Detailed Information on Publication Record
2015
Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data
VALIŠ, David, Libor ŽÁK and Ondřej POKORABasic information
Original name
Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data
Authors
VALIŠ, David (203 Czech Republic, guarantor), Libor ŽÁK (203 Czech Republic) and Ondřej POKORA (203 Czech Republic, belonging to the institution)
Edition
Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, London, SAGE Publications Ltd, 2015, 1748-006X
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
20306 Audio engineering, reliability analysis
Country of publisher
United Kingdom of Great Britain and Northern Ireland
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 1.073
RIV identification code
RIV/00216224:14310/15:00082089
Organization unit
Faculty of Science
UT WoS
000349221400004
Keywords in English
Failure prediction; system residual technical life estimation; field data; Wiener process
Tags
International impact, Reviewed
Změněno: 5/11/2019 11:26, Mgr. Marie Šípková, DiS.
Abstract
V originále
At present, numerous approaches are devoted to monitoring a system state. Their intention is to determine the current state of a system and predict reliability parameters for the future. This article addresses one of the several possible approaches that allows us to determine a system technical state on the basis of diagnostic data. These diagnostic data are from the area of tribiodagnostics, namely, engine oil. The article examines iron and lead particles that are selected deliberately with respect to their origin in kinematic parts of the system and their degree of correlation with operation measures. The particles occur in oil during both operating time and calendar time development. To model their occurrence during operation time, we have used, in the first part of the article, a mathematical regression method to set parameters. In the second part, we have applied a diffusion model based on a Wiener process. The results confirm that we are able to estimate the residual technical life of a system. Moreover, the results enable us to schedule properly the intervals of preventive maintenance (oil change) and to plan a mission/operation. This results in optimising life cycle costs. It is assumed that the potential of the diagnostic data will be extracted by other approaches and methods. In the subsequent work, it will be useful to determine specific interval values of optimised preventive maintenance.