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When a truck or bus suffers from a breakdown it is important that the vehicle comes back on the road as soon as possible. In this paper we present a prototype diagnostic decision support system capable of automatically identifying possible causes of a failure and propose recommended actions on how to get the vehicle back on the road as cost efficiently as possible. This troubleshooting system is novel in the way it integrates the remote diagnosis with the workshop diagnosis when providing recommendations. To achieve this integration, a novel planning algorithm has been developed that enables the troubleshooting system to guide the different users (driver, help-desk operator, and mechanic) through the entire troubleshooting process. In this paper we formulate the problem of integrated remote and workshop troubleshooting and present a working prototype that has been implemented to demonstrate all parts of the troubleshooting system.

Many model based solutions to diagnosis problems in SI-engines have been discussed in literature. However most presented methods are useful only for a specific class of faults. Here a systematic and more general method is presented. With this method, which is based on a structure of hypothesis tests, it is possible to diagnose a large variety of different types of faults. The method is applied to the diagnosis of sensor-faults and leakage in the air-intake system of an SI-engine. The features of the method are demonstrated by using experiments on a real SI-engine. The experiments show that the method is capable to diagnose both leakage and different types of sensor faults. Both detection and isolation are considered. It is for example possible to distinguish between a manifold leak and a manifold pressure sensor fault.