CN110647922B - Layered non-Gaussian process monitoring method based on public and special feature extraction - Google Patents

Abstract

The invention discloses a layered non-Gaussian process monitoring method based on public and special feature extraction. Dividing training data into a plurality of modes, and obtaining high-order public characteristics of the training data by applying a plurality of weight vectors, component vectors and other characteristic quantities in each mode; obtaining low-order public features of the training data by applying a plurality of weight vectors, component vectors and other feature quantities in the remaining low-order modes; constructing statistical limits and statistics in the public subspace according to the public characteristics, and carrying out fault detection; and constructing statistical limits and statistics in the remaining unique subspaces to detect faults. The method is superior to other traditional methods in multi-modal non-Gaussian process fault detection, can extract the special characteristics of the multi-modal and can also extract the common characteristics, and considers the mutual connection of the characteristics and the commonality among different modes, so that the multi-modal process monitoring is more effective.

Description

Layered non-Gaussian process monitoring method based on public and special feature extraction

Technical Field

The invention belongs to the field of industrial process system engineering, and relates to a multi-mode fault monitoring method applied to complex industrial processes such as chemical production and the like.

Background

The complexity of the complex industrial process is reflected in that the process of the modern industrial production generates a large amount of data, and the process data generally have different process characteristics and are not clear in distribution characteristics; the industrial process has a plurality of units, and the data has a mechanism relation; moreover, the traditional method mostly assumes that the chemical process is operated under a single and stable operation condition, but actually the plant-level data is multi-modal, so how to better monitor the multi-modal problem is very worthy of study.

In the past, a great deal of research has been conducted by researchers on advanced data classification and information fusion methods, so that the new measurement data can be accurately distributed to actual operation modes, and the monitoring results can be displayed more accurately. It is noted that there are some process monitoring methods that study global process monitoring methods by using different patterns of data simultaneously, such as support vector data description, k-nearest neighbor based methods, multi-block partial least squares. However, most of these methods related to multi-modal process monitoring can only acquire the unique features of each mode data, and ignore the common features among each mode data. In actual monitoring, it would be beneficial to monitor multimodal processes if the correlation of characteristics and commonalities between different modes could be obtained in advance. Some researchers have proposed a two-step model framework, the first step being the need to develop separate process monitoring models, and the second step being the examination of similarities between them to obtain models representing common features. There have also been some studies attempting to divide the data of each mode into common and unique parts by using the data of multiple modes simultaneously, and although this method has been further extended to the monitoring of the transition process between modes, this method can be used simply for the case of calculating both modes. Moreover, the common features and characteristic properties between different modes are not clearly explained in the geometrical relationship, so that the method has no way to carry out deeper research on the multimode process. A two-step multi-set principal component analysis method has also been proposed to obtain common basis vectors for multiple sets of data. The scholars propose a multimode process monitoring method based on a least square subspace based on the obtained common base vector, although the multimode process monitoring method also successfully extends to the conversion process between monitoring modes, the common base vector does not span any subspace of subset data and only serves as an auxiliary vector to improve the correlation of a cross set to the maximum extent, and therefore, the common characteristics of multimode data cannot be accurately obtained by directly mapping the data to the subspace. Therefore, how to extract the relationship among the multi-modal data and apply different models to the closely-connected multi-modal data becomes an important problem for fault monitoring.

Disclosure of Invention

In order to overcome the problems that in the multi-mode monitoring process, the traditional multi-mode process fault monitoring is carried out, only the specific characteristics of each mode data are developed, and the mutual connection of the characteristics and the commonality of different modes is neglected, the invention aims to provide a layered non-Gaussian process monitoring method based on common and specific characteristic extraction.

The invention seeks to simultaneously construct process monitoring models from all mode data, obtain a common subspace by capturing common characteristics behind different modes, obtain an independent subspace reflecting the characteristic characteristics of each mode, and then carry out fault detection method research on each subspace.

The purpose of the invention is realized by the following technical scheme:

firstly, the classified multi-modal data are classified to obtain common characteristics through weight vector parameters and weight vector scores, and the common characteristics are utilized to monitor faults. And introducing a double-layer non-Gaussian monitoring method aiming at the residual characteristic features to monitor the faults.

