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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.

Maintenance planning of trucks at Scania have previously been done using static cyclic plans with fixed sets of maintenance tasks, determined by mileage, calendar time, and some data driven physical models. Flexible maintenance have improved the maintenance program with the addition of general data driven expert rules and the ability to move sub-sets of maintenance tasks between maintenance occasions. Meanwhile, successful modelling with machine learning on big data, automatic planning using constraint programming, and route optimization are hinting on the ability to achieve even higher fleet utilization by further improvements of the flexible maintenance. The maintenance program have therefore been partitioned into its smallest parts and formulated as individual constraint rules. The overall goal is to maximize the utilization of a fleet, i.e. maximize the ability to perform transport assignments, with respect to maintenance.

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.