Investigation of Engine Lubrication Oil Quality Using a Support Vector Machine and Electronic Nose

Monitoring the quality of engine oil improves engine efficiency and reduces engine maintenance costs. Several methods have been proposed for this purpose; however, most of them take too long to test oil quality. This paper introduces a fast, simple, and accurate method to determine oil quality using an electronic nose and artificial intelligence. The TU5 engine and 10-40W “Behran Super Pishtaz” engine oil were used in the experiments. Tests were conducted at six different quality levels. Oil properties such as viscosity, density, flash point, and freezing point were measured at each level. Additionally, oil smell signals were recorded using an olfactory machine at these quality levels. The fraction method was employed to adjust the sensors’ responses. Five statistical features were extracted from each signal, and these features were used to train and test a support vector machine (SVM) for classifying oil quality using the five-fold cross-validation method. The results indicated a statistically significant change in viscosity and density with variations in oil quality. The density increased as the quality decreased. Viscosity, however, initially decreased and then increased at later stages. An analysis of the sensory outputs revealed that changes in oil quality also affected these outputs, with the most pronounced sensitivity observed in the MQ135 and MQ138 sensors. The final accuracies of the SVM in classifying oil quality were 68.22%, 85.86%, and 95.44% for linear, radial basis function (RBF), and polynomial kernels, respectively. The SVM sensitivities for oil qualities A, B, C, D, E, and F were 97.99%, 97.37%, 95.51%, 92.67%, 94.48%, and 94.59%, respectively.