Oils quality and performance analysis of vehicle's engines using radial basis neural networks

Abstract

Purpose

The purpose of this paper is to improve the application of neural networks on vehicle engine systems for fault detecting and analysing engine oils.

Design/methodology/approach

Three types of neural networks are employed to find exact neural network predictor of vehicle engine oil performance and quality. Nevertheless, two oil types are analysed for predicting performance in the engine. These oils are used and unused oils. In experimental work, two accelerometers are located at the bottom of the car engine to measure related vibrations for analysing oil quality of both cases.

Findings

The results of both computer simulation and experimental work show that the radial basis neural network predictor gives good performance at adapting different cases.

Research limitations/implications

The results of the proposed neural network analyser follow the desired results of the vehicle engine's vibration variation. However, this kind of neural network scheme can be used to analyse oil quality of the car in experimental applications.

Practical implications

As theoretical and practical studies are evaluated together, it is hoped that oil analysers and interested researchers will obtain significant results in this application area.

Originality/value

This paper is an original contribution on vehicle oil quality analysis using a proposed artificial neural network and it should be helpful for industrial applications of vehicle oil quality analysis and fault detection.

Citation

Kalkat, M., Yıldırım, Ş. and Erkaya, S. (2009), "Oils quality and performance analysis of vehicle's engines using radial basis neural networks", Industrial Lubrication and Tribology, Vol. 61 No. 6, pp. 301-310. https://doi.org/10.1108/00368790910988417