J 2015

Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data

VALIŠ, David, Libor ŽÁK a Ondřej POKORA

Základní údaje

Originální název

Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data

Autoři

VALIŠ, David (203 Česká republika, garant), Libor ŽÁK (203 Česká republika) a Ondřej POKORA (203 Česká republika, domácí)

Vydání

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, London, SAGE Publications Ltd, 2015, 1748-006X

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

20306 Audio engineering, reliability analysis

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Impakt faktor

Impact factor: 1.073

Kód RIV

RIV/00216224:14310/15:00082089

Organizační jednotka

Přírodovědecká fakulta

UT WoS

000349221400004

Klíčová slova anglicky

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

Štítky

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 5. 11. 2019 11:26, Mgr. Marie Šípková, DiS.

Anotace

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.