The specific method comprises the following steps:

1) Acquiring data of an input variable x and an output variable y of the industrial production process as training data through a sensor under the condition that no fault is known in the industrial production process;

under the condition that the industrial production process needs to detect faults, acquiring an input variable x of the industrial production process on line through a sensor _test And an output variable y _test The data to be detected is used as the data to be detected;

2) Classifying the training data by adopting a fault detection method of a layered non-Gaussian algorithm based on sample multi-modal classification to obtain classified training data;

3) Aiming at the classified training data, each classification corresponds to a mode, an optimization objective function is established in each mode by applying a plurality of weight vectors, component vectors and other characteristic quantities, common characteristics of the classified training data and the data to be detected are obtained, according to the common characteristics, the step iii) in the industrial production process fault monitoring method based on the hierarchical non-Gaussian monitoring algorithm is adopted to extract the training data of the non-Gaussian part with the common characteristics in each mode, and the statistical limit of the training data of the non-Gaussian part with the common characteristics and the statistical quantity of the data to be detected are processed;

4) After training data of a non-Gaussian part with common characteristics are obtained, extracting the common characteristics of the remaining Gaussian part data, establishing an optimization objective function by using a plurality of weight vectors, component vectors and other characteristic quantities in each mode for the Gaussian part data to obtain the common characteristics of the Gaussian part data, and combining a partial least square method according to the common characteristics to obtain the statistical limit of the training data of the Gaussian part with the common characteristics and the statistical quantity of data to be detected;

5) After Gaussian part data with common characteristics and non-Gaussian part data with common characteristics are extracted, the rest data are data with characteristic characteristics, and according to the data with characteristic characteristics, the iv) step in the industrial production process fault monitoring method based on the hierarchical non-Gaussian monitoring algorithm is adopted to obtain the statistical limit of training data with characteristic characteristics and the statistical quantity of data to be detected;

6) And classifying the data to be detected in an online identification mode to obtain the monitored and classified data to be detected.

The input variable x is, for example, a process variable in an industrial process.

The output variable y is, for example, a quality variable in an industrial process.

In the step 3), the concrete steps are as follows:

3.a) all the input variables X of the industrial production process are formed into an input variable data set X, wherein X belongs to R ^n×M·m Carrying out standardization processing on each input variable X in an input variable data set X, wherein n is the number of sampling values in the input variable X, the input variable X is composed of a series of sampling values, M is the number of the input variables, and M is the classification number of training data, namely the mode number; outputting the variable data set as Y in the same way;

3.b) the classified input variable data is expressed as: x = [ X = ₍₁₎ X ₍₂₎ ... X _(M) ]The output variable data is expressed as：Y＝[Y ₍₁₎ Y ₍₂₎ ... Y _(M) ]；X ₍₁₎ X ₍₂₎ ... X _(M) Input variable data respectively expressed as first to Mth classifications, Y ₍₁₎ Y ₍₂₎ ... Y _(M) Output variable data respectively expressed as first to Mth classifications;

3.c) in this way, the component variable t of the input variable x for each modality in the data is obtained using the following formula _c And a component vector u of an output variable y of each modality _c ：

Wherein p is _c And q is _c Respectively single input and output common weight vectors; α, β are input and output component weight vector parameters for combining M pieces of modal data, α = [ α ] ₍₁₎ α ₍₂₎ ... α _(M) ]，α ₍₁₎ α ₍₂₎ ... α _(M) Expressed as input component weight vector parameters of first to Mth classes, respectively, β = [ β ] ₍₁₎ β ₍₂₎ ... β _(M) ]，β ₍₁₎ β ₍₂₎ ... β _(M) Output component weight vector parameters of a first classification to an Mth classification are respectively expressed, wherein i represents an ith mode;

establishing a first objective function as:

max{I(t _c ,u _c )}

s.t.||p _c ||＝||q _c ||＝||α||＝||β||＝1

wherein, I (t) _c ,u _c ) Component variable t being input variable x _c And component vector u of output variable y _c Mutual information value between them, | | | | represents modulus;

3.d) using iterative algorithm to obtain input and output common weight vector p _c 、q _c And input and output component weight vector parameters alpha and beta, and then processing to obtain non-Gaussian partial data with common features, which is expressed as

And processed by training data for non-Gaussian portions having a common characteristic to result in->

Statistical limit for statistics>

3.e) pre-multiplying the input variable of the data to be detected by p _c Obtaining a first fault estimation vector of the data to be detected, and obtaining the data to be detected by using the first fault estimation vector

Statistics are obtained.

The step 3.d) uses the following iterative algorithm to solve and obtain the input and output public weight vector p _c 、q _c And input and output component weight vector parameters α, β:

(3.d.1) randomly initializing input and output component weight vector parameters alpha and beta;

(3.d.2) substituting the input and output component weight vector parameters alpha and beta into the classified input variable data and the classified output variable data to obtain weighted input data

And output data->

(3.d.3) solving input and output public weight vectors by adopting a fault monitoring method in the industrial production process based on a layered non-Gaussian monitoring algorithm;

(3.d.4) inputting and outputting the public weight vector p obtained in the last step _c And q is _c Brought into the first object boxCounting;

(3.d.5) solving input and output component weight vector parameters alpha and beta by using a particle swarm algorithm;

(3.d.6) repeating the steps (2) - (6) until α, β and p _c ，q _c Convergence, i.e. the four values are all less than the respective preset threshold.

