2015
Failure prediction of diesel engine based on occurrence of selected wear particles in oil
VALIŠ, David; Libor ŽÁK a Ondřej POKORAZákladní 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.