CN201035377Y - Fault Diagnosis Device for Melt Index Detection in Propylene Polymerization Production - Google Patents

Abstract

A fault diagnosis device for melt index detection in propylene polymerization production, including on-site intelligent instruments connected to the propylene polymerization production process, a DCS system and a host computer, the DCS system is composed of a data interface, a control station, and a database; intelligent The instrument, the DCS system, and the upper computer are connected in sequence, and the upper computer includes a standardized processing module, an independent component analysis module, a support vector machine classifier function module, a signal acquisition module, a data determination module to be diagnosed and a fault judgment module. The utility model provides a fault diagnosis device for melting index detection in propylene polymerization production which is relatively convenient to solve, can obtain good diagnosis effect and effectively reduces false alarm rate.

Description

Fault Diagnosis Device for Melt Index Detection in Propylene Polymerization Production

(1) Technical field

The utility model relates to the field of industrial process fault diagnosis, in particular to a fault diagnosis device for detecting melt index in propylene polymerization production.

(2) Background technology

Polypropylene is a synthetic resin mainly polymerized from propylene monomer, and is an important product in the plastics industry. Among the polyolefin resins in my country, it has become the third largest plastic after polyethylene and polyvinyl chloride. In the production process of polypropylene, melt index (MI) is an important index reflecting product quality and an important basis for production quality control and brand switching. However, MI can only be detected offline. Generally, offline analysis takes at least 2 hours, which is costly and time-consuming. Especially during the 2 hours of offline analysis, it will not be possible to know the status of the polypropylene production process in time. Therefore, it is very important to select the easily measurable variable closely related to the melt index as the secondary variable, analyze the melt index, and check whether the production process is normal for the propylene polymerization production process.

(3) Contents of the invention

In order to overcome the disadvantages of the existing fault diagnosis device for melt index detection in propylene polymerization production, which are troublesome to solve, difficult to obtain a better diagnosis effect, and high false alarm rate, the utility model provides a relatively convenient solution that can obtain a good Fault diagnosis device for melt index detection in propylene polymerization production with diagnostic effect and effective reduction of false alarm rate.

The technical scheme that the utility model solves its technical problem adopts is:

A fault diagnosis device for melt index detection in propylene polymerization production, including on-site intelligent instruments connected to the propylene polymerization production process, a DCS system and a host computer, the DCS system is composed of a data interface, a control station, and a database; intelligent instruments, The DCS system and the host computer are connected in turn, and the host computer includes:

The standardization processing module is used to standardize the data of key variables when the acquisition system is normal in the database. The mean value of each variable is 0 and the variance is 1 to obtain the input matrix X. The following process is used to complete:

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

2) Calculate the 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: $x=\frac{\mathrm{TX}-\stackrel{\‾}{\mathrm{TX}}}{{\mathrm{\σ}}_x},---\left(3\right)$

为训练样本的均值；Among them, TX is the training sample, N is the number of training samples,

is the mean of the training samples;

The independent component analysis module is used to calculate the unmixing matrix W by using the fast ICA algorithm based on fixed-point iteration according to the number of independent components. The specific steps are as follows:

①Randomly select the initial weight vector w _i with a norm of 1, if i≥2, then $w_i=w_i-W_{i-1}{W}_{i-1}^T{w}_i,$ Where W _i-1 =[w ₁ w ₂ L w _i-1 ], i=1,...,m;

② Iteratively update w _i : $w_i^{+}=\mathrm{E.}\left\{\mathrm{x\; g}\left(w_i^{T}x\right)\right\}-\mathrm{E.}\left\{g^{\′}\left(w_i^{T}x\right)\right\}{w}_i,$ Where w _i ⁺ represents the updated weight vector, E is the mathematical expectation, g represents a function of the form g(x)=xexp(-x ² /2), and g' is the reciprocal of g;

③Standardized processing $w_i=w_i^{+}/\left|\right|w_i^{+}\left|\right|,$ Where ‖w _i ⁺ ‖ represents the norm of w _i ⁺ ;

④ If not converged, return to ②, otherwise iterate until i=m;

⑤ When the dot product between the updated w _i and the original w _i is 1, it is judged as convergent;

