Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Fault classification method based on self-adaption integrated semi-supervision Fisher discrimination

A Fisher discrimination and fault classification technology, applied in electrical testing/monitoring, testing/monitoring control systems, instruments, etc., can solve problems such as unstable performance as supervised learning and semi-supervised learning, and improve monitoring effects, Favorable effects of automated implementation, enhanced mastery

Inactive Publication Date: 2017-06-13
ZHEJIANG UNIV
View PDF4 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But the actual problem is that the performance of semi-supervised learning is not stable, and the performance under specific data may not be as good as that of supervised learning.

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
  • Fault classification method based on self-adaption integrated semi-supervision Fisher discrimination
  • Fault classification method based on self-adaption integrated semi-supervision Fisher discrimination
  • Fault classification method based on self-adaption integrated semi-supervision Fisher discrimination

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0017] The invention aims at the problem of fault classification in industrial processes. In the method, a large amount of unlabeled data is randomly sampled when offline modeling is performed, and a plurality of semi-supervised random training subsets are formed with the labeled data. When training sub-classifiers in each iteration, adaptive weight adjustment of labeled samples is performed, and then semi-supervised Fisher dimensionality reduction is performed to obtain multiple Fisher discriminant matrices (composed of r Fisher discriminant vectors, where r is Dimensions after dimensionality reduction), and use the labeled sample data after dimensionality reduction to obtain the posterior probability matrix, the fusion weight of the sub-classifier and the sample weight of the labeled data in the next iteration according to the Bayesian statistical method. The posterior probability matrix of the labeled data and the corresponding label are used as the training samples of the K...

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 an industrial process fault classification method based on self-adaption integrated semi-supervision Fisher discrimination. The method comprises the steps of when off-line modeling is conducted, firstly conducting off-line modeling on unlabeled data, and constituting a semi-supervision random training subset by combining labeled data with the unlabeled data; when iteration training is conducted on a sub classifier each time, conducting semi-supervision Fisher dimensionality reduction to obtain a Fisher discrimination matrix, and obtaining a posterior probability matrix, a combined weight of the sub classifier and a sample weight of the labeled data during next time iteration with the labeled sample data after dimensionality reduction according to a Bayesian statistics method; adopting the posterior probability matrix of the labeled data and a label of the matrix as a training set of a fusion algorithm K near neighbor; during online classification, calling each sub classifier to obtain the posterior probability matrix of an online sample to be detected, and inputting the posterior probability matrix into a fusion K near neighbor classifier with the weight to obtain a final result. Compared with an existing method, the industrial process fault classification method based on the self-adaption integrated semi-supervision Fisher discrimination improves the fault classification result of an industrial process, and more facilitates automated implementation of the industrial process.

Description

technical field [0001] The invention belongs to the field of industrial process control, in particular to a fault classification method based on adaptive integrated semi-supervised Fisher discrimination. Background technique [0002] As an important part of process system engineering, process monitoring technology has great research significance and application value for the core goals of modern process industries such as ensuring process safety and improving product quality. With the continuous development of process industry control technology and the widespread application of Distributed Control System (DCS) in the process industry, the process industry has begun to produce massive process data. Therefore, process monitoring technology based on multivariate statistics and pattern recognition has attracted widespread attention from academia and industry, and has become a research hotspot in the field of process monitoring. In the past two decades, a large number of resear...

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
CPCG05B23/024
Inventor 葛志强王虹鉴
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
Eureka Blog
Learn More
PatSnap group products