Classification of Bearing Faults Combining Compressive Sampling, Laplacian Score, and Support Vector Machine

H. O. A. Ahmed, M. L. Dennis Wong, A. K. Nandi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
40 Downloads (Pure)

Abstract

Rolling element bearings have a pivotal role in rotating machine and their failures are the leading cause of more substantial failures in the machine. In response to their importance, there is a growing body of research looking at condition monitoring of rolling element bearings to avoid machine breakdowns. In this study, by taking advantages of Compressive Sampling (CS), Laplacian Score (LS) and Multi-class Support Vector Machine (MSVM), an intelligent method for rolling bearing fault classification is proposed The CS is used to obtain compressed samples of the raw vibration signals, and the LS is used to rank the features of the obtained compressed samples with respect to their importance and correlations with the core fault characteristics. Then, based on LS ranking, we selected a small amount of the most significant compressed samples to produce the features vector. Finally, classification performance using MSVM shows high classification accuracy with a significantly reduced feature set.
Original languageEnglish
Title of host publicationProceedings of IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE
Pages8053-8058
ISBN (Print)978-1-5386-1127-2
DOIs
Publication statusPublished - 1 Nov 2017
Event43rd Annual Conference of the IEEE Industrial Electronics Society - China National Convention Center, Beijing, China
Duration: 29 Oct 20171 Nov 2017
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8168197

Conference

Conference43rd Annual Conference of the IEEE Industrial Electronics Society
Abbreviated titleIECON 2017
CountryChina
CityBeijing
Period29/10/171/11/17
Internet address

Fingerprint Dive into the research topics of 'Classification of Bearing Faults Combining Compressive Sampling, Laplacian Score, and Support Vector Machine'. Together they form a unique fingerprint.

  • Cite this

    Ahmed, H. O. A., Wong, M. L. D., & Nandi, A. K. (2017). Classification of Bearing Faults Combining Compressive Sampling, Laplacian Score, and Support Vector Machine. In Proceedings of IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society (pp. 8053-8058). IEEE. https://doi.org/10.1109/IECON.2017.8217413