Soft measurement method and system for vine copula correlation description based on Hamiltonian Monte Carlo sampling

A soft measurement and correlation technology, applied in the field of soft measurement, can solve problems affecting the effect of soft measurement, information loss, etc.

Active Publication Date: 2020-03-13
EAST CHINA UNIV OF SCI & TECH
View PDF3 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when the process data is highly nonlinear and non-Gaussian, information loss often occurs and directly affects the final soft sensor effect

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Soft measurement method and system for vine copula correlation description based on Hamiltonian Monte Carlo sampling
  • Soft measurement method and system for vine copula correlation description based on Hamiltonian Monte Carlo sampling
  • Soft measurement method and system for vine copula correlation description based on Hamiltonian Monte Carlo sampling

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0126] The present invention discloses a soft-sensing method based on the vine copula correlation description of Hamiltonian Monte Carlo sampling, and the specific steps are as follows:

[0127] [Step S1]: Select appropriate auxiliary variables for the soft sensor model according to the actual industrial production situation and expert knowledge

[0128] [Step S2]: Use the monotone transformation method to obtain the transformed data conforming to copula modeling, and calculate the average variance of the target variable of the training data:

[0129] See formula (1) for the zero-mean standardization of the original data

[0130]

[0131] in,

[0132] x i is the variable before transformation, X i ’ is the variable after zero-mean standardization, mean(X i ) is the variable X i mean, sd(X i ) is the variable X i The standard deviation of , d is the dimension of the vector X. Define the monotone transformation form, see formula (2):

[0133] Z i =(1-α i )X i '+α ...

Embodiment 2

[0186] The description of the following examples will help to understand the present invention, but does not limit the content of the present invention. see figure 2 , the present embodiment realizes the prediction (PER) of the degree of ethylene cracking in the ethylene cracking process. The data of this implementation example comes from the SRT-III model ethylene cracking furnace, and the prediction target is the ethylene cracking rate, which is determined by PER (propylene / ethylene ratio) Said that 500 sets of data under normal working conditions were selected, 400 sets were used to train the copula model, and 100 sets were used for testing.

[0187] (1) According to the prior information, four auxiliary variables are selected: the average outlet temperature of the cracking furnace x 1 , the density x of the pyrolysis feedstock 2 , total feed x 3 and steam hydrocarbon ratio x 4 . The target variable y is the lysis depth index PER.

[0188] (2) Data preprocessing: sta...

Embodiment 3

[0198] see Figure 4 , this embodiment realizes the prediction of acetylene concentration in the acetylene hydrogenation reactor. The data of this implementation example comes from the acetylene hydrogenation process, and the prediction target is the acetylene concentration. 250 sets of data in normal working conditions are selected, and 200 sets are used for training copula model, 50 for testing.

[0199] (1) According to prior information, three auxiliary variables are selected: C2 component feed rate x 1 , hydrogen feed rate x 2 , the temperature difference between inlet and outlet x 3 , the leading variable is the acetylene concentration y.

[0200] (2) Data preprocessing: standardize the zero mean value of the training samples, and select the last one-dimensional auxiliary variable x as the reference variable 3 , use the Pearson correlation coefficient method to perform monotone transformation, and obtain the transformed data [z 1 ,z 2 ,z 3 ,z v ].

[0201] (3) U...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a soft measurement method and a system for vine copula correlation description based on Hamiltonian Monte Carlo sampling. The method comprises the following steps: selecting anappropriate auxiliary variable for a soft measurement model; performing standardization and monotonous transformation on the training data, and calculating an average variance of target variables of the training data; c-vine copula is used for carrying out correlation modeling; carrying out online collection, standardization processing and monotonic transformation calculation on auxiliary variables of a to-be-predicted sample; performing Hamiltonian Monte Carlo sampling according to the distribution of the target variables of the training sample; calculating a copula function value of the processed auxiliary variable of the to-be-predicted sample and the sampling sample, and further calculating conditional probabilities of all possible results of the target variable; obtaining a mathematical expectation of a final prediction value; determining a confidence interval of the predicted value according to the conditional probability, and calculating a variance; and comparing whether the mathematical expectation variance of the prediction value exceeds the average variance of the target variable of the training sample.

Description

technical field [0001] The invention belongs to the field of soft measurement technology, in particular to a soft measurement method based on the vine copula correlation description of Hamilton Monte Carlo sampling; meanwhile, the invention also relates to a vine copula correlation description based on Hamilton Monte Carlo sampling soft sensor system. Background technique [0002] The modern industrial production process tends to be more and more complicated and large-scale. In order to monitor the system status in time and quickly track the product quality, real-time detection of key product quality is very important. In the actual production process, product quality is often obtained through laboratory analysis, but the cost of analytical instruments is high, and there is a certain delay in the laboratory process, which brings great difficulties to the timely sampling of key quality variables. Soft-sensing technology emerged as the times require. It establishes a mathemat...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F17/18
CPCG06F17/18Y02P90/02
Inventor 李绍军倪佳能周洋田一彤王世豪贾琼蔡俊
Owner EAST CHINA UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap