Automatic Identification of Gasoline – Biofuel Blend Type in an Internal Combustion Four-stroke Engine based on Unsupervised Novelty Detection and Active Learning

In this study, unsupervised novelty detection was applied to determine automatically both the type of fuel and the blending ratio of the fuel mixture based on analysis vibrational signals during operation of a four-stroke combustion engine fueled with gasoline and gasoline blends with ethanol and methanol. Three types of fuel were utilized: unleaded gasoline as base fuel compared to the mixture of two alcohols, ethanol and methanol in admixture with gasoline, as a percentage ratio of around 10%, 20% and 30%, respectively. The engine tests were performed at 1000, 1300, 1600 and 1900rpm. The collections of measurements were conducted by a triaxial accelerometer. Features in time and frequency were calculated from the signals received. The classification of the type of fuel and fuel blend ratio was achieved using an active learning method based on incremental application of One Class Principal Component Analysis (OCPCA) for novelty detection.