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

Combustion monitoring and diagnosis method based on feature extraction and fuzzy C-means cluster

A mean value clustering and feature extraction technology, applied in character and pattern recognition, biological neural network models, instruments, etc., can solve problems such as the complexity of boiler combustion

Inactive Publication Date: 2014-06-25
SOUTHEAST UNIV +1
View PDF1 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the complexity and variability of boiler combustion, further research work on combustion diagnosis is needed

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
  • Combustion monitoring and diagnosis method based on feature extraction and fuzzy C-means cluster
  • Combustion monitoring and diagnosis method based on feature extraction and fuzzy C-means cluster
  • Combustion monitoring and diagnosis method based on feature extraction and fuzzy C-means cluster

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0041] The present invention will be further described below in conjunction with the figures.

[0042] A group of tests were carried out on May 15, 16 and 17, 2012, with a total of 28 working conditions. The duration of each experimental working condition was 15 minutes (after stabilization), and the air volume and atomization pressure were changed respectively. , fuel pressure, fuel temperature and other parameters to obtain fire detection signals under various working conditions. The whole process mainly includes input data preprocessing, entropy value calculation, PNN neural network modeling and monitoring management and other core modules. The detailed process is as follows: figure 1 Shown:

[0043] 1. The furnace flame obtains the flame detection signal through the flame detector, serial interface and attached data acquisition system;

[0044] 2. After the data enters the input data preprocessing link, that is, the data acquisition system and the data storage file, the f...

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 combustion monitoring and diagnosis method based on feature extraction and fuzzy C-means cluster, comprising steps of utilizing time-frequency domain analysis and comentropy analysis technology to extract a flame characteristic value reflecting the combustion condition in flame detection signals, utilizing the fuzzy C-means cluster algorithm to perform FCM cluster analysis on the flame characteristic value and calculating a fuzzy combustion index through the fuzzy recognition. The invention can obtain a quantitative value of a combustion state through the fuzzy mode recognition and calculation of the combustion index, can classify the combustion states to realize quantitative monitoring of the combustion state, can promptly and accurately reflect boiler operation performance attributes, can provide new thoughts and methods for optimizing the performance and monitoring the state of the boiler, and can provide a reference model to the advanced modules of the power plant monitoring information system (boiler operation optimization, state monitoring and fault diagnosis).

Description

technical field [0001] The invention relates to a combustion working condition monitoring method, in particular to an FCM clustering method and fuzzy pattern recognition, and belongs to the field of machine learning modeling. Background technique [0002] Cluster analysis is a kind of multivariate statistical analysis and an important branch of unsupervised pattern recognition. Clustering is a process of distinguishing and categorizing things. In this process, there is no prior knowledge related to classification, and the internal similarity between things is the only criterion for category division. Therefore, as an unsupervised classification method, it divides an unlabeled sample set into several classes according to certain criteria, and gathers similar samples into one class as much as possible, and gathers dissimilar samples into different classes. Cluster analysis can be divided into two categories: traditional clustering and fuzzy clustering. Traditional clustering...

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): G06K9/46G06K9/62G06N3/02
Inventor 顾慧司风琪桂汉生
Owner SOUTHEAST UNIV
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