Keyword: «machine learning methods»

This work examines the use of machine learning methods for the classification and anomaly detection of Motul motor oil using infrared spectra. The study analyzes the relevance of automating the quality control process for lubricants. Principal component analysis, support vector machines, and ensemble models were applied. The results demonstrate high accuracy in the identification of oil compositions and effective detection of anomalous samples. The scientific novelty of this work is in the integration of advanced machine learning techniques for spectral data processing, which contributes to the development of automated systems for motor oil quality control