Application of feed-forward backpropagation neural network for the diagnosis of mechanical failures in engines provoked ignition

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Wilmer Rafael Contreras Urgiles
José Maldonado Ortega
Rogelio León Japa


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 intake phase 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. Statistical values of the MAP sensor signal such as, area, energy, entropy, maximum, mean, minimum, power and RMS, were selected according to the greater amount of information and significant difference. The data were obtained with the application of 3 statistical methods (ANOVA, correlation matrix and Random Forest) to create a database that allows the training of a neural network feed-forward backpropagation, with which a classification error of 1.89 e-11 was achieved. The validation of the diagnostic system was carried out by the generating supervised failures in different engines with provoked ignition.
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