Off Board diagnosis using analytical system for Diesel powered vehicles

We propose a demonstration of a new analytical method of failure diagnosis for diesel engines. This method is able to help the end user to solve problems in the vehicle using failure detection without having deep technical knowledge in automotive engineering, and it gives Product Engineering the opportunity to update DFMEA directly by field failure results detected by the system diagnosis. The ability to detect failures by this method is based on the component failures rate in the field, its mileage and symptoms of the engine failure that may be perceived as leaks, noise and smoke, among others. This method is developed using statistical calculation of probabilities through Bayesian networks, in addition to applying the knowledge of experts in the field of quality, diesel engines, artificial intelligence and vehicle diagnostics.