，…，xn]T，M＝I-ZTY(YTZZTY)-1YTZ, the characteristic values being arranged in descending order of magnitude, i.e. λ1≥λ2≥…≥λmThe length of the feature vectors is equal to 1.
(5) The number k of the remaining dynamic orthogonal components is the minimum value satisfying the following conditions:
(6) the feature vector w1，w2，…，wkThe composition matrix W ═ W1，w2，…，wk]And calculating the dynamic orthogonal component S epsilon R according to the formula S ═ ZW(n-2)×k。
The implementation process of the step (4) is actually to solve the following optimization problem:
max wTZTZw
constraint conditions are as follows: w is aTw＝1，wTZTY＝0
Constraint condition w to be satisfied when solving vector wTZTY-0 requires that the resulting component s-Zw is orthogonal to the matrix Y, i.e. sTY is 0. Since the data in matrix Y is composed of samples of the first 2 sampling instants of each sample data in Z, the resulting components exhibit orthogonal properties in time-series correlation. This is also a direct reason why the method of the present invention defines S as a dynamic orthogonal component, and the corresponding algorithm is also called as a dynamic orthogonal analysis algorithm. Compared with the optimization problem related to the traditional PCA algorithm, the method adds a constraint condition wTZTAnd Y is 0, so that the orthogonalization of the extracted components and the time delay measurement sample is ensured.
(7) Solving a regression coefficient matrix B between S and Z by using a least square algorithm (S)TS)-1STZ。
(8) According to the formula
And
respectively calculating the upper control limit D of the monitoring statistics D and QlimAnd QlimAnd the parameter set Θ is reserved as { W, B, D ═ Blim，QlimIs ready for on-line monitoring, wherein
The value of the chi-square distribution with the degree of freedom k under the condition that the confidence coefficient alpha is 99 percent is represented,
the value of the chi-square distribution with the degree of freedom m under the condition that the confidence coefficient a is 99% is represented.
The above steps (1) to (8) are implementation details for establishing a dynamic process monitoring model offline by the method of the present invention, and the following steps (9) to (11) are detailed implementation processes for implementing online fault detection by the method of the present invention.
(9) Collecting data samples x at new sampling instantst∈Rm×1To xtPerforming the same normalization process as the step (2)
(10) According to the formula st＝WxtCalculating the dynamic orthogonal component stAnd calculating e according to the formulat＝xt-BTstModel residual et。
(11) The specific values of the monitoring statistics D and Q are calculated according to the following formula:
D＝st TΛst (3)
Q＝et Tet (4)
in the above formula, matrix Λ ═ STS/(n-3)。
(12) Judging whether the condition D is not more than DlimAnd Q is less than or equal to QlimIs there a If so, the current sample is sampled under normal working conditions; if not, the current sampling data comes from the fault working condition.
Compared with the traditional method, the method has the advantages that:
firstly, the method further considers how to deeply consider the orthogonal characteristic between the potential characteristic component and the delay measurement data when mining the potential characteristic component on the basis of the traditional PCA algorithm. Therefore, the method of the present invention should achieve better monitoring effect on dynamic process than the conventional PCA or dynamic PCA method. In addition, extra calculation amount is not increased in the two stages of off-line modeling and on-line monitoring of the method. It can be said that the method of the present invention is a more preferred dynamic process monitoring method.
Drawings
FIG. 1 is a flow chart of an embodiment of the method of the present invention.
FIG. 2 is a comparison graph of the monitoring details of the inlet temperature fault of the cooling water of the condenser of the TE process.
Detailed Description
The method of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the present invention discloses a dynamic process monitoring method based on dynamic orthogonal component analysis. The following description is given with reference to a specific industrial process example to illustrate the practice of the method of the present invention and its advantages over the prior art methods.
The application object is from the U.S. Tennessee-Ismann (TE) chemical process experiment, and the prototype is a practical process flow of an Ismann chemical production workshop. At present, the TE process has been widely used as a standard experimental platform for fault detection research due to the complexity of the process. The entire TE process includes 22 measured variables, 12 manipulated variables, and 19 constituent measured variables. The collected data is divided into 22 groups, which include 1 group of data sets under normal conditions and 21 groups of fault data. Of these fault data, 16 are known fault types such as changes in cooling water inlet temperature or feed composition, valve sticking, reaction kinetic drift, etc., and 5 are unknown. To monitor the process, 33 process variables as shown in Table 1 were selected, and the specific implementation steps of the present invention are described in detail below in connection with the TE process.
