J 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 POKORA

Basic 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

DOI

UT WoS

000349221400004

Keywords in English

Failure prediction; system residual technical life estimation; field data; Wiener process

Tags

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.