Troubleshooting using Cost Effective Algorithms and Bayesian Networks

As the heavy duty truck market becomes more competitive the importance of quick and cheap repairs increases. However, to find and repair the faulty component constitutes cumbersome and expensive work and it is not uncommon that the troubleshooting process results in unnecessary expenses. To repair the truck in a cost effective fashion a troubleshooting strategy that chooses actions according to cost minimizing conditions is desirable. This thesis proposes algorithms that uses Bayesian networks to formulate cost minimizing troubleshooting strategies. The algorithms consider the effectiveness of observing components, performing tests and repairs to decide the best current action. The algorithms are investigated using three different Bayesian networks, out of which one is a model of a real life system. The results from simulation cases illustrate the effectiveness and properties of the algorithms