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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.

Troubleshooting trees are traditionally used to guide technicians through the process of identifying the cause of vehicle problems and solving them. These static trees can successfully visualize complex information. However, for modular vehicles, the trees become difficult to create and maintain due to the numerous different configurations of vehicles that can be constructed. These issues can be overcome by using a model-based approach. This paper describes a prototype tool for guided troubleshooting and shows its application to a selective catalytic reduction system used in many heavy vehicles. The troubleshooting tool guides the technician through the troubleshooting process by presenting the most likely fault candidates and recommending the most useful actions to perform. The list of candidates and recommendations are updated continuously to reflect the outcomes of past actions.