Aplicación de una red neuronal feed-forward backpropagation para el diagnóstico de fallas mecánicas en motores de encendido provocado

This research explains the methodology for the creation of a diagnostic system applied to the detection of mechanical failures in vehicles with gasoline engines through artificial neural networks, the system is based on the study of the phase of Admission of the Otto cycle, which is recorded through the physical implementation of a MAP sensor (Manifold Absolute Pressure). A strict sampling protocol and its corresponding statistical analysis are applied. The statistical values of the MAP sensor signal as: area, energy, entropy, maximum, mean, minimum, power and RMS, were selected according to the greater input of information and significant difference. The data were obtained with the application of 3 statistical methods (ANOVA, correlation matrix and Random Forest) to obtain a database that allows the training of a neural network feed-forward backpropagation, with which you get an error of Classification of 1.89e−11. The validation of the diagnostic system was carried out by the provoking of failures supervised in different ignition engines provoked.