Model based bearing fault detection using support vector machines

Karthik Kappaganthu, C. Nataraj, Biswanath Samanta

Research output: Contribution to book or proceedingConference articlepeer-review

2 Scopus citations

Abstract

This paper deals with the development of a model based method for bearing fault diagnostics. This method effectively combines the information available in the data and the model for efficient classification of the bearing and the type of defect. A four degrees of freedom nonlinear rigid rotor model is used to simulate the rotor bearing system. Precession of the shaft is measured using proximity probes. The deviation of the measurement from the model is used to classify the system. Typically proximity probe data by itself does not contain enough information for accurate classification. However, when the information from the model is incorporated the combined features provide excellent classification performance. Further the use of a model also enables better classification over varying parameters. A support vector machine is used for classification.

Original languageEnglish
Title of host publicationAnnual Conference of the Prognostics and Health Management Society, PHM 2009
PublisherPrognostics and Health Management Society
ISBN (Electronic)9781936263004
StatePublished - 2009
EventAnnual Conference of the Prognostics and Health Management Society, PHM 2009 - San Diego, United States
Duration: Sep 27 2009Oct 1 2009

Publication series

NameAnnual Conference of the Prognostics and Health Management Society, PHM 2009

Conference

ConferenceAnnual Conference of the Prognostics and Health Management Society, PHM 2009
Country/TerritoryUnited States
CitySan Diego
Period09/27/0910/1/09

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