Step 4), removing the data of the non-gaussian part obtained in step 3) from the training data, and remaining the training data of the gaussian part, and then the specific steps are as follows:

4.a) the gaussian data for the classified input variables is represented as: x _G ＝[X _G(1) X _G(2) ... X _G(M) ]The gaussian data for the output variable is expressed as: y is _G ＝[Y _G(1) Y _G(2) ... Y _G(M) ]；X _G(1) X _G(2) ... X _G(M) Gaussian data, Y, respectively expressed as input variables of the first to Mth classifications _G(1) Y _G(2) ... Y _G(M) Gaussian data respectively expressed as output variables of the first to mth classifications;

4.b) in this way, the component variable t of the input variable x for each modality in the gaussian partial data is obtained using the following formula _c,G And a component vector u of an output variable y of each modality _c,G ：

Wherein p is _c,G And q is _c,G A single input, output common weight vector of the gaussian partial data, respectively; alpha is alpha _G And beta _G Is the input and output component weight vector parameter, alpha, of the combined M modal data of the Gaussian partial data _G ＝[α _G(1) α _G(2) ... α _G(M) ]，α _G(1) α _G(2) ... α _G(M) Respectively expressed as the first to Mth classificationsInput component weight vector parameter, β, of a class _G ＝[β _G(1) β _G(2) ... β _G(M) ]，β _G(1) β _G(2) ... β _G(M) Output component weight vector parameters of a first classification to an Mth classification are respectively expressed, wherein i represents an ith mode;

establishing a second objective function as:

max{I(t _c,G ,u _c,G )}

s.t.||p _c,G ||＝||q _c,G ||＝||α _G ||＝||β _G ||＝1

wherein, I (t) _c,G ,u _c,G ) Component variable t of input variable x in Gaussian part data _c,G And component vector u of output variable y _c,G The mutual information value between, | | | | represents the modulus;

4.c) using iterative algorithm to solve and obtain input and output common weight vector p in Gaussian partial data _c,G 、q _c,G And the input and output component weight vector parameter alpha _G 、β _G Then processing to obtain training data of a Gaussian part with common characteristics;

and T is obtained by processing the training data of Gaussian part with common characteristics _c ² Statistical limit of statistics

4.d) pre-multiplying the input variable of the data to be detected by p _c,G Obtaining a second fault estimation vector of the data to be detected, and obtaining T of the data to be detected by using the second fault estimation vector in combination with a partial least square method _c ² Statistics are obtained.

The step 4.c) uses the following iterative algorithm to solve and obtain the input and output public weight vector p in the Gaussian partial data _c,G 、q _c,G And input and output component weight vector parameter alpha _G 、β _G ：

(4.c.1) randomly initializing input and output component weight vector parameter alpha in Gaussian partial data _G 、β _G ；

(4.c.2) Weighting vector parameter alpha of input and output components in Gaussian partial data _G 、β _G The classified input variable data and the classified output variable data are brought in to obtain weighted input data

And output data

(4.c.3) solving input and output public weight vectors p in Gaussian part data by adopting industrial production process fault monitoring method based on hierarchical non-Gaussian monitoring algorithm _c,G And q is _c,G ；

(4.c.4) inputting and outputting the public weight vector p in the Gaussian part data obtained in the last step _c,G And q is _c,G Is brought into a second objective function;

(4.c.5) solving weight vector parameter alpha of input and output components in Gaussian partial data by using particle swarm optimization _G 、β _G ；

(4.c.6) repeating the processes of steps (2) - (6) until alpha _G 、β _G And p _c,G 、q _c,G Convergence, i.e. the four values are all less than the respective preset threshold.

In the step 5), the concrete steps are as follows:

5.a) as the common weight vector p _c After the determination, the training data of each modality with the characteristic features remaining is obtained by processing in the following way:

X _I(i) ＝X _(i) -X _(i) *p _c p _c ^T

wherein, X _I(i) Input variable data set representing training data of the i-th modality with characteristic features, Y _I(i) An output variable data set representing training data of the ith modality with characteristic features, X _(i) Input variable data set, Y, representing training data for the ith modality _(i) An output variable data set representing training data for an ith modality;

5.b) for training data of each mode with special characteristics, adopting step iii) to step iv) of fault monitoring method of industrial production process based on hierarchical non-Gaussian monitoring algorithm to process step by step to obtain load matrix for non-Gaussian part and Gaussian part of training data respectively, and obtaining I by processing the load matrix _i ² Statistical limit corresponding to statistical quantity

5.c) removing the obtained non-gaussian part data from the training data with characteristic features, and obtaining load matrixes for the gaussian part and the gaussian part of the training data by processing the training data with characteristic features in the steps from step iii to step iv in the fault monitoring method of the industrial production process based on the hierarchical non-gaussian monitoring algorithm according to the training data with characteristic features, and obtaining T by processing the load matrixes _i ² Statistical limits corresponding to the statistics and Q statistics

And statistical limit Q _{_limit} ；

5.d) pre-multiplying the data to be detected with the gaussian part having the characteristic by the training data of the gaussian part having the characteristic to obtain a load matrix, pre-multiplying the input variable of the data to be detected with the gaussian part by the load matrix P _G Obtaining a fourth fault estimation vector of the data to be detected, and obtaining the data to be detected T by using the fourth fault estimation vector _i ² Statistics and Q statistics.

