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Abstract



Investigation of a troubleshooting procedure by assessing fault tracing algorithms


The thesis delves into the area of troubleshooting procedures, an interesting area for industry. Many products in industry tend to be complex, which in turn makes troubleshooting procedures trickier. A fast and efficient repair process is often desired, since customers want the product to be repaired as fast as possible. The purpose of a troubleshooting procedure is to find a fault in a broken product, and to choose proper repair actions in a workshop. Such a procedure can be simplified by diagnosis tools, for example software programs that make fault conclusions based on fault codes. These tools can make such conclusions with the help of algorithms, i.e. fault tracing algorithms. Before a product release, it is hard to specify all faults and connections in the sys- tem. New unknown fault cases are likely to arise after release, and somehow this need to be taken into account in the troubleshooting scenario. The troubleshoot- ing procedure can be made more robust, if new data could be easily incorporated in the current structure. This work seek to answer how new data can be incorpo- rated in trouble shooting procedures. A good and reliable fault tracing algorithm is essential in the process of finding faults and repair actions, which is the reason behind the focus of this thesis. The presented problem asks how a fault can be identified from fault codes and symp- toms, in order to recommend suitable repair actions. Therefore, the problem is divided into two parts, finding the fault and recommending repair actions. In the first part, three candidate algorithms for finding the faults are investigated, namely Bayesian networks, neural networks, and a method called matrix correla- tion inspired from latent semantic indexing. The investigation is done by training each algorithm with data, and evaluating the results. The second part consists of one method proposal for repair action recommendations and one example. The theoretical investigation is based on the Servo unit steering (SUS), which reside in the IPS system of Volvo Penta. The primary contribution of the thesis is the evaluation of three different al- gorithms and a proposal of one strategy to recommend suitable repair actions. In this study Bayesian networks are found to conform well with the desired at- tributes, which in turn lead to the conclusion that Bayesian networks is well suited for this problem.

Lukas Lorentzon

2014

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