6533b827fe1ef96bd1286410
RESEARCH PRODUCT
Early detection and classification of bearing faults using support vector machine algorithm
Kjell G. RobbersmyrJagath Sri Lal SenanayakaSurya Teja KandukuriHuynh Van Khangsubject
010302 applied physicsElectric motorEngineeringBearing (mechanical)business.industry020208 electrical & electronic engineeringFeature extractionPattern recognition02 engineering and technology01 natural sciencesFault detection and isolationlaw.inventionSupport vector machineStatistical classificationlawFrequency domain0103 physical sciences0202 electrical engineering electronic engineering information engineeringArtificial intelligencebusinessTest datadescription
Bearings are one of the most critical elements in rotating machinery systems. Bearing faults are the main reason for failures in electrical motors and generators. Therefore, early bearing fault detection is very important to prevent critical system failures in the industry. In this paper, the support vector machine algorithm is used for early detection and classification of bearing faults. Both time and frequency domain features are used for training the support vector machine learning algorithm. The trained classier can be employed for real-time bearing fault detection and classification. By using the proposed method, the bearing faults can be detected at early stages, and the machine operators have time to take preventive action before a large-scale failure. The usefulness of the algorithm is validated by using a run-to-failure experimental test data.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-04-01 | 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD) |