In the step 6), the concrete steps are as follows:

the data to be detected starts to operate, but the types of modal working conditions are unknown, one mode corresponds to one modal working condition, the industrial production process has modal working conditions under M modes in total, the data to be detected of each known mode is monitored to obtain each statistical limit, the data to be detected of each current unknown mode is monitored to obtain each statistical quantity, and the following judgment is carried out:

if the current unknown mode of the data to be detected

statistic/T _c ² The statistic is simultaneously higher than that obtained by the corresponding training data of the first modality representing the common feature>

Statistical limit of statistic->

Or T _c ² Statistical limit of statistic->

The data to be detected in the current unknown mode is fault data;

if the current unknown mode of the data to be detected

statistic/T _c ² A statistic lower than that obtained by corresponding training data of the first modality representing the common signature>

Statistical limit of statistic->

Or T _c ² Statistical limit of statistic->

Then makes a judgment>

statistic/T _i ² statistics/Q systemWhether the measurement is simultaneously higher than the corresponding ^ representing characteristic feature>

Statistical limit of statistics

T _i ² Statistical limit of statistic->

And a statistical limit Q of the Q statistic _{_limit} ：

If not, the data to be detected of the current unknown mode belongs to a first mode;

if the current modal is higher than the preset threshold, training data of a second modal is called to be judged, and the data to be detected of the current unknown modal is judged

statistic/T _i ² Whether the statistic/Q statistic is simultaneously greater than +obtained by training data representing a second modality of characteristic feature>

Statistical limit for statistics>

T _i ² Is greater than or equal to>

And statistical limit of Q _{_limit} ：

If not, the data to be detected in the current unknown mode belongs to a second mode;

if the current modal is higher than the preset threshold, training data of a third modal is called for judgment, and the current unknown modal to-be-detected data is judged

statistic/T _i ² Statistics ofWhether the quantity/Q statistic is simultaneously higher than I obtained from corresponding training data of a third modality representing a characteristic feature _i ² Statistical limit of statistic->

T _i ² Statistical limit of statistic->

And statistical limit Q of Q statistic _{_limit} ：

If not, the data to be detected of the current unknown mode belongs to a third mode;

if the current modal is higher than the first modal, calling the training data of the fourth modal for judgment, and so on until the current unknown modal to be detected data I _i ² statistic/T _i ² The statistic/Q statistic being higher than that obtained by modal training data

Statistical limit of statistic->

T _i ² Statistical limit of statistic->

And statistical limit Q of Q statistic _{_limit} And considering the data to be detected in the current unknown mode as fault data, and finishing the monitoring and classification of the data to be detected.

Step 7) is also included after the step 6), specifically, the fault detection is carried out by using the statistical limit and the statistical quantity in the step 3), the step 4) and the step 5).

The step 7), specifically, the statistical limit and the statistical quantity in the step 3), 4), 5) meet the data to be detected corresponding to any one of the following five conditions, namely, the industrial production process is considered to have a process fault:

for data of non-Gaussian part with common characteristic in data to be detectedAbove statistical limits

Data to be detected corresponding to the statistic;

for the data of Gaussian part with common features in the data to be detected, the data is positioned at T above the statistical limit _c ² Data to be detected corresponding to the statistic;

for data of non-Gaussian part with characteristic features in the data to be detected, the data is located above the statistical limit

Data to be detected corresponding to the statistic;

for the data of Gaussian part with characteristic features in the data to be detected, the data is positioned at T above the statistical limit _i ² And the statistic and the data to be detected corresponding to the Q statistic.

The invention has the beneficial effects that:

and extracting multi-modal common features and special features for tracking the running performance of the process and the quality of the process. The method seeks to simultaneously construct process monitoring models from all mode data, obtain a common subspace by capturing common features behind different modes, obtain an independent subspace reflecting the characteristic features of each mode, and then carry out fault detection method research on each subspace.

The method is superior to other traditional methods in fault detection of the non-Gaussian multi-modal process, can fully consider the highly complex coupling relation among variables, can extract the non-Gaussian part in the data with unknown distribution characteristics, and can effectively extract the common characteristics and the unique characteristics of the multi-modal data, so that the fault monitoring of the multi-modal process is more efficient and accurate.

