In this work we proposed a system based on metal oxide gas micro-sensors to estimate diesel or gasoline contamination in different engine oil samples. The gas-sensing layers (undoped, Pt, Pd, Rh-doped SnO2, In2O3 and mixed In2O3-SnO2) have been synthetized by the sol-gel method and deposited by spin-coating onto 2 mm times 2 mm silicon substrates equipped by Pt heater on the back and Pt interdigitated electrodes on the front. The sensor array has been exposed to no-used and used commercial engine oil samples contaminated with different amounts of unburned fuel. The results of data analysis (DWT-based feature extraction, PCA and Gaussian mixture model classifier (GMM)) showed that different fuel contaminated used engine oils can be discriminated and successfully classified by the sensor array.
26 Oct 2008
SENSORS, 2008 IEEE