QUT ePrints

Prognosis of bearing failure based on health state estimation

Kim, Hack-Eun, Tan, Andy C. C., Mathew, Joseph, Kim, Eric Y. H., & Choi, Byeong-Keun (2009) Prognosis of bearing failure based on health state estimation. In Proceedings of the 4th World Congress on Engineering Asset Management, Springer, Marriott Athens Ledra Hotel, Athens.

View at publisher

Abstract

This paper proposes a new prognosis model based on the technique for health state estimation of machines for accurate assessment of the remnant life. For the evaluation of health stages of machines, the Support Vector Machine (SVM) classifier was employed to obtain the probability of each health state. Two case studies involving bearing failures were used to validate the proposed model. Simulated bearing failure data and experimental data from an accelerated bearing test rig were used to train and test the model. The result obtained is very encouraging and shows that the proposed prognostic model produces promising results and has the potential to be used as an estimation tool for machine remnant life prediction.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

257 since deposited on 21 Oct 2009
28 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 28105
Item Type: Conference Paper
Additional URLs:
Keywords: Prognosis, Bearing degradation state, Support vector machines, Remaining useful life
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300) > Mechanical Engineering not elsewhere classified (091399)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
Divisions: Current > Research Centres > CRC Integrated Engineering Asset Management (CIEAM)
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2009 Springer
Copyright Statement: The original publication is available at www.springerlink.com
Deposited On: 21 Oct 2009 15:22
Last Modified: 29 Feb 2012 23:59

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page