The method is superior to other traditional methods in multi-modal non-Gaussian process fault detection, can extract the special characteristics of the multi-modal and can also extract the common characteristics, and considers the mutual connection of the characteristics and the commonality among different modes, so that the multi-modal process monitoring is more effective.

Drawings

FIG. 1 is a graph of the classification results of the multi-modal process of the present invention.

Fig. 2 is a diagram of the first failure detection result of the present invention.

Fig. 3 is a diagram of a second failure detection result of the present invention.

Detailed Description

The method of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

The specific implementation case adopted by the invention is a Tennessee-Emament (TE) process, which comprises five main units: a reactor, a condenser, a compressor, a separator, and a stripper.

The product stream from the reactor was cooled by a condenser and then sent to a vapor/liquid separator. The vapor from the separator is recycled to the reactor by means of a compressor. To prevent the accumulation of inert components and reaction by-products in the process, a portion of the recycle stream must be vented. The condensed components from the separator (stream 10) are pumped to the stripper. Stream 4 is used to strip the remaining reactants in stream 10 which are combined with the recycle stream via stream 5 for further reaction.

In order to monitor the faults of the TE process, ensure the safety of the actual chemical production process and improve the economic benefit, the Tennessee-Marshman (TE) process has 41 measurement variables (including 22 process measurement variables and 19 component measurement variables) and 12 control variables, 31 variables are selected from the 22 process measurement variables and the 12 control variables as input variables as shown in Table 1 and 6 component measurement variables are selected from the 19 measurement variables as output variables, namely quality variables as shown in Table 2, when the process monitoring variables are determined. The tennessee-eastman process also simulated 21 faults as shown in table 3.

TABLE 1

TABLE 2

TABLE 3

In order to demonstrate the superiority of the proposed processing method, the present invention is embodied as a multi-modal process case including different stable models, from which 3000 samples are collected in total, and a data matrix of 50 variables is used as a training data set, and the multi-modal process is divided into three modes, wherein the first mode includes 1000 samples, the second mode includes 1000 samples, and the third mode includes 1000 samples, as shown in fig. 1. In order to achieve the purpose of fault detection, two faults are designed, wherein the fault 1 is that a step fault is added to a sample from 300 to 1000 of a 22 th variable of a first modality, and the fault 2 is that a slope fault is added to a sample from 1500 to 2000 of the 22 th variable of a second modality, and the fault detection is respectively carried out as a data set to be detected, and the specific steps are as follows:

1) Under the condition that no fault is known in the industrial production process, acquiring data of an input variable x and an output variable y in the industrial production process as training data through a sensor, and carrying out standardization processing to enable the mean value of the data to be 0 and the variance to be 1;

under the condition that the industrial production process needs to detect faults, acquiring an input variable x of the industrial production process on line through a sensor _test And an output variable y _test The two fault data are used as data to be detected, and standardized processing is carried out, so that the mean value of the data is 0, and the variance is 1, and the data are used as the data to be detected;

2) Classifying the training data by adopting a fault detection method of a layered non-Gaussian algorithm based on sample multi-modal classification to obtain classified training data;

3) Aiming at the classified training data, each classification corresponds to one mode, an optimization objective function is established in each mode by using a plurality of weight vectors, component vectors and other characteristic quantities to obtain the classified training data and the common characteristics of the data to be detected, the step iii) in the fault monitoring method of the industrial production process based on the hierarchical non-Gaussian monitoring algorithm is adopted according to the common characteristics to extract the training data of the non-Gaussian part with the common characteristics in each mode, and the statistical limit of the training data of the non-Gaussian part with the common characteristics and the statistical quantity of the data to be detected are processed;

4) After training data of a non-Gaussian part with common characteristics are obtained, extracting the common characteristics of the rest Gaussian part data, establishing an optimized objective function by applying a plurality of weight vectors, component vectors and other characteristic quantities in each mode for the Gaussian part data to obtain the common characteristics of the Gaussian part data, and obtaining the statistical limit of the training data of the Gaussian part with the common characteristics and the statistical quantity of data to be detected according to the common characteristics by combining a partial least square method;

5) After Gaussian part data with common characteristics and non-Gaussian part data with common characteristics are extracted, the rest data are data with characteristic characteristics, and according to the data with characteristic characteristics, the iv) step in the industrial production process fault monitoring method based on the hierarchical non-Gaussian monitoring algorithm is adopted to obtain the statistical limit of training data with characteristic characteristics and the statistical quantity of data to be detected;

6) And classifying the data to be detected in an online identification mode to obtain the monitored and classified data to be detected.

