Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Multi-modal process fault detection method and system based on vine copula correlation description

A fault detection and correlation technology, applied in general control systems, control/regulation systems, simulators, etc., to solve problems such as the complexity of the parameter optimization process

Inactive Publication Date: 2015-09-16
EAST CHINA UNIV OF SCI & TECH
View PDF4 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The traditional copula has a problem of complexity in the parameter optimization process when describing the correlation of high-dimensional data

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
  • Multi-modal process fault detection method and system based on vine copula correlation description
  • Multi-modal process fault detection method and system based on vine copula correlation description
  • Multi-modal process fault detection method and system based on vine copula correlation description

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0076] see image 3 , the present invention discloses a multimodal chemical process fault detection method based on vine copula correlation modeling and Bayesian inference, the specific steps are as follows:

[0077] [Step S1] Obtain training sample sets of normal data in different modalities according to expert knowledge or clustering method.

[0078] [Step S2] Carry out correlation modeling by using C-vine copula to obtain the joint probability density function of each mode.

[0079] For n-dimensional random vector x=[x 1 ,x 2 ,...,x n ] T , its C-vine model (joint probability density function of x) is:

[0080] f ( x ) = Π t = 1 n f t ( x t ) × Π i ...

Embodiment 2

[0122] 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 , this embodiment realizes the multi-modal fault detection of the TE process in the case of mode one and mode three, and the production load and other related parameters of the two modes are shown in Table 1. The TE process studied in this example is a steady-state process under closed-loop control, and the process data are collected from 22 common process variables, and the sampling time is set to 0.05h. 1000 sets of training sample data are obtained from mode 1 and mode 3. The first 200 sets of data of the test sample data come from mode 3, and the last 200 sets of data come from mode 1, where fault 13 (drift) occurs from the 101st to 200th time, and fault 6 (drift) occurs from the 301st to 400th time step).

[0123] Table 1: Parameter settings of TE process mode 1 and mode 3

[0124]

[0125] (1) Accord...

Embodiment 3

[0137] A multimodal process fault detection method based on vine copula correlation description, said method comprising the steps of:

[0138] Step S1, obtaining training sample sets of normal data in different modalities;

[0139] Step S2, performing correlation modeling to obtain the joint probability density function of each mode;

[0140] Step S3, sampling the joint probability density function of different modes, and calculating the joint probability density function value of each sample;

[0141] Step S4, determine the discretization step size l according to the control limit, and use the density quantile method to construct the static density quantile table of the process;

[0142] Step S5, estimate the monitoring data at time t by means of table lookup Generalized local probability index under mode k P L ( k ) ( X t ...

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 discloses a multi-modal process fault detection method and system based on vine copula correlation description. The detection method comprises the following steps: obtaining a training sample set of normal data under different modals according to expert knowledge or adopting a clustering method; carrying out correlation modeling by utilizing C-vine copula and obtaining a joint probability density function of each modal; carrying out sampling on the joint probability density functions of different modals by utilizing a Markov Monte Carlo method and calculating the value of the joint probability density function of each sample; determining discretization step length l according to control limits and establishing a static density quantile table of the process by utilizing a density quantile method; estimating general local probability index PL<k> (Xt<monitor>), under the modal k, of monitoring data Xt<monitor> at the moment t by searching the table; and calculating BIP index by adopting Bayesian reasoning, and judging whether the index is beyond the limit so as to achieve real-time process monitoring.

Description

technical field [0001] The invention belongs to the technical field of fault detection, relates to a fault detection method, in particular to a multi-modal process fault detection method based on vine copula correlation description; at the same time, the invention also relates to a multi-modal process fault detection method based on vine copula correlation description Modal Process Fault Detection System. Background technique [0002] With the rapid development of society, people's demand for chemical products has greatly improved both in terms of quality and quantity, which promotes the development of chemical production processes in the direction of large-scale, comprehensive and complex. However, with the rapid growth and diversified development of the chemical industry, chemical production is facing the challenge of a weak safety foundation. Chemical production usually has the characteristics of high temperature and high pressure, poisonous and harmful, flammable and ex...

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): G05B19/048
CPCG05B19/048
Inventor 李绍军任翔郑文静许文夕杨一航
Owner EAST CHINA UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com