Bearing fault prognosis based on health state probability estimation

Kim, Hack-Eun, Tan, Andy C.C., Mathew, Joseph, & Choi, Byeong-Keun (2012) Bearing fault prognosis based on health state probability estimation. Expert Systems with Applications, 39(5), pp. 5200-5213.

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In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

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ID Code: 60028
Item Type: Journal Article
Refereed: Yes
Keywords: Prognosis, Degradation stage, Support Vector Machine (SVM), Remaining useful life (RUL), High pressure LNG pump
DOI: 10.1016/j.eswa.2011.11.019
ISSN: 0957-4174
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Research Centres > CRC Integrated Engineering Asset Management (CIEAM)
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2011 Elsevier Ltd. All rights reserved.
Deposited On: 04 Jun 2013 01:41
Last Modified: 04 Jun 2013 01:41

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