VALIŠ, David and Ondřej POKORA. Estimation of system residual useful life based on selected tribo data. In Industrial Engineering and Engineering Management (IEEM), 2014 IEEE International Conference on. Selangor: IEEE Computer Society, 2015, p. 1171-1175. ISBN 978-1-4799-6410-9. Available from: https://dx.doi.org/10.1109/IEEM.2014.7058823.
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Basic information
Original name Estimation of system residual useful life based on selected tribo data
Authors VALIŠ, David (203 Czech Republic, guarantor) and Ondřej POKORA (203 Czech Republic, belonging to the institution).
Edition Selangor, Industrial Engineering and Engineering Management (IEEM), 2014 IEEE International Conference on, p. 1171-1175, 5 pp. 2015.
Publisher IEEE Computer Society
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 20306 Audio engineering, reliability analysis
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
RIV identification code RIV/00216224:14310/15:00086995
Organization unit Faculty of Science
ISBN 978-1-4799-6410-9
ISSN 2157-3611
Doi http://dx.doi.org/10.1109/IEEM.2014.7058823
Keywords in English Field data assessment; first hitting time; maintenance optimization; off-line diagnostics; residual life
Tags AKR
Changed by Changed by: Ing. Nicole Zrilić, učo 240776. Changed: 25/5/2017 13:34.
Abstract
The aim of the article is to estimate a system residual technical life. When estimating a residual technical life statistically, a big amount of tribo-diagnostic data is used. Data includes the information about particles contained in oil which testify to oil condition as well as system condition. We focus on the particles which we consider to be interesting. They are Ferrum (Fe) and Lead (Pb). By modelling the occurrence of particles in oil we expect to determine the adequate moment to perform preventive maintenance and the length of residual system useful life. The way of modelling is based on the specific characteristics of diffusion processes, namely the Ornstein-Uhlenbeck process. Following the modelling results we could set the principles of 'CBM-Condition Based Maintenance'. However, the possibilities are much wider, since we can also plan operation, mission and reduce life cost.
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