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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 recent years there has been an increasing worldwide effort to limit polluting emissions from road vehicles. The On Board II Diagnostic (OBD II) regulations adopted by California Air Resources Board (CARB) are among the most restrictive rules. They require on-board devices which monitor emission control systems in order to identify deterioration or malfunction of components. For automotive purpose, the high cost of achieving hardware redundancy can be reduced by substituting software redundancy. This approach requires an engine model definition. In this work the application of the Artificial Neural Networks (ANNs) technology, is analyzed and validated by experiments. First model has been tested under varying load conditions with very encouraging results.

In this paper, a further step along a research line concerning the set up of a Fault Diagnosis system for OBD-II purpose is presented. The suitability of Artificial Neural Networks for the use as engine simulation modules in the framework of a software redundancy approach has been analyzed. Experimental tests were performed, by acquiring four main engine operational parameters. Using this knowledge base, the performance of a wide variety of different Net Types was analyzed and discussed. Peculiar aspects of the possible industrial applications of this methodology are also deeply examined.