A novelty degradation assessment method for equipment based on multi-kernel SVDD

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1 Scopus citations

Abstract

Support vector data description (SVDD) has been applied to performance degradation assessment for years. But single kernel may not describe the varying distribution very well. Multi-kernel learning (MKL) method was developed and proved to perform better than single kernel. Previous studies have been conducted to build up a fixed model, which takes the sample distance as the assessment index. However, different condition may have the same distribution in feature space. In this paper, we proposed a new robust method for bearing performance degradation assessment based on multi-kernel SVDD, and designed a new index with hyper-sphere radius. The experiment results show that the new index can reflect the degradation's development exactly.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Automation Science and Engineering
Subtitle of host publicationGreen Automation Toward a Sustainable Society, CASE 2012
Pages753-756
Number of pages4
DOIs
StatePublished - 2012
Event2012 IEEE International Conference on Automation Science and Engineering: Green Automation Toward a Sustainable Society, CASE 2012 - Seoul, Korea, Republic of
Duration: 20 Aug 201224 Aug 2012

Publication series

NameIEEE International Conference on Automation Science and Engineering
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference2012 IEEE International Conference on Automation Science and Engineering: Green Automation Toward a Sustainable Society, CASE 2012
Country/TerritoryKorea, Republic of
CitySeoul
Period20/08/1224/08/12

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