Identification of Drivability Failures Using Adaline in an ECU

Abstract

Abnormalities in fuel injection systems affect the drivability of vehicles, blemishing the driver's maneuvering experience or the smoothness of the response of these vehicles under different operating conditions. With the technological advances achieved by the automobile industry, objective methods for detecting anomalies in vehicle drivability have been studied over the last few years, with emphasis on methodologies using time-frequency analysis with wavelet transform. When performing an extraction of characteristics through wavelet decomposition aiming to detect abrupt transient variations, it becomes possible to improve the drivability of a vehicle identifying the occurrence of failures by using the energy of the decomposed signal. Therefore, using the concepts of continuous wavelet transform and entropy of information, this work makes a time-frequency analysis of the rotation signal of an internal combustion engine. The samples collected from the motor are standardized, the continuous wavelet transform is calculated and, finally, the entropy of the transformed signal is measured. Thus, the possibility of implementing an Adaline model capable of detecting the presence, or not, of abrupt changes in these signals is verified, and later, it can be embedded in an electronic control unit (ECU). The results show that the use of the Log Energy entropy as an input of the Adaline model is promising, granting 100% of accuracy on the dataset studied.