The invention can simultaneously construct the process monitoring model from all mode data, obtain the public subspace by capturing the public characteristics behind different modes, and obtain the independent subspace reflecting the characteristic characteristics of each mode, and then carry out the fault detection method research on each subspace, thereby improving the accuracy and effectiveness of fault monitoring.

The above embodiments are disclosed to illustrate the present invention, not to limit the present invention, and any modifications and changes made within the spirit of the present invention and the scope of the claims fall within the scope of the present invention.

Claims (9)

1. A layered non-Gaussian process monitoring method based on public and unique feature extraction is characterized by comprising the following steps:

1) Acquiring data of an input variable x and an output variable y of the industrial production process as training data through a sensor under the condition that no fault is known in the industrial production process;

under the condition that the industrial production process needs to detect faults, acquiring an input variable x of the industrial production process on line through a sensor _test And an output variable y _test The data to be detected is used as the data to be detected;

2) Classifying the training data to obtain classified training data;

3) Aiming at the classified training data, each classification corresponds to one mode, an optimization objective function is established in each mode by using a plurality of weight vectors and feature quantities of component vectors to obtain the classified training data and the common features of the data to be detected, the training data of the non-Gaussian part with the common features in each mode are extracted according to the common features, and the statistical limit of the training data of the non-Gaussian part with the common features and the statistical quantity of the data to be detected are processed;

4) After training data of a non-Gaussian part with common characteristics are obtained, extracting the common characteristics of the remaining Gaussian part data, establishing an optimization objective function by using the characteristic quantities of a plurality of weight vectors and component vectors in each mode for the Gaussian part data to obtain the common characteristics of the Gaussian part data, and obtaining the statistical limit of the training data of the Gaussian part with the common characteristics and the statistical quantity of data to be detected according to the common characteristics by combining a partial least square method;

5) After Gaussian partial data with common characteristics and non-Gaussian partial data with common characteristics are extracted, the rest data are data with characteristic characteristics, and statistical limits of training data with characteristic characteristics and statistics of data to be detected are obtained according to the data with characteristic characteristics;

6) And classifying the data to be detected in an online identification mode to obtain the monitored and classified data to be detected.

2. The hierarchical non-gaussian process monitoring method based on common and unique feature extraction as claimed in claim 1, wherein:

in the step 3), the concrete steps are as follows:

3.a) all input variables X of industrial production process are formed into input variable data set X, X belongs to R ^n×M·m Standardizing each input variable X in an input variable data set X, wherein n is the number of sampling values in the input variable X, M is the number of input variables, and M is the classification number of training data, namely a modal number;

3.b) the classified input variable data is expressed as: x = [ X = ₍₁₎ X ₍₂₎ ... X _(M) ]The output variable data is expressed as: y = [ Y) ₍₁₎ Y ₍₂₎ ... Y _(M) ]；X ₍₁₎ X ₍₂₎ ... X _(M) Input variable data respectively expressed as first to Mth classifications, Y ₍₁₎ Y ₍₂₎ ... Y _(M) Output variable data respectively expressed as first to Mth classifications;

3.c) in this way, the component variable t of the input variable x for each modality in the data is obtained using the following formula _c And a component vector u of an output variable y of each modality _c ：

Wherein p is _c And q is _c Respectively single input and output common weight vectors; alpha, beta is the input and output component weight vector of the combined M modal dataParameter, α = [ ] ₍₁₎ α ₍₂₎ ... α _(M) ]，α ₍₁₎ α ₍₂₎ ... α _(M) Expressed as input component weight vector parameters of first to Mth classes, respectively, β = [ β ] ₍₁₎ β ₍₂₎ ... β _(M) ]，β ₍₁₎ β ₍₂₎ ... β _(M) Output component weight vector parameters of a first classification to an Mth classification are respectively expressed, wherein i represents an ith mode;

establishing a first objective function as:

max{I(t _c ,u _c )}

s.t.||p _c ||＝||q _c ||＝||α||＝||β||＝1

wherein, I (t) _c ,u _c ) A component variable t being an input variable x _c And component vector u of output variable y _c The mutual information value between, | | | | represents the modulus;

3.d) using iterative algorithm to obtain input and output common weight vector p _c 、q _c And inputting and outputting component weight vector parameters alpha and beta, then processing to obtain non-Gaussian part data with common features, and processing the non-Gaussian part data with common features to obtain the non-Gaussian part data

Statistical limits of statistics

3.e) pre-multiplying an input variable of data to be detected by p _c Obtaining a first fault estimation vector of the data to be detected, and obtaining the data to be detected by using the first fault estimation vector

Statistics are obtained.

