RESEARCH PAPER
Predicting motor oil condition using artificial neural networks and principal component analysis
1
CISE, Univ. Beira Interior, Covilhã, 6201-001, Portugal
2
Industrial Eng. and Management, Univ. Lusófona, Campo Grande 376, 1749-024, Lisboa, Portugal
3
Polytechnic Institute of Coimbra – ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal
4
CEMMPRE, Coimbra University, DEM, Polo 2, 3030-290 Coimbra, Portugal
5
ISR, Coimbra University, DEEC, Polo 2, 3030-290 Coimbra, Portugal
Publication date: 2020-09-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(3):440-448
KEYWORDS
ABSTRACT
The safety and performance of engines such as Diesel, gas or even wind turbines depends on the quality and condition of the
lubricant oil. Assessment of engine oil condition is done based on more than twenty variables that have, individually, variations
that depend on the engines’ behaviour, type and other factors. The present paper describes a model to automatically classify the
oil condition, using Artificial Neural Networks and Principal Component Analysis. The study was done using data obtained from
two passenger bus companies in a country of Southern Europe. The results show the importance of each variable monitored for
determining the ideal time to change oil. In many cases, it may be possible to enlarge intervals between maintenance interventions,
while in other cases the oil passed the ideal change point.
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