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@article{206559,
        author = {Ayush Sahu and Ritesh Kumar Dewangan},
        title = {Literature Review on Engine Oil Degradation and Viscosity Prediction Using Engine Oil Dataset by Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {2295-2300},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206559},
        abstract = {Engine oil degradation and viscosity changes significantly affect engine performance, efficiency, and reliability. Traditional oil condition monitoring methods are often costly and time-consuming. Machine learning techniques provide an effective alternative by predicting oil degradation and viscosity using historical engine oil data. This literature review examines recent studies that apply algorithms such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR) for oil condition monitoring. The review highlights the effectiveness of these techniques in predictive maintenance and identifies the need for integrated models that can simultaneously predict engine oil degradation and viscosity. Such approaches can improve maintenance planning, reduce operational costs, and enhance engine life.},
        keywords = {Engine Oil Degradation, Viscosity Prediction, Machine Learning, Predictive Maintenance, Engine Oil Dataset.},
        month = {July},
        }