3. The hierarchical non-gaussian process monitoring method based on common and unique feature extraction as claimed in claim 2, wherein:

the step 3.d) uses the following iterative algorithm to solve and obtain the input and output public weight vector p _c 、q _c And input and output component weight vector parameters α, β:

(3.d.1) randomly initializing input and output component weight vector parameters alpha and beta;

(3.d.2) substituting the input and output component weight vector parameters alpha and beta into the classified input variable data and the classified output variable data to obtain weighted input data

And output data

(3.d.3) solving an input and output public weight vector;

(3.d.4) inputting and outputting the public weight vector p obtained in the last step _c And q is _c Is brought into a first objective function;

(3.d.5) solving input and output component weight vector parameters alpha and beta by using a particle swarm algorithm;

(3.d.6) repeating the steps (2) - (6) until α, β and p _c ，q _c And (6) converging.

4. The hierarchical non-gaussian process monitoring method based on common and unique feature extraction as claimed in claim 1, wherein:

step 4), removing the data of the non-gaussian part obtained in step 3) from the training data, and remaining the training data of the gaussian part, and then the specific steps are as follows:

4.a) the gaussian data for the classified input variables is represented as: x _G ＝[X _G(1) X _G(2) ... X _G(M) ]The gaussian data for the output variable is expressed as: y is _G ＝[Y _G(1) Y _G(2) ... Y _G(M) ]；X _G(1) X _G(2) ... X _G(M) Gaussian data respectively expressed as input variables of first to Mth classifications，Y _G(1) Y _G(2) ... Y _G(M) Gaussian data respectively expressed as output variables of the first to mth classifications;

4.b) in this way, the component variable t of the input variable x for each modality in the gaussian partial data is obtained using the following formula _c,G And a component vector u of an output variable y of each modality _c,G ：

Wherein p is _c,G And q is _c,G A single input, output common weight vector of the gaussian partial data, respectively; alpha is alpha _G And beta _G Is the input and output component weight vector parameter, alpha, of the combined M modal data of the Gaussian partial data _G ＝[α _G(1) α _G(2) ... α _G(M) ]，α _G(1) α _G(2) ... α _G(M) Expressed as the input component weight vector parameter, beta, of the first to Mth classes, respectively _G ＝[β _G(1) β _G(2) ... β _G(M) ]，β _G(1) β _G(2) ... β _G(M) Output component weight vector parameters of a first classification to an Mth classification are respectively expressed, wherein i represents an ith mode;

establishing a second objective function as:

max{I(t _c,G ,u _c,G )}

s.t.||p _c,G ||＝||q _c,G ||＝||α _G ||＝||β _G ||＝1

wherein, I (t) _c,G ,u _c,G ) Component variable t of input variable x in Gaussian part data _c,G And component vector u of output variable y _c,G The mutual information value between, | | | | represents the modulus;

4.c) using iterative algorithm to solve and obtain Gaussian partial dataInput and output common weight vector p _c,G 、q _c,G And input and output component weight vector parameter alpha _G 、β _G Then processing to obtain training data of a Gaussian part with common characteristics;

and T is obtained by processing the training data of Gaussian part with common characteristics _c ² Statistical limit of statistics

4.d) pre-multiplying the input variable of the data to be detected by p _c,G Obtaining a second fault estimation vector of the data to be detected, and obtaining T of the data to be detected by using the second fault estimation vector in combination with a partial least square method _c ² Statistics are obtained.

5. The hierarchical non-Gaussian process monitoring method based on common and unique feature extraction as claimed in claim 4, wherein:

the step 4.c) uses the following iterative algorithm to solve and obtain the input and output public weight vector p in the Gaussian partial data _c,G 、q _c,G And input and output component weight vector parameter alpha _G 、β _G ：

(4.c.1) randomly initializing input and output component weight vector parameters alpha in Gaussian partial data _G 、β _G ；

(4.c.2) inputting and outputting the weight vector parameter alpha of the input and output components in the Gaussian part data _G 、β _G The classified input variable data and the classified output variable data are brought in to obtain weighted input data

And output data

(4.c.3) solving the Gaussian fraction by adopting the industrial production process fault monitoring method based on the layered non-Gaussian monitoring algorithmAccording to which the common weight vector p is input and output _c,G And q is _c,G ；

(4.c.4) inputting and outputting the public weight vector p in the Gaussian part data obtained in the last step _c,G And q is _c,G Is brought into a second objective function;

(4.c.5) solving weight vector parameter alpha of input and output components in Gaussian partial data by using particle swarm optimization _G 、β _G ；

(4.c.6) repeating the processes of steps (2) - (6) until alpha _G 、β _G And p _c,G 、q _c,G And (6) converging.

