Diagnóstico de Fallos del Sistema de Inyección de un Motor de Encendido Provocado Mediante Inteligencia Artificial

The present article establishes a method for the diagnosis of failures of a motor of ignition provoked by the analysis of the signals of the sensors MAP, TPS and VSS using tools of learning and classification. At present, different artificial intelligence methods, used for the detection of failures in internal combustion engines, are known by reading the signals emitted by the various sensors that establish the engine’s operating parameters. Through the learning and classification tools a system is generated that obeys the instructions given by the operator, in this way a system based on the symptoms shows the type and effect of the fault present in the engine. Based on this, an experimental design is established for the diagnosis of faults through the analysis of the signals of the sensors, in order to determine and detect the faults present in the internal combustion engine more accurately andto minimize the index of gases such as CO, HC, CO2 and O2, thus reducing environmental pollution and thus generating time savings in fault detection and correction, optimizing working times and economic losses.