Model Based Bearing Fault Detection Using Support Vector Machines

Karthik Kappaganthu, C. Nataraj, and Biswanath Samanta
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_09_35.pdf404.01 KBSeptember 15, 2009 - 10:47am

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.

Publication Control Number: 
035
Submission Keywords: 
bearings
classification
diagnosis
features
model based diagnostics
support vector machines
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