Table 1: the TE process monitors variables.
| Serial number | Description of variables | Serial number | Description of variables | Serial number | Description of variables |
| 1 | Flow rate of material A | 12 | Liquid level of separator | 23 | D feed valve position |
| 2 | Flow rate of material D | 13 | Pressure of separator | 24 | E |
| 3 | Flow rate of material E | 14 | Bottom flow of separator | 25 | A |
| 4 | Total feed flow | 15 | Stripper grade | 26 | A and C feed valve position |
| 5 | Flow rate of circulation | 16 | Stripper pressure | 27 | Compressor cycleValve position |
| 6 | Reactor feed | 17 | Bottom flow of stripping tower | 28 | Evacuation valve position |
| 7 | Reactor pressure | 18 | Stripper temperature | 29 | Separator liquid |
| 8 | Reactor grade | 19 | Stripping tower overhead steam | 30 | Stripper liquid phase valve position |
| 9 | Reactor temperature | 20 | Compressor power | 31 | Stripper steam valve position |
| 10 | Rate of emptying | 21 | Reactor cooling water outlet temperature | 32 | Reactor condensate flow |
| 11 | Separator temperature | 22 | Separator cooling water outlet temperature | 33 | Flow rate of cooling water of condenser |
Firstly, establishing a fault detection model by using 960 sampling data under the normal working condition of a TE process, wherein the fault detection model comprises the following steps:
(1) collecting data samples in normal working condition operation state in the production process to form a training data matrix X belonging to R960×33。
(2) Standardizing each column vector in the matrix X to obtain a standardized matrix
(3) Sample data x1，x2，…，x959The data matrix Y is formed according to the formula shown below:
(4) computing matrix C-MZTAll eigenvalues λ of Z1，λ2，…，λmAnd its corresponding feature vector w1，w2，…，wmWherein Z ═ x3，x4，…，xn]T，M＝I-ZTY(YTZZTY)-1YTZ。
(5) The number k of the remaining dynamic orthogonal components is the minimum value satisfying the following conditions:
(6) the feature vector w1，w2，…，w16The composition matrix W ═ W1，w2，…，w16]And calculating the dynamic orthogonal component S epsilon R according to the formula S ═ ZW958×16。
(7) Solving a regression coefficient matrix B between S and Z by using a least square algorithm (S)TS)-1STZ。
(8) According to the formula
Respectively calculating the upper control limit D of the monitoring statistics D and QlimAnd QlimAnd the parameter set Θ is reserved as { W, B, D ═ Blim，QlimAnd is called for on-line monitoring.
(9) Collecting data samples x at new sampling instantst∈R1×33To xtPerforming the same normalization process as the step (2)
(10) According to the formula st＝WxtCalculating the dynamic orthogonal component stAnd calculating e according to the formulat＝xt-BTstModel residual et。
(11) According to the formula D ═ st TΛstAnd Q ═ et TetAnd calculating specific values of the monitoring statistics D and Q respectively.
(12) Judging whether the condition D is not more than DlimAnd Q is less than or equal to QlimIs there a If so, the current sample is sampled under normal working conditions; if not, the current sampling data comes from the fault working condition.
Finally, the process monitoring details of the method of the present invention and the conventional dynamic PCA method are compared as in fig. 2. As can be seen from FIG. 2, the monitoring effect of the method of the present invention is superior to that of the conventional dynamic PCA method.
The above embodiments are merely illustrative of specific implementations of the present invention and are not intended to limit the present invention. Any modification of the present invention within the spirit of the present invention and the scope of the claims will fall within the scope of the present invention.
