Specifications
and Other Waterways
DOI: 10.4028/www.scientific.net/AMM.256-259.2616
Database: Compendex
Compilation and indexing terms, © 2013 Elsevier Inc.
21.
Accession number: 20130415932691
Title: Failure mode recognition clustering algorithm based on manifold learning
Authors: Lou, Zhigang1 ; Liu, Hongzhao1/娄志刚;刘宏昭
Author affiliation:
1 The Faculty of Mechanical and Precision Instrument Engineering, Xi'an University of
Technology, Xi'an, 710048, China
Corresponding author: Lou, Z. (louzg@163.com)
Source title: Applied Mechanics and Materials
Abbreviated source title: Appl. Mech. Mater.
Volume: 263-266
Issue: PART 1
Monograph title: Information Technology Applications in Industry
Issue date: 2013
Publication year: 2013
Pages: 2126-2130
Language: English
ISSN: 16609336
E-ISSN: 16627482
ISBN-13: 9783037855744
Document type: Conference article (CA)
Conference name: 2012 International Conference on Information Technology and
Management Innovation, ICITMI 2012
Conference date: November 10, 2012 - November 11, 2012
Conference location: Guangzhou, China
Conference code: 95052
Sponsor: Information Science School of Guangdong; University of Business Studies
Publisher: Trans Tech Publications, P.O. Box 1254, Clausthal-Zellerfeld, D-38670,
Germany
Abstract: Manifold learning is a new unsupervised learning method. Its main purpose is to
find the inherent law of generated data sets. Be used for high dimensional nonlinear fault
samples for learning, in order to identify embedded in high dimensional data space in the low
dimensional manifold, can be effective data found the essential characteristics of fault
identification. In many types of fault, sometimes often failure and normal operation of the
equipment of some operation similar to misjudgment, such as oil pipeline transportation process,
pipeline regulating pump, adjustable valve, pump switch, normal operation and pipeline leakage
fault condition similar spectral characteristics, thus easy for pipeline leakage cause mistakes. This
paper uses the manifold learning algorithm for fault pattern clustering recognition, and through
experiments on the algorithm is evaluated. © (2013) Trans Tech Publications, Switzerland.
Number of references: 5