Model Based Bearing Fault Detection Using Support Vector Machines



Published Mar 26, 2021
Karthik Kappaganthu C. Nataraj Biswanath Samanta


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.

How to Cite

Kappaganthu , K. ., Nataraj , C. ., & Samanta, B. . (2021). Model Based Bearing Fault Detection Using Support Vector Machines. Annual Conference of the PHM Society, 1(1). Retrieved from
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bearings, classification, diagnosis, features, model based diagnostics, support vector machines

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