⑥ Calculate the independent components: S=WX; wherein, S is the independent component matrix, W is the unmixing matrix, and X is the input matrix;

The support vector machine classifier function module is used to calculate the kernel function according to the support vector machine kernel parameters and confidence probability, and adopts the radial basis function K( _xi , x)=exp(-‖xx _i ‖/σ ² ), the The training process is transformed into a quadratic programming problem:

$\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}\mathrm{<span\; class="notranslate">\; K</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

Thus, the classification function is obtained, that is, the symbolic function f(x) of the following function:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\mathrm{<span\; class="notranslate">\; K</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$

Among them, α _i (i=1,...,N) is the Lagrangian multiplier, x _i (i=1,...,N) is the input vector, y is the output variable, ω is the method of SVM hyperplane Vector, determines the direction of the hyperplane, b is the parameter that determines the position of the hyperplane, and δ is the kernel parameter;

The signal acquisition module is used to set the time gap of each sampling and collect the signal of the on-site smart instrument;

The module for determining the data to be diagnosed is used to transmit the collected data to the DCS real-time database, and obtain the latest variable data from the real-time database of the DCS database in each timing cycle as the data to be diagnosed VX;

The fault diagnosis module is used to obtain when the data VX to be detected is used for training and σ _x ² for normalization processing, and the standardized processing data as the input of the independent component analysis module, transform the input with the unmixing matrix W obtained during training, and the transformed matrix is input to the support vector machine classifier function module, Substitute the input data into the discriminant function f(x) obtained by training, and calculate the value of the discriminant function. When f(x)>=0, the data sample is in a normal state; when f(x)<0, it is in an abnormal state;

The on-site intelligent instrument is connected to the signal acquisition unit for data, the signal acquisition unit is connected to the data determination module to be diagnosed, the data determination module to be diagnosed is connected to the fault diagnosis module, the standardized processing module is connected to the database data, and the standardized The processing module is connected with the independent component analysis module, and the independent component analysis module is connected with the support vector machine classifier function module, and the support vector machine classifier function module is connected with the fault diagnosis module.

As a preferred scheme: the host computer also includes: a discriminant model update module, which is used to regularly add the normal points of the process state to the training set VX, and output to the standardization processing module, independent component analysis module, support vector machine A classifier function module, and update the classification model in the support vector machine classifier function module, the discriminant model updating module is connected with the support vector machine classifier function module.

As another preferred solution: the host computer also includes: a result display module, which is used to transmit the fault diagnosis result to the DCS system, and display the process status at the control station of the DCS; The status information is transmitted to the field operation station for display; the output of the fault diagnosis module is connected to the result display module.

As another preferred solution: the key variables include main catalyst flow rate f ₄ , co-catalyst flow rate f ₅ , three propylene feed flow rates (f ₁ , f ₂ , f ₃ ), fluid temperature T in the tank , Fluid pressure P in the kettle, liquid level l in the kettle and hydrogen volume concentration α in the kettle.

The technical idea of the utility model is: the traditional multivariable statistical monitoring fault diagnosis method mostly adopts principal component analysis and partial least squares analysis, and these methods require variables to obey normal distribution while assuming that the variables satisfy independent and identical distribution, and Only the second-order statistical information is used, and it is often difficult to obtain better fault diagnosis results.

The utility model utilizes industrial actual measurement data and adopts a statistical method to diagnose faults, avoids complex mechanism analysis, and is relatively convenient to solve.

Blind source signal analysis (Independent Component Analysis ICA) is a signal processing method based on high-order statistics. It is used for process data analysis and processing in the process industry, which can make more effective use of the probability and statistics characteristics of variables. Decompose the observed variables in a meaningful way to obtain the internal driving information source of the process, so as to describe the characteristics of the process more essentially, and make the monitoring and fault diagnosis of the process more accurate and reliable.

The beneficial effects of the utility model are mainly manifested in: the independent component analysis's decorrelation ability and the support vector machine's multivariable nonlinear mapping ability and strong generalization ability are well combined, and the respective advantages are brought into play, so that the failure The diagnosis is more reliable and effective, which can better guide production and improve production efficiency.

