Principal component tracking based industrial fault monitoring method and application thereof

A principal component tracking, fault detection technology, applied in general control systems, electrical testing/monitoring, testing/monitoring control systems, etc., can solve problems such as high false alarm rate, and achieve the effect of improving the effect

Active Publication Date: 2015-07-22
ZHEJIANG UNIV
View PDF4 Cites 22 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Theoretically, fault detection can be performed by observing the amplitude of elements in a sparse matrix, but due to noise interference, doing so will lead to a relatively high false alarm rate

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
  • Principal component tracking based industrial fault monitoring method and application thereof
  • Principal component tracking based industrial fault monitoring method and application thereof
  • Principal component tracking based industrial fault monitoring method and application thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0071] As one of the most important basic industries in the national economy, iron and steel smelting is an important indicator to measure a country's economic level and comprehensive national strength. Blast furnace ironmaking is the most important link in the production process of the iron and steel industry, so it is of great significance to study the abnormal working condition diagnosis and safe operation methods of large blast furnaces.

[0072] The blast furnace is a huge airtight reaction vessel, and its internal smelting process is a typical "black box" operation through a series of complex physical, chemical and heat transfer reactions under high temperature and high pressure conditions. It is precisely because of the complexity inside the blast furnace that the data collected are diverse, linear, nonlinear, non-Gaussian and dynamic. Therefore, our proposed method is effective for the diagnosis of blast furnace faults. The effectiveness of the method of the present i...

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 principal component tracking based industrial fault monitoring method and application thereof, and belongs the technical field of industrial process monitoring and diagnosing. The method includes decomposing industrial collected data into a low rank matrix including complete process operation information and a sparse matrix including sensor noise and process faults according to a principal component tracking method; performing fault detection in the low rank matrix by the aid of a statistical magnitude T2, and performing fault detection in the sparse matrix by the aid of a statistical magnitude of a mean value correlation coefficient. Compared with the prior art, the principal component tracking based industrial fault monitoring method has the advantages that according to the characteristic that the matrixes are decomposed by the principal component tracking method, different statistical magnitudes are adopted for the matrixes with different characteristics, effective information included in the data are fully utilized, and accordingly, high accuracy in industrial fault detection is achieved.

Description

technical field [0001] The invention belongs to the field of industrial process monitoring and fault diagnosis, in particular to an online industrial fault detection based on principal component tracking, using T 2 Statistics and Mean Correlation Coefficient Statistics. Background technique [0002] Industrial process production is the pillar industry of national economic development, so it is very important to ensure the efficiency and stability of the production process. Fault detection is a key step to achieve this goal. [0003] There are many traditional fault detection methods, such as principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), artificial neural network (ANN) and improved methods based on them. The principal component tracking method has the advantages of being insensitive to outliers, sensitive to faults caused by small changes in variables, and can solve nonlinear processes. The essence of the principal component ...

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
CPCG05B23/0232
Inventor 杨春节潘怡君王琳孙优贤安汝峤
Owner ZHEJIANG UNIV
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