Claims (1)
1. A dynamic process monitoring method based on dynamic orthogonal component analysis is characterized by comprising the following steps:
the implementation of the offline modeling phase is as follows:
step (1) collecting samples in the normal running state of the production process to form a training data matrix X belonging to Rn×mWherein n is the number of training samples, m is the number of process measurement variables, R is the set of real numbers, R is the number of training samplesn×mA real number matrix representing dimensions n × m;
step (2) standardizing each column vector in the matrix X to obtain a standardized matrix
Wherein xi∈Rm×1For sample data at the ith sampling moment, the subscript number i is 1, 2, …, n, and the superscript number T represents the transpose of a matrix or a vector;
step (3) sample data x1，x2，…，xn-1The data matrix Y is formed according to the formula shown below:
step (4) calculating matrix C ═ MZTAll eigenvalues λ of Z1，λ2，…，λmAnd its corresponding feature vector w1，w2，…，wmWherein Z ═ x3，x4，…，xn]T，M＝I-ZTY(YTZZTY)-1YTZ, the characteristic values are arranged in descending order according to the numerical valueI.e. λ1≥λ2≥…≥λmThe length of the feature vectors is equal to 1;
the number k of the dynamic orthogonal components reserved in the step (5) is the minimum value meeting the following conditions:
step (6) is to apply the feature vector w1，w2，…，wkThe composition matrix W ═ W1，w2，…，wk]And calculating the dynamic orthogonal component S epsilon R according to the formula S ═ ZW(n-2)×k；
Step (7) solving a regression coefficient matrix B between S and Z by using a least square algorithm (S ═ S)TS)-1STZ；
Step (8) according to the formula
And
respectively calculating the upper control limit D of the monitoring statistics D and QlimAnd QlimAnd the parameter set Θ is reserved as { W, B, D ═ Blim，QlimIs ready for on-line monitoring, wherein
The value of the chi-square distribution with the degree of freedom k under the condition that the confidence coefficient alpha is 99 percent is represented,
expressing the value of chi-square distribution with the degree of freedom m under the condition that the confidence coefficient alpha is 99 percent;
the implementation of the on-line process monitoring phase is as follows:
step (9) collecting data sample x of new sampling timet∈Rm×1To xtThe same standardization as in step (2) was carried outGet reason to
Step (10) according to the formula st＝WxtCalculating the dynamic orthogonal component stAnd according to formula et＝xt-BTstCalculating model residual et；
And (11) calculating specific numerical values of the monitoring statistics D and Q according to the following formula:
D＝st TΛst (3)
Q＝et Tet (4)
in the above formula, matrix Λ ═ STS/(n-3)；
Step (12) of judging whether or not the condition D is satisfiedlimAnd Q is less than or equal to Qlim(ii) a If so, the current sample is sampled under normal working conditions; if not, the current sampling data comes from the fault working condition.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104699077A (en) * | 2015-02-12 | 2015-06-10 | 浙江大学 | Nested iterative fisher discriminant analysis-based fault diagnosis isolation method |
| CN108037668A (en) * | 2017-12-28 | 2018-05-15 | 杭州电子科技大学 | A kind of new Chemical Batch Process modeling and monitoring method |
| CN108508865A (en) * | 2018-03-06 | 2018-09-07 | 宁波大学 | A kind of fault detection method based on distributing OSC-PLS regression models |
| CN108520111A (en) * | 2018-03-06 | 2018-09-11 | 宁波大学 | A kind of flexible measurement method based on orthogonal component optimal selection and optimum regression |
| CN108803520A (en) * | 2018-06-11 | 2018-11-13 | 宁波大学 | A kind of dynamic process monitoring method rejected based on the non-linear autocorrelation of variable |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104699077A (en) * | 2015-02-12 | 2015-06-10 | 浙江大学 | Nested iterative fisher discriminant analysis-based fault diagnosis isolation method |
| CN108037668A (en) * | 2017-12-28 | 2018-05-15 | 杭州电子科技大学 | A kind of new Chemical Batch Process modeling and monitoring method |
| CN108508865A (en) * | 2018-03-06 | 2018-09-07 | 宁波大学 | A kind of fault detection method based on distributing OSC-PLS regression models |
| CN108520111A (en) * | 2018-03-06 | 2018-09-11 | 宁波大学 | A kind of flexible measurement method based on orthogonal component optimal selection and optimum regression |
| CN108803520A (en) * | 2018-06-11 | 2018-11-13 | 宁波大学 | A kind of dynamic process monitoring method rejected based on the non-linear autocorrelation of variable |
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