Chemical process fault detection method based on sparse filtering and logic regression

A logistic regression, chemical process technology, applied in electrical testing/monitoring and other directions, can solve problems such as poor generalization ability, complex modeling, and difficult industrial processes

Active Publication Date: 2017-12-05
SOUTH CHINA UNIV OF TECH
View PDF4 Cites 29 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the generalization ability of many existing technologies is not good, and the modeling is complicated, so it is difficult to apply to industrial processes

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
  • Chemical process fault detection method based on sparse filtering and logic regression
  • Chemical process fault detection method based on sparse filtering and logic regression
  • Chemical process fault detection method based on sparse filtering and logic regression

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0073] This embodiment provides a chemical process fault detection method based on sparse filtering and logistic regression, the flow chart of the method is as follows figure 1 As shown, the method proposed in this embodiment is applied to the Tennessee-Eastman (TE) chemical process to further illustrate the method of this embodiment, and the TE process was published in 1993 by Downs and Vogel of Eastman Chemical Company in the United States The computer simulation of the actual chemical process of Eastman Company, which was later mainly developed to evaluate the performance of process control technology and process monitoring methods, the process flow chart of the process is as follows figure 2 shown. The TE process mainly includes five operating units, namely: reactor, condenser, vapor-liquid separator, cycle compressor, and stripper. In the simulated data, a total of 41 observed variables were monitored, including 22 continuous process variables and 19 component variables...

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 chemical process fault detection method based on sparse filtering and logic regression. The method comprises steps of data acquisition and pre-processing, characteristic learning and characteristic classification, and chemical process fault detection is realized through the three steps. According to the method, firstly, the sparse filtering algorithm of depth learning is applied to chemical process characteristic learning, secondly, the learned characteristics are classified through utilizing logic regression, the characteristic learning stage utilizes monitoring-less learning, artificial data marking is not needed, characteristics of adaptive learning original data can be realized, normal data can be distinguished from each type of fault data, and application to the industry is more simple and intelligent.

Description

technical field [0001] The invention relates to the field of chemical process fault detection and diagnosis, in particular to a chemical process fault detection method based on sparse filtering and logistic regression. Background technique [0002] Process safety has always been one of the most important concerns in the modern chemical industry. Fault detection and diagnosis, as the most powerful tool for the management of chemical abnormal working conditions, provide a certain guarantee for process safety. With the rapid development of control systems, chemical processes are becoming more and more automated. Fault Detection and Diagnosis (FDD) has been proposed for more than 40 years, but FDD in actual production cannot be automated, which has a lot to do with the complexity of the process and the applicability of the method. [0003] Data-based chemical process fault detection methods can be applied to complex chemical process systems without the need to obtain a large a...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
Inventor 旷天亮李秀喜詹志新
Owner SOUTH CHINA UNIV OF 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