(4) Description of drawings

Fig. 1 is the hardware structural diagram of the fault diagnosis device proposed by the utility model;

Fig. 2 is a functional block diagram of the fault diagnosis device proposed by the utility model;

Fig. 3 is a schematic diagram of the production process of polypropylene;

Figure 4 is a detection effect diagram of ICA-SVM;

Fig. 5 is a functional block diagram of the upper computer of the utility model.

(5) Specific implementation methods

Below in conjunction with accompanying drawing, the utility model is further described.

Referring to Fig. 1, Fig. 2, Fig. 3, Fig. 4 and Fig. 5, a fault diagnosis device for melt index detection in propylene polymerization production, including on-site intelligent instrument 2, DCS system and host computer 6 connected with the propylene polymerization production process, Described DCS system is made up of data interface 3, control station 4, database 5; Smart instrument 2, DCS system, host computer 6 are connected successively by field bus, and described host computer 6 comprises:

The standardization processing module 7 is used to standardize the data of the key variables when the acquisition system is normal in the database. The mean value of each variable is 0 and the variance is 1 to obtain the input matrix X. The following process is used to complete:

1) Calculate the mean: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i,---\left(1\right)$

2) Calculate the variance: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}}),---\left(2\right)$

3) Standardization: $x=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x},---\left(3\right)$

为训练样本的均值；Among them, TX is the training sample, N is the number of training samples,

is the mean of the training samples;

The independent component analysis module 8 is used to calculate the unmixing matrix W by using the fast ICA algorithm based on fixed-point iteration according to the number of independent components. The specific steps are as follows:

①Randomly select the initial weight vector w _i with a norm of 1, if i≥2, then $w_i=w_i-W_{i-1}{W}_{i-1}^T{w}_i,$ Where W _i-1 =[w ₁ w ₂ L w _i-1 ], i=1,...,m;

② Iteratively update w _i : $w_i^{+}=\mathrm{E.}\left\{\mathrm{x\; g}\left(w_i^{T}x\right)\right\}-\mathrm{E.}\left\{g^{\&prime;}\left(w_i^{T}x\right)\right\}{w}_i,$ Where w _i ⁺ represents the updated weight vector, E is the mathematical expectation, g represents a function of the form g(x)=xexp(-x ² /2), and g' is the reciprocal of g;

③Standardized processing $w_i=w_i^{+}/\left|\right|w_i^{+}\left|\right|,$ Where ‖w _i ⁺ ‖ represents the norm of w _i ⁺ ;

④ If not converged, return to ②, otherwise iterate until i=m;

⑤ When the dot product between the updated w _i and the original w _i is 1, it is judged as convergent;

⑥ Calculate the independent components: S=WX; wherein, S is the independent component matrix, W is the unmixing matrix, and X is the input matrix;

The support vector machine classifier function module 9 is used to calculate the kernel function according to the support vector machine kernel parameters and confidence probability, using the radial basis function K(xi _, x)=exp(-‖xx _i ‖/σ ² ), Transform the training process into a quadratic programming problem:

$\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}\mathrm{<span\; class="notranslate">\; K</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

The classification function is thus obtained, that is, the sign function of the following function:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\mathrm{<span\; class="notranslate">\; K</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

Among them, α _i (i=1,...,N) is the Lagrangian multiplier, x _i (i=1,...,N) is the input vector, y is the output variable, ω is the method of SVM hyperplane Vector, determines the direction of the hyperplane, b is the parameter that determines the position of the hyperplane, and δ is the kernel parameter;

The signal collection module 10 is used to set the time gap of each sampling, and collects the signal of the on-site smart instrument;

The data to be diagnosed determination module 11 is used to transmit the collected data to the DCS real-time database, and obtain the latest variable data as the data to be diagnosed VX from the real-time database of the DCS database in each timing cycle;

和σ_x ²进行标准化处理，并将标准化处理后的数据作为独立成分分析模块的输入，用训练时得到的解混矩阵W对输入进行变换，变换后矩阵输入到支持向量机分类器功能模块，将输入数据代入训练得到的判别函数f(x)，计算判别函数值，当f(x)＞＝0，数据样本处于正常状态；当f(x)＜0时，处于异常状态；Fault diagnosis module 12, used to obtain when training data VX to be detected

and σ _x ² for normalization processing, and the standardized processing data as the input of the independent component analysis module, transform the input with the unmixing matrix W obtained during training, and the transformed matrix is input to the support vector machine classifier function module, Substitute the input data into the discriminant function f(x) obtained by training, and calculate the value of the discriminant function. When f(x)>=0, the data sample is in a normal state; when f(x)<0, it is in an abnormal state;

