Application of fuzzy inference system for analysis of oil field data to optimize combustion engine maintenance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F19%3A00536532" target="_blank" >RIV/60162694:G43__/19:00536532 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26210/19:PU131472

Výsledek na webu

<a href="https://journals.sagepub.com/doi/abs/10.1177/0954407019833521" target="_blank" >https://journals.sagepub.com/doi/abs/10.1177/0954407019833521</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/0954407019833521" target="_blank" >10.1177/0954407019833521</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of fuzzy inference system for analysis of oil field data to optimize combustion engine maintenance

Popis výsledku v původním jazyce

The condition of a technical system has been subject to intense scrutiny in recent years. Monitoring the technical condition of a system may be performed by applying different approaches. The main intention of the monitoring is to get the information about the instant system condition, and to estimate and predict reliability measures. In the article, the authors suggest possible ways to process diagnostic measures which have the potential to determine the system condition and to predict its future development. The diagnostic measures are in this case indirect and they are introduced in the form of oil data. The diagnostic data are obtained from the tribodiagnostic system which is composed of kinematic pairs and oil. The analysed oil samples come from the combustion engine of a heavy ground vehicle. The authors focus on the output values in the form of wear particles, iron and lead, and additive particles. The concentration of these particles in the oil is influenced by operating time and calendar time. However, the particles include inherent and natural levels of uncertainty and fuzziness. Therefore, the authors apply and present the models imitating the development of the particles which are based on a fuzzy inference system. Highly valuable and extensive data set records enabled the authors to perform two-dimensional data modelling based both on operation time and calendar time. The obtained results enable us to predict the remaining useful life of the system. Moreover, the results could also be beneficial when modifying hard time scheduled preventive maintenance intervals (e.g. when to change the oil). The major contribution of this paper is the fact that all analysed diagnostic data are not artificial but real; moreover, they were collected for more than 10 years and therefore contain hundreds of records.

Název v anglickém jazyce

Application of fuzzy inference system for analysis of oil field data to optimize combustion engine maintenance

Popis výsledku anglicky

The condition of a technical system has been subject to intense scrutiny in recent years. Monitoring the technical condition of a system may be performed by applying different approaches. The main intention of the monitoring is to get the information about the instant system condition, and to estimate and predict reliability measures. In the article, the authors suggest possible ways to process diagnostic measures which have the potential to determine the system condition and to predict its future development. The diagnostic measures are in this case indirect and they are introduced in the form of oil data. The diagnostic data are obtained from the tribodiagnostic system which is composed of kinematic pairs and oil. The analysed oil samples come from the combustion engine of a heavy ground vehicle. The authors focus on the output values in the form of wear particles, iron and lead, and additive particles. The concentration of these particles in the oil is influenced by operating time and calendar time. However, the particles include inherent and natural levels of uncertainty and fuzziness. Therefore, the authors apply and present the models imitating the development of the particles which are based on a fuzzy inference system. Highly valuable and extensive data set records enabled the authors to perform two-dimensional data modelling based both on operation time and calendar time. The obtained results enable us to predict the remaining useful life of the system. Moreover, the results could also be beneficial when modifying hard time scheduled preventive maintenance intervals (e.g. when to change the oil). The major contribution of this paper is the fact that all analysed diagnostic data are not artificial but real; moreover, they were collected for more than 10 years and therefore contain hundreds of records.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering

ISSN

0954-4070

e-ISSN

2041-2991

Svazek periodika

233

Číslo periodika v rámci svazku

14

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

3736-3745

Kód UT WoS článku

000496739000013

EID výsledku v databázi Scopus

2-s2.0-85062474858