OBD Engine Fault Detection Using a Neural Approach

The present work is the continuation of the research activity developed by the same authors in last years about the use of recent technologies (Artificial Neural Networks) for the set up of “software redundancy” modules to be implemented On Board for the use in Diagnostic Systems.

In the present work, a system based on Artificial Neural Networks models for automotive engines Fault Diagnosis and Isolation purposes is set-up and analysed. Four sensors/actuators (throttle valve, rotational speed, torque and intake manifold pressure) are considered, and the respective acquired data are used to train and test four ANN modules correlating the different quantities.

An FDI scheme is presented which generates fault codes sequences by suitably treating the primary residuals, obtained by comparing experimental data with the calculated ones by the ANN modules. The robust fault isolation capabilities of the proposed FDI system are presented and discussed. The proposed model architecture appears as suitable to solve either OBD problems or quality controls at factory engine production lines.