Predicting condition based on oil analysis – A case study

Hugo Raposo; José Torres Farinha; Inácio Fonseca; Diego Galar

doi:10.1016/j.triboint.2019.01.041

Predicting condition based on oil analysis – A case study

Hugo Raposo^*
, José Torres Farinha
, Inácio Fonseca
, Diego Galar

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

37 Citations (Scopus)

Abstract

The paper presents and discusses a model for condition monitoring. Using data from the oil in the Diesel engines of a fleet of urban buses, it studies the evolution of degradation and develops a predictive maintenance policy for oil replacement. Based on the analysis of the oil condition, the intervals of oil replacement can be expanded, allowing increased availability. The paper links time series forecasting with the statistical behavior of some oil effluents, like soot. This exercise can be expanded to include other variables, and the model has the potential to be applied to other physical assets to achieve the best availability based on a condition monitoring policy.

Original language	English
Pages (from-to)	65-74
Number of pages	10
Journal	Tribology International
Volume	135
DOIs	https://doi.org/10.1016/j.triboint.2019.01.041
Publication status	Published - Jul 2019
Externally published	Yes

Keywords

Condition monitoring
Diesel engines
Oil analysis
Predictive maintenance
Time series
t-Student

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.triboint.2019.01.041

Cite this

@article{49aeab9213024192a5f86995a721852d,

title = "Predicting condition based on oil analysis – A case study",

abstract = "The paper presents and discusses a model for condition monitoring. Using data from the oil in the Diesel engines of a fleet of urban buses, it studies the evolution of degradation and develops a predictive maintenance policy for oil replacement. Based on the analysis of the oil condition, the intervals of oil replacement can be expanded, allowing increased availability. The paper links time series forecasting with the statistical behavior of some oil effluents, like soot. This exercise can be expanded to include other variables, and the model has the potential to be applied to other physical assets to achieve the best availability based on a condition monitoring policy.",

keywords = "Condition monitoring, Diesel engines, Oil analysis, Predictive maintenance, Time series, t-Student",

author = "Hugo Raposo and Farinha, \{Jos{\'e} Torres\} and In{\'a}cio Fonseca and Diego Galar",

note = "Publisher Copyright: {\textcopyright} 2019 Elsevier Ltd",

year = "2019",

month = jul,

doi = "10.1016/j.triboint.2019.01.041",

language = "English",

volume = "135",

pages = "65--74",

journal = "Tribology International",

issn = "0301-679X",

publisher = "Elsevier Ltd.",

}

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T1 - Predicting condition based on oil analysis – A case study

AU - Raposo, Hugo

AU - Farinha, José Torres

AU - Fonseca, Inácio

AU - Galar, Diego

PY - 2019/7

Y1 - 2019/7

N2 - The paper presents and discusses a model for condition monitoring. Using data from the oil in the Diesel engines of a fleet of urban buses, it studies the evolution of degradation and develops a predictive maintenance policy for oil replacement. Based on the analysis of the oil condition, the intervals of oil replacement can be expanded, allowing increased availability. The paper links time series forecasting with the statistical behavior of some oil effluents, like soot. This exercise can be expanded to include other variables, and the model has the potential to be applied to other physical assets to achieve the best availability based on a condition monitoring policy.

AB - The paper presents and discusses a model for condition monitoring. Using data from the oil in the Diesel engines of a fleet of urban buses, it studies the evolution of degradation and develops a predictive maintenance policy for oil replacement. Based on the analysis of the oil condition, the intervals of oil replacement can be expanded, allowing increased availability. The paper links time series forecasting with the statistical behavior of some oil effluents, like soot. This exercise can be expanded to include other variables, and the model has the potential to be applied to other physical assets to achieve the best availability based on a condition monitoring policy.

KW - Condition monitoring

KW - Diesel engines

KW - Oil analysis

KW - Predictive maintenance

KW - Time series

KW - t-Student

UR - https://www.scopus.com/pages/publications/85062372547

U2 - 10.1016/j.triboint.2019.01.041

DO - 10.1016/j.triboint.2019.01.041

M3 - Article

AN - SCOPUS:85062372547

SN - 0301-679X

VL - 135

SP - 65

EP - 74

JO - Tribology International

JF - Tribology International

ER -

Predicting condition based on oil analysis – A case study

Abstract

Keywords

UN SDGs

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