6. The hierarchical non-gaussian process monitoring method based on common and unique feature extraction as claimed in claim 1, wherein:

in the step 5), the concrete steps are as follows:

5.a) as the common weight vector p _c After the determination, the training data of each modality with the characteristic features remaining is obtained by processing in the following way:

X _I(i) ＝X _(i) -X _(i) *p _c p _c ^T

wherein, X _I(i) Input variable data set representing training data of the i-th modality with characteristic features, Y _I(i) Output variable data set representing training data of the ith modality with characteristic features, X _(i) Input variable data set, Y, representing training data for the ith modality _(i) An output variable data set representing training data for an ith modality;

5.b) for training data of each mode with specific characteristics, processing by industrial production process fault monitoring method based on hierarchical non-Gaussian monitoring algorithm to obtain load matrix for non-Gaussian part and Gaussian part of training data, and processing by using the load matrix to obtain load matrix

Statistical limit corresponding to statistical quantity

5.c) removing the above obtained non-gaussian part data from the training data with characteristic features, and processing the training data with gaussian part with characteristic features by using a fault monitoring method based on a hierarchical non-gaussian monitoring algorithm to obtain load matrices for gaussian part and gaussian part of the training data, and processing the load matrices to obtain T _i ² Statistical limits corresponding to the statistics and Q statistics

And statistical limit Q _{_limit} ；

5.d) pre-multiplying the data to be detected with the gaussian part having the characteristic by the training data of the gaussian part having the characteristic to obtain a load matrix, pre-multiplying the input variable of the data to be detected with the gaussian part by the load matrix P _G Obtaining a fourth fault estimation vector of the data to be detected, and obtaining the data to be detected T by using the fourth fault estimation vector _i ² Statistics and Q statistics.

7. The method of claim 1, wherein the method comprises the following steps:

in the step 6), the concrete steps are as follows:

the data to be detected starts to operate, modal conditions under M modals are totally generated in the industrial production process, the data to be detected of each known modality are monitored to obtain each statistical limit, the data to be detected of each current unknown modality are monitored to obtain each statistic, and the following judgment is carried out:

if when it is usedOf data to be detected in a previously unknown modality

statistic/T _c ² The statistics being obtained while being higher than the corresponding training data of the first modality

Statistical limit of statistics

Or T _c ² Statistical limits of statistics

The data to be detected in the current unknown mode is fault data;

if the current unknown mode of the data to be detected

statistic/T _c ² The statistics being obtained from training data lower than the corresponding first modality

Statistical limit of statistics

Or T _c ² Statistical limit of statistics

Then judge

statistic/T _i ² Whether statistic/Q statistic is simultaneously higher than corresponding

Statistical limit of statistics

T _i ² Statistical limit of statistics

And statistical limit Q of Q statistic _{_limit} ：

If not, the data to be detected of the current unknown mode belongs to a first mode;

if the current mode is higher than the preset threshold, the training data of the second mode is called for judgment, and the data to be detected of the current unknown mode is judged

statistic/T _i ² Whether the statistic/Q statistic is simultaneously higher than the training data of the second modality

Statistical limit of statistics

T _i ² Statistical limit of

And statistical limit of Q _{_limit} ：

If not, the data to be detected in the current unknown mode belongs to a second mode;

if the current modal is higher than the preset threshold, training data of a third modal is called for judgment, and the current unknown modal to-be-detected data is judged

statistic/T _i ² Whether the statistic/Q statistic is simultaneously higher than the corresponding training data of the third modality

Statistical limit of statistics

T _i ² Statistical limit of statistics

And statistical limit Q of Q statistic _{_limit} ：

If not, the data to be detected in the current unknown mode belongs to a third mode;

if the current modal is higher than the preset threshold, the training data of the fourth modal is called for judgment, and the like is repeated until the current unknown modal to-be-detected data

statistic/T _i ² The statistic/Q statistic being higher than that obtained by modal training data

Statistical limit of statistics

T _i ² Statistical limit of statistics

And statistical limit Q of Q statistic _{_limit} And considering the data to be detected in the current unknown mode as fault data.

8. The method of claim 1, wherein the method comprises the following steps:

and step 7) is also included after the step 6), specifically, the fault detection is carried out by utilizing the statistical limits and statistics in the step 3), the step 4) and the step 5).

9. The hierarchical non-gaussian process monitoring method based on common and unique feature extraction as claimed in claim 1, wherein:

the step 7), specifically, the statistical limit and the statistical quantity in the step 3), 4), 5) meet the data to be detected corresponding to any one of the following five conditions, namely, the industrial production process is considered to have a process fault:

for data of non-Gaussian part with common features in the data to be detected, the data is above the statistical limit

Data to be detected corresponding to the statistic;

for the data of Gaussian part with common features in the data to be detected, the data is positioned at T above the statistical limit _c ² Data to be detected corresponding to the statistic;

for data of non-Gaussian part with characteristic features in the data to be detected, the data is located above the statistical limit

Data to be detected corresponding to the statistic;

for the data of Gaussian part with characteristic features in the data to be detected, the data is positioned at T above the statistical limit _i ² And the statistic and the data to be detected corresponding to the Q statistic.

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