The on-site smart instrument 2 is connected to the data of the signal acquisition unit 10, the signal acquisition unit 10 is connected to the data determination module 11 to be diagnosed, the data determination module 11 to be diagnosed is connected to the fault diagnosis module 12, and the standardized processing module 7 is connected to the fault diagnosis module 12. Database 5 data connection, described standardized processing module 7 is connected with independent component analysis module 8, and described independent component analysis module 8 is connected with support vector machine classifier functional module 9, and described support vector machine classifier functional module 9 is connected with fault diagnosis Module 12 Connections

Described upper computer also comprises: discriminant model update module 13, is used for regularly adding the normal point of process state in the training set VX, outputs to standardization processing module, independent component analysis module, support vector machine classifier function module, and The classification model in the support vector machine classifier function module is updated, and the discrimination model update module 13 is connected with the support vector machine classifier function module 9 .

The host computer also includes: a result display module 14, which is used to transmit the fault diagnosis result to the DCS system, and display the process status at the control station of the DCS, and simultaneously transmit the process status information to the field operation station through the DCS system and the field bus For displaying, the output of the fault diagnosis module 12 is connected to the result display module 14 .

The key variables mentioned include main catalyst flow rate f ₄ , co-catalyst flow rate f ₅ , three propylene feed flow rates (f ₁ , f ₂ , f ₃ ), fluid temperature T in the tank, fluid pressure P in the tank, The internal liquid level l and the hydrogen volume concentration α in the kettle.

The hardware structural diagram of the industrial process fault diagnosis device described in the utility model is as shown in accompanying drawing 1, and the core of described fault diagnosis device is comprised of standardization module 7, independent component analysis module 8, support vector machine classifier module 9 etc. three The upper computer 6 is composed of a large function module and a man-machine interface, and also includes: field intelligent instrument 2, DCS system and field bus. The DCS system is composed of a data interface 3, a control station 4, and a database 5; the propylene polymerization production process 1, the smart instrument 2, the DCS system, and the host computer 6 are sequentially connected through a field bus to realize the upload and release of information flow. The fault diagnosis system runs on the host computer 6, which can conveniently exchange information with the underlying system and respond to system faults in a timely manner.

The functional module diagram of the fault diagnosis device described in the utility model is shown in Figure 2, mainly including three major functional modules such as a standardized processing module 7, an independent component analysis module 8, and a support vector machine classifier module 9.

The fault diagnosis method described in the utility model is implemented according to the following steps:

The fault diagnosis method is implemented according to the following steps:

1. From the historical database of the DCS database 5, collect the data of the following nine variables when the system is normal as the training sample TX: main catalyst flow rate f ₄ , auxiliary catalyst flow rate f ₅ , three propylene feed flow rates (f ₁ , f ₂ , f ₃ ) Fluid temperature T in the kettle, fluid pressure P in the kettle, liquid level l in the kettle, and hydrogen volume concentration α in the kettle;

2. In the independent component analysis module 8 and the support vector machine classifier module 9 of the host computer 6, parameters such as the number of independent components, support vector machine kernel parameters and confidence probability are set respectively, and the sampling period in the DCS is set;

3. The training sample TX is sequentially processed in the host computer 6 through standardization processing 7, independent component analysis 8, support vector machine 9 and other modules, and the following steps are used to complete the training of the fault diagnosis system in the host computer 6;

1) In the standardization processing function module 7 of the host computer 6, standardization processing is performed on the data so that the mean value of each variable is 0 and the variance is 1, and the input matrix X is obtained. This is done using the following process:

① Calculate the mean: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

② Calculate the variance: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}})---\left(2\right)$

③Standardization: $x=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x}---\left(3\right)$

Where N is the number of training samples, N is the number of training samples, is the mean of the training samples.

The normalization processing performed by the normalization processing function module 7 of the upper computer 6 can eliminate the influence of each variable due to different dimensions.

