An evaluation of the potential offered by a relevance vector classifier in machinery fault diagnosis

Zhang Kui, Li Yuhua, Fan Yibo, Gu Fengshou, Andrew Ball

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The increasing complexity of modern machinery systems demands an effective fault diagnosis strategy with low cost, high efficiency and reliability. This paper reports work which attempts to explore the potential offered by a Relevance Vector Machine (RVM) in machinery fault diagnosis. This work starts with a full investigation into the demands of modern fault diagnosis and the characteristics of the RVM method, and then provides an insight into the model of a relevance vector machine for classification. Finally, a case study of the multi-class classification of bearing faults further demonstrates the application potential of the method. Besides, it is proved that the proposed method is most suitable for real-time applications due to its high computational speed, low memory requirement and high accuracy.

Original languageEnglish
Pages (from-to)35-40
Number of pages6
JournalInternational Journal of COMADEM
Volume9
Issue number4
Publication statusPublished - 1 Oct 2006
Externally publishedYes

Fingerprint Dive into the research topics of 'An evaluation of the potential offered by a relevance vector classifier in machinery fault diagnosis'. Together they form a unique fingerprint.

Cite this