Non-linear process industry fault prediction method based on novel FDE-ELM and EFSM

A fault prediction and non-linear technology, applied in electrical test/monitoring, test/monitoring control systems, instruments, etc., can solve problems such as inaccurate representation, inaccurate analysis of the relationship between variables, complex mathematical models, etc., to improve generalization The effect of low accuracy, complex solution model, and high generalization accuracy

Active Publication Date: 2015-04-08
BEIJING UNIV OF CHEM TECH
View PDF6 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the traditional EFSM cannot be directly introduced into the process industry, and there are still some unsolvable problems: First, the traditional EFSM data dependency graph is built entirely by mathematical models, but in the process industry system, the mathematical models are often very complicated and cannot be accurately expressed; secondly , variables between process industry systems often have time delays, traditional EFSM does not take this time delay into account, which will lead to inaccurate analysis of the relationship between variables

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
  • Non-linear process industry fault prediction method based on novel FDE-ELM and EFSM
  • Non-linear process industry fault prediction method based on novel FDE-ELM and EFSM
  • Non-linear process industry fault prediction method based on novel FDE-ELM and EFSM

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] Such as figure 1Shown is the working flow diagram of the method of the present invention. (1) Data preprocessing process: This process mainly performs noise reduction processing on industrial data to avoid affecting the accuracy of subsequent operation results due to noise interference. (2) Time-delay EFSM model construction process: This process mainly uses TDMI to calculate the delay time and correlation analysis of the preprocessed data, build a data dependency graph, and build the state through prior knowledge and mechanism analysis of the model Dependency graphs and migration tables reduce complex process industry objects to simple models, clearly showing the internal connections between system variables, the internal connections between states, and the interrelationships between states and variables. (3) Variable prediction process based on FDE-ELM: This process is to use FDE-ELM network to predict possible abnormalities in process industry operation. When const...

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 non-linear process industry fault prediction and inference method. The method comprises: a data pre-processing process, an extended finite state machine (EFSM) model building process, a variable prediction process based on a feedback differential evaluation optimized extreme learning machine (FDE-ELM), and a fault inference process based on the EFSM. The method is advantaged by high model building stability, high fault prediction precision, low algorithm complexity, automatic fault inferring, and visual inference process. The method provides help for ensuring security of process industry, ensuring property and personnel safety, and saving hardware cost.

Description

technical field [0001] The present invention takes the nonlinear process industrial system as the object, aims at improving the modeling stability and fault prediction accuracy, and proposes a complex process industrial data preprocessing technology, Feedback Differential Evolution Optimized Extreme Learning Machine (Feedback Differential Evolution Optimized Extreme Learning Machine, FDE-ELM) technology, and the industrial fault prediction method of extended finite state machine (Extended Finite State Machine, EFSM) technology. Background technique [0002] The process industry, including petrochemical, metallurgy, and papermaking, is the basic industry of our country, and it involves all aspects of national life. The process industry has the characteristics of large production scale, complex process, nonlinear process and high risk factor, so it has become an important research field of fault prediction and diagnosis. In recent years, there have been frequent accidents in ...

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
CPCG05B23/0218
Inventor 徐圆周子茜朱群雄曹健王晓耿志强卢玉帅叶亮亮刘莹陈彦京
Owner BEIJING UNIV OF CHEM 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