J 2015

Failure prediction of diesel engine based on occurrence of selected wear particles in oil

VALIŠ, David; Libor ŽÁK a Ondřej POKORA

Základní údaje

Originální název

Failure prediction of diesel engine based on occurrence of selected wear particles in oil

Autoři

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

Vydání

Engineering Failure Analysis, PERGAMON-ELSEVIER SCIENCE LTD, 2015, 1350-6307

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

Impakt faktor

Impact factor: 1.358

Kód RIV

RIV/00216224:14310/15:00082688

Organizační jednotka

Přírodovědecká fakulta

UT WoS

000361832300049

EID Scopus

2-s2.0-84941995720

Klíčová slova česky

System failure prediction; Material wear; System and material deterioration; System residual technical life estimation; Diffusion processes

Klíčová slova anglicky

System failure prediction; Material wear; System and material deterioration; System residual technical life estimation; Diffusion processes

Štítky

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 13. 3. 2018 15:55, Mgr. Ondřej Pokora, Ph.D.

Anotace

V originále

When assessing reliability, the principles of system failure prognostic are basic requirements. Condition-based maintenance is more demanding when estimating a system failure and residual/remaining technical life time (RTL). This paper introduces analytical and prognostic methods used for assessing system material wear to predict a failure occurrence. The principles presented in the article are based on indirect but real diagnostic oil data. We concentrate on wear metal particles such as iron (Fe) and lead (Pb) as potential failure indicators. Our approach is very different from other papers published in this area as their data were often artificial or viewed as potentially useful, but they never existed. The advantage and novelty of the outcomes presented in the article are that they might be used mainly for predicting failure occurrence and also for optimising intervals of preventive maintenance (PM), analyzing cost-benefit and planning operation/mission.