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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.

In this paper the evaluation of the suitability of the Artificial Neural Networks for setting up simulation modules for “analytical redundancy” was further carried out. The performance of the ANN modules was enhanced, by taking into account the engine dynamics for the simulation of fast engine transients and obtaining satisfactory results. Working toward actual on board application in Fault Diagnosis systems, some ANN modules were implemented in an on-line system which acquires signals from an engine mounted on a test bench and compares in real time the experimental values with the estimated ones. In this way, it was possible to perform long duration tests of ANN's behaviour, substantially confirming the results of the conventional off-line analysis.