Estimación de emisiones por fallos en motores Otto mediante redes neuronales convolucionales

This study applies a machine learning technique, specifically Convolutional Neural Networks (CNNs), to predict pollutant emissions resulting from failures in actuators and components of Otto engines. The work addresses the current lack of non-intrusive methods that exploit signals already available in the vehicle to estimate, with high accuracy, emissions associated with failures in the injection, ignition, and air intake systems. Concentrations of CO (% carbon monoxide), CO2 (% carbon dioxide), HC (unburned hydrocarbons, ppm), and O2 (% oxygen) are quantified by analyzing the Manifold Absolute Pressure (MAP) sensor signal under a rigorous sampling and signal-processing protocol. Optimal features are extracted from the MAP signal based on their informational relevance and discriminative capacity. These features are obtained through spectrographic transformation, enabling the construction of a robust database. The resulting dataset serves as an effective input for CNN training, achieving emission prediction errors below 1%.