2) In the independent component analysis module 8 of the host computer 6, the independent component analysis is performed, and the unmixing matrix W is calculated by using a fast ICA algorithm based on fixed-point iteration. The specific implementation steps are as follows:

①Randomly select the initial weight vector w _i with a norm of 1, if i≥2, then $w_i=w_i-W_{i-1}{W}_{i-1}^T{w}_i,$ Where W _i-1 =[w ₁ w ₂ L w _i-1 ], i=1,...,m;

② Iteratively update w _i : $w_i^{+}=\mathrm{E.}\left\{\mathrm{x\; g}\left(w_i^{T}x\right)\right\}-\mathrm{E.}\left\{g^{\&prime;}\left(w_i^{T}x\right)\right\}{w}_i,$ Where w _i ⁺ represents the updated weight vector, E is the mathematical expectation, g represents a function of the form g(x)=xexp(-x ² /2), and g' is the reciprocal of g;

③Standardized processing $w_i=w_i^{+}/\left|\right|w_i^{+}\left|\right|,$ Where ‖w _i ⁺ ‖ represents the norm of w _i ⁺ ;

④ If not converged, return to ②, otherwise iterate until i=m;

⑤ When the dot product between the updated w _i and the original w _i is 1, it is judged as convergent;

⑥ Calculate the independent components: S=WX; wherein, S is the independent component matrix, W is the unmixing matrix, and X is the input matrix;

3) Training the classification model of the support vector machine classifier function module 9 in the host computer 6 .

The kernel function of the support vector machine classifier functional module 9 in the host computer 6 adopts the radial basis function K(xi, x)=exp(-‖xx _i ‖/σ ² ), and the training process is transformed into the following Quadratic programming solver problem:

$\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}\mathrm{<span\; class="notranslate">\; K</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

The classification function is thus obtained, that is, the sign function of the following function:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\mathrm{<span\; class="notranslate">\; K</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

Among them, α _i (i=1,...,N) is the Lagrangian multiplier, x _i (i=1,...,N) is the input vector, y is the output variable, ω is the method of SVM hyperplane Vector, determines the direction of the hyperplane, b is the parameter that determines the position of the hyperplane, and δ is the kernel parameter;

Definition When f(x)>=0, the data sample is in a normal state; when f(x)<0, it is in an abnormal state.

Based on statistical learning theory, support vector machine adopts the criterion of structural risk minimization, which solves the problems of small samples, local minimum points, high dimensionality, etc., and can improve the classification accuracy when used in classification problems.

4. The system starts to operate:

1) Use a timer to set the time interval for each sampling;

2) The on-site smart instrument 2 detects the process data and transmits it to the real-time database of the DCS database 5;

3) The host computer 6 obtains the latest variable data from the real-time database of the DCS database 5 in each timing cycle as the data VX to be diagnosed;

和σ_x ²进行标准化处理，并将标准化处理后的数据作为独立成分分析模块8的输入；4) The data VX to be detected, in the standardized processing function module 7 of the host computer 6, obtain during training

and σ _x ² are standardized, and the standardized data is used as the input of the independent component analysis module 8;

5) The independent component analysis module 8 in the host computer 6 transforms the input with the unmixing matrix W obtained during training, and the converted matrix is input to the support vector machine classifier function module 9 in the host computer 6;

6) The support vector machine classifier module 9 in the host computer 6 substitutes the input data into the discriminant function obtained by training, calculates the discriminant function value, and discriminates and displays the state of the process on the man-machine interface of the host computer 6;

7) The upper computer 6 transmits the fault diagnosis result to the DCS, and displays the process status at the control station 4 of the DCS, and at the same time transmits the process status information to the field operation station for display through the DCS system and field bus, so that the field operators can respond in time .

5. Classifier model update

During the system commissioning process, regularly add the points with normal process status to the training set TX, repeat the training process of step 3, so as to update the classification model in the support vector machine classifier 9 of the host computer 6 in time, and keep the classifier model It has a good classification effect.

A specific embodiment of the utility model will be described in detail below.

