Detecting Fuel Adulteration in Otto-Cycle Vehicles Using On-Board Diagnostics Data and Machine Learning–Based Anomaly Detection

Fuel adulteration increases operating costs, reduces vehicle efficiency, and contributes to air pollution. Fuel-quality verification typically requires laboratory infrastructure or dedicated sensors that are not available to most end users. In our previous work, we demonstrated that a software-only approach based on standard on-board diagnostics (OBD) signals and supervised machine learning can detect water adulteration in hydrated ethanol under real-world driving conditions with high accuracy. However, supervised classifiers remain challenging because fuel-trim dynamics vary across vehicles due to powertrain calibration, component aging, and operating environments, which can limit model generalization. This work addresses this limitation by introducing a detection framework that learns a vehicle-specific baseline of normal operation using gradient boosting regression (GBR) and flags anomalous events through residual-based cumulative sum (CUSUM) monitoring. The baseline model is trained using compliant-fuel data, and the CUSUM decision threshold is calibrated from temporally ordered normal-operation data using an expanding-window procedure with an initial warm-up segment. Across two vehicles, the proposed framework achieved recall values of 99.77% and 84.38%, with F1 scores of 99.88% and 91.53%, respectively, while maintaining zero false positives under compliant-fuel operation.