Comparative Analysis of Soft Computing Models for Predicting Viscosity in Diesel Engine Lubricants: An Alternative Approach to Condition Monitoring.

The viability of employing soft computing models for predicting the viscosity of engine lubricants is assessed in this paper. The dataset comprises 555 reports on engine oil analysis, involving two oil types (15W40 and 20W50). The methodology involves the development and evaluation of six distinct models (SVM, ANFIS, GPR, MLR, MLP, and RBF) to predict viscosity based on oil analysis results, incorporating metallic and nonmetallic elements and engine working hours.