Modeling and classifying the in-operando effects of wear and metal contaminations of lubricating oil on diesel engine: A machine learning approach

The lubricating oil analysis may be used to verify an assessment of the engine's health and operational conditions, as well as the need for oil changes. The wide sight of oil characteristics allows for the detection of elemental contaminations, additives, and wear for maintenance monitoring. The goal of this work is to investigate the in-operando effects of oil metal pollution on diesel engine conditions using large datasets and Support vector machine (SVM) and Radial basis function (RBF) models. To do this, 1948 datasets from engine lubricant spectral analysis were used. The greatest accuracy for forecasting engine conditions was obtained using cubic Polynomial (poly 3) and rbf kernel function in SVM; and RBF-NN classifiers used two training methods, trainbr, and trainlm. For engine condition predictions, RBF and SVM classifiers had an average accuracy of 99 percent and 97 percent, respectively. The capacity of models to generalize was tested by varying the dataset size (10% to 90%), with RBF demonstrating the greatest performance. Following that, RBF-NN used the confusion matrix approach to diagnose the engine's critical state with a 99.38 percent accuracy. The presence of metal contaminations such as Cr, Si, and Fe has a significant impact on the accuracy of engine identification in normal, critical, and numerous classes, according to the RBF-NN sensitivity study. Finally, RBF-NN has been chosen to define and monitor diesel engine conditions since it outperforms the SVM model.

Automated summary

This study investigates the effects of oil metal pollution on diesel engine conditions using lubricating oil analysis and machine learning models. The results show that the RBF-NN model outperforms the SVM model in predicting and diagnosing engine conditions.