Take the actual industrial production of polypropylene production HYPOL process as an example. Figure 3 shows a typical Hypol continuous stirred tank (CSTR) process flow diagram for producing polypropylene. The first two tanks are CSTR reactors, and the last two tanks are fluidized bed reactors (FBR). The nine easily measurable operational variables of the main catalyst flow rate, the auxiliary catalyst flow rate, the three propylene feed flow rates, the fluid temperature in the kettle, the fluid pressure in the kettle, the liquid level in the kettle, and the hydrogen volume concentration in the kettle are selected as the input of the model. The data of these nine parameters are obtained from the DCS system in the production process as training samples, of which 50 normal samples are used as training sets, and the other 22 sample points are used as test set data to verify the diagnosis effect. The number of independent components extracted by ICA is 7, the kernel parameter of support vector machine is 5, the confidence probability is 0.98, and the sampling period is 2 hours. Figure 4 is the detection effect diagram of ICA-SVM, in which only the distribution of the first two independent components is drawn. Table 1 lists the actual fault points in the test set and the fault points detected by this system. It can be seen that only the fault point No. 3 is missed, and the false positive rate is 0. Obviously, this system has high diagnostic accuracy.

Actual point of failure 1, 2, 3, 10, 12, 15, 16 Detect failure points 1, 2, 10, 12, 15, 16

Table 1.

The above-described embodiments are used to explain the utility model, rather than to limit the utility model. Within the spirit of the utility model and the protection scope of the claims, any amendments and changes made to the utility model all fall into the scope of the utility model. protected range.

Claims (3)

1. A fault diagnosis device for detecting melt index in propylene polymerization production comprises an on-site intelligent instrument, a DCS system and an upper computer which are connected with the propylene polymerization production process, wherein the DCS system consists of a data interface, a control station and a database; intelligent instrument, DCS system, host computer link to each other in proper order, its characterized in that: the host computer include:

the standardization processing module is used for standardizing the data of the key variables in the normal acquisition system of the database;

the independent component analysis module is used for calculating the unmixing matrix W by adopting a fast ICA algorithm based on fixed point iteration according to the number of independent components;

for calculating kernel function according to kernel parameters and confidence probability of support vector machine, adopting radial basis function K (x) _i ，x)＝exp(-‖x-x _i ‖/σ ² ) Will trainA support vector machine classifier functional module for solving a problem in a process of quadratic programming;

the signal acquisition module is used for setting a time gap of each sampling and acquiring a signal of the on-site intelligent instrument;

the data determining module to be diagnosed is used for transmitting the acquired data to the DCS real-time database, obtaining the latest variable data from the real-time database of the DCS database at each timing period as the data VX to be diagnosed, and is used for training the data VX to be detected

And σ _x ² Carrying out standardization processing, using the standardized data as the input of an independent component analysis module, transforming the input by using a de-mixing matrix W obtained in training, inputting the transformed matrix into a support vector machine classifier function module, substituting the input data into a discriminant function f (x) obtained in training, calculating a discriminant function value, and when f is the discriminant function value _i (x) > = O, data sample is in normal state: when f (x) < 0, the fault diagnosis module is in an abnormal state;

the field intelligent instrument is in data connection with the signal acquisition unit, the signal acquisition unit is connected with the data determination module to be diagnosed, the data determination module to be diagnosed is connected with the fault diagnosis module, the standardization processing module is in data connection with the database, the standardization processing module is connected with the independent component analysis module, the independent component analysis module is connected with the support vector machine classifier function module, and the support vector machine classifier function module is connected with the fault diagnosis module.

2. The failure diagnosis apparatus for detecting a melt index in the polymerization production of propylene according to claim 1, wherein: the host computer still include:

the discrimination model updating module is used for periodically adding points with normal process states into the training set VX, outputting the points to the standardization processing module, the independent component analysis module and the support vector machine classifier functional module, and updating the classification model in the support vector machine classifier functional module;

and the discrimination model updating module is connected with the support vector machine classifier functional module.

3. The failure diagnosis apparatus for melt index detection in propylene polymerization production as claimed in claim 1 or 2, wherein: the host computer still include:

a result display module used for transmitting the fault diagnosis result to the DCS system, displaying the process state at the control station of the DCS, and simultaneously transmitting the process state information to the field operation station for displaying through the DCS system and the field bus;

and the output of the fault diagnosis module is connected with the result display module.

Images