Machine Learning-Based Classification of Oils Using Dielectric Properties and Microwave Resonant Sensing
This work addresses the need for automated, non-destructive oil identification in industrial settings, though it is incremental as it applies existing machine learning methods to a new sensor-based dataset.
The paper tackles the problem of classifying oil types by using a machine learning approach with a microwave resonant sensor to measure dielectric properties, achieving a classification accuracy of 99.41% with a random forest classifier.
This paper proposes a machine learning-based methodology for the classification of various oil samples based on their dielectric properties, utilizing a microwave resonant sensor. The dielectric behaviour of oils, governed by their molecular composition, induces distinct shifts in the sensor's resonant frequency and amplitude response. These variations are systematically captured and processed to extract salient features, which serve as inputs for multiple machine learning classifiers. The microwave resonant sensor operates in a non-destructive, low-power manner, making it particularly well-suited for real-time industrial applications. A comprehensive dataset is developed by varying the permittivity of oil samples and acquiring the corresponding sensor responses. Several classifiers are trained and evaluated using the extracted resonant features to assess their capability in distinguishing between oil types. Experimental results demonstrate that the proposed approach achieves a high classification accuracy of 99.41% with the random forest classifier, highlighting its strong potential for automated oil identification. The system's compact form factor, efficiency, and high performance underscore its viability for fast and reliable oil characterization in industrial environments.