Publication detail

Potential for using the ANN-FIS meta-model approach to assess levels of particulate contamination in oil used in mechanical systems

VALIŠ, D. GAJEWSKI, J. ŽÁK, L.

Original Title

Potential for using the ANN-FIS meta-model approach to assess levels of particulate contamination in oil used in mechanical systems

Type

journal article in Web of Science

Language

English

Original Abstract

Mechanical systems need to ensure high levels of quality. Today, greater generic reliability in systems makes it difficult to base any failure prognosis on previous system failures. Predicting the condition of a mechanical system needs to be based, instead, on monitoring the degradation of a system's components. Diagnostic signals can be identified and used as data to estimate the rate of degradation. A key driver for this work is the need to understand the performance of lubricants in systems involving mechanical contact. This article presents methods for studying field data collected with regard to oil. It focuses, in particular, on contaminated oil as this is an excellent source of diagnostic signals and information. However, data on oil present a degree of uncertainty in terms of both their collection and their use in the laboratory. Analysis of oil contaminants was, therefore, performed by applying a fuzzy inference system (FIS) and neural networks. The multilayer perception network was found to be an effective tool. The concentrations of iron and soot particles in used oil were selected as being both illustrative and the most significant model variables. The aim of this study is to acquire information about the condition of both lubricants and the mechanical systems, along with the development of degradation in mechanical equipment and the estimation of residual useful life (RUL). The results obtained will be useful in organizing effective operation of the mechanical systems being studied and modifying their maintenance.

Keywords

Tribological process variables; Oil contaminants; Particles concentration; Oil and related system degradation; System reliability and condition; Diagnostics; Neural networks

Authors

VALIŠ, D.; GAJEWSKI, J.; ŽÁK, L.

Released

1. 7. 2019

Publisher

PACCAR Technical Center

Location

Spojené království Velké Británie a Severního Irska

ISBN

0301-679X

Periodical

Tribology International

Year of study

135

Number

1

State

United Kingdom of Great Britain and Northern Ireland

Pages from

324

Pages to

334

Pages count

11

URL

https://apps.webofknowledge.com/InboundService.do?product=WOS&Func=Frame&DestFail=http%3A%2F%2Fwww.webofknowledge.com&SrcApp=RRC&locale=en_US&SrcAuth=RRC&SID=F3yGhhMlKL2YxQ9fAvu&customersID=RRC&mode=FullRecord&IsProductCode=Yes&Init=Yes&action=retrieve&UT=WOS%3A000469157500033

BibTex

@article{BUT163674,
  author="David {Vališ} and Jakub {Gajewski} and Libor {Žák}",
  title="Potential for using the ANN-FIS meta-model approach to assess levels of particulate contamination in oil used in mechanical systems",
  journal="Tribology International",
  year="2019",
  volume="135",
  number="1",
  pages="324--334",
  doi="10.1016/j.triboint.2019.03.012",
  issn="0301-679X",
  url="https://apps.webofknowledge.com/InboundService.do?product=WOS&Func=Frame&DestFail=http%3A%2F%2Fwww.webofknowledge.com&SrcApp=RRC&locale=en_US&SrcAuth=RRC&SID=F3yGhhMlKL2YxQ9fAvu&customersID=RRC&mode=FullRecord&IsProductCode=Yes&Init=Yes&action=retrieve&UT=